How can Artificial Intelligence help solve environmental problems like Air Pollution

Air pollution

Air pollution is caused by solid and liquid particles and certain gases that are suspended in the air. These particles and gases can come from car and truck exhaust, factories, dust, pollen, mold spores, volcanoes and wildfires. The solid and liquid particles suspended in our air are called aerosols.

Certain gases in the atmosphere can cause air pollution. For example, in cities, a gas called ozone is a major cause of air pollution. Ozone is also a greenhouse gas that can be both good and bad for our environment. It all depends where it is in Earth’s atmosphere.

Ozone high up in our atmosphere is a good thing. It helps block harmful energy from the Sun, called radiation. But, when ozone is closer to the ground, it can be really bad for our health. Ground level ozone is created when sunlight reacts with certain chemicals that come from sources of burning fossil fuels, such as factories or car exhaust.

When particles in the air combine with ozone, they create smog. Smog is a type of air pollution that looks like smoky fog and makes it difficult to see. (https://climatekids.nasa.gov/air-pollution/)

The major outdoor pollution sources include vehicles, power generation, building heating systems, agriculture/waste incineration and industry. In addition, more than 3 billion people worldwide rely on polluting technologies and fuels (including biomass, coal and kerosene) for household cooking, heating and lighting, releasing smoke into the home and leaching pollutants outdoors.

Air quality is closely linked to earth’s climate and ecosystems globally. Many of the drivers of air pollution (i.e. combustion of fossil fuels) are also sources of high CO2 emissions. Some air pollutants such as ozone and black carbon are short-lived climate pollutants that greatly contribute to climate change and affect agricultural productivity. Policies to reduce air pollution, therefore, offer a “win-win” strategy for both climate and health, lowering the burden of disease attributable to air pollution, as well as contributing to the near- and long-term mitigation of climate change.

Air pollution can be significantly reduced by expanding access to clean household fuels and technologies, as well as prioritizing: rapid urban transit, walking and cycling networks; energy-efficient buildings and urban design; improved waste management; and electricity production from renewable power sources. (https://www.who.int/airpollution/ambient/about/en/)

How does air pollution affect our health?

Breathing in polluted air can be very bad for our health. Long-term exposure to air pollution has been associated with diseases of the heart and lungs, cancers and other health problems. That’s why it’s important for us to monitor air pollution.


Source : World Health Organization

AI might be used to improve urban sustainability and quality of life. It is about time that Artificial Intelligence is used for something important for the whole planet. That is why we will talk about AI solutions that address the problem of air pollution.

Air pollution – AI solutions

Artificial Intelligence for cleaner air in Smart Cities

In Singapore, where air pollution and related health costs are particularly high, a team of researchers investigated the possibility to combine the power of sensor technologies, Internet of things (IoT) and AI to get reliable and valid environmental data and feed better, greener policy-making. As reported by The Business Times, through the computation of real-time IoT sensor data measuring spatial and temporal pollutants, user-friendly air quality heat maps and executive dashboards can be created, and the most severe pollution hotspots can be determined with the help of machine learning algorithms for predictive modelling. This is the first step to take proactive actions towards further decarbonizing the economy, including incentives for virtuous businesses, the development of wiser land use plans, the revitalization of urban precincts, and more. (https://www.pdxeng.ch/2019/03/28/artificial-intelligence-for-cleaner-air-in-smart-cities/)

An Artificial Intelligence-Based Environment Quality Analysis System

The paper describes an environment quality analysis system based on a combination of some artificial intelligence techniques, artificial neural networks and rule-based expert systems. Two case studies of the system use are discussed: air pollution analysis and flood forecasting with their impact on the environment and on the population health. The system can be used by an environmental decision support system in order to manage various environmental critical situations (such as floods and environmental pollution), and to inform the population about the state of the environment quality. (An Artificial Intelligence-Based Environment Quality Analysis System – https://link.springer.com/chapter/10.1007/978-3-642-23957-1_55)

AI non-profit to track air pollution from every power plant in the world and make data public

A nonprofit artificial intelligence firm called WattTime is going to use satellite imagery to precisely track the air pollution (including carbon emissions) coming out of every single power plant in the world, in real time. And it’s going to make the data public. This system promises to effectively eliminate poor monitoring and gaming of emissions data.

The plan is to use data from satellites that make theirs publicly available, as well as data from a few private companies that charge for their data. The images will be processed by various algorithms to detect signs of emissions. Google.org, Google’s philanthropic wing, is getting the project off the ground…with a $1.7 million grant. WattTime made a splash earlier this year with Automated Emissions Reduction. AER is a program that uses real-time grid data and machine learning to determine exactly when the grid is producing the cleanest electricity.

Author: David Roberts, Vox, Published on: 8 May 2019

“We’ll soon know the exact air pollution from every power plant in the world. That’s huge.”, 7 May 2019. (https://www.business-humanrights.org/en/ai-non-profit-to-track-air-pollution-from-every-power-plant-in-the-world-and-make-data-public)

A fresher breeze: How AI can help improve air quality

As part of our AI for Earth commitment, Microsoft supports five projects from Germany in the areas of environmental protection, biodiversity and sustainability. In the next few weeks, we will introduce the project teams and their innovative ideas that made the leap into our global programme and group of AI for Earth grantees.

AI for Earth

The AI ​​for Earth program helps researchers and organizations to use artificial intelligence to develop new approaches to protect water, agriculture, biodiversity and the climate. Over the next five years, Microsoft will invest $ 50 million in “AI for Earth.” To become part of the “AI for Earth” program, developers, researchers and organizations can apply with their idea for a so-called “Grant”. If you manage to convince the jury of Microsoft representatives, you´ll receive financial and technological support and also benefit from knowledge transfer and contacts within the global AI for Earth network. As part of Microsoft Berlin´s EarthLab and beyond, five ideas have been convincing and will be part of our “AI for Earth” program in the future in order to further promote their environmental innovations.  (https://news.microsoft.com/europe/2019/08/20/a-fresher-breeze-how-ai-can-help-improve-air-quality/)

Artificial Intelligence For Air Quality Control Systems: A Holistic Approach

Abstract

Recent environmental regulations introduced by the United States environmental protection agency such as the Mercury Air Toxics Standards and Hazardous Air Pollution Standards have challenged environmental particulate control equipment especially the electro-static precipitators to operate beyond their design specifications. The impact is exacerbated in power plants burning a wide range of low and high-ranking fossil fuels relying on co-benefits from upstream processes such as the selective catalytic reactor and boilers. To alleviate and mitigate the challenge, this manuscript presents the utilization of modern and novel algorithms in machine learning and artificial intelligence for improving the efficiency and performance of electrostatic precipitators reflecting a holistic approach by considering upstream processes as model parameters. In addition, the paper discusses input relevance algorithms for neural networks and random forests such as partial derivatives, input perturbation and GINI importance comparing their performance and applicability for our case study. Our approach comprises of applying random forests and neural network algorithms to an electrostatic precipitator extending the model to include upstream process parameters such as the selective catalytic reactor and the air heaters. To study variable importance differences and model generalization performance between our employed algorithms, we developed a statistical approach to compare features data distributions impact on input relevance.

Read more here:

 (https://ieeexplore.ieee.org/document/8635295)

Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of Delhi, India

Highlights

•Neural network and fuzzy logic are combined for forecasting of PM2.5 during haze conditions.

•The haze occurs when the level of PM2.5 is more than 50 μg/m3 and relative humidity is less than 90%.

•Neuro-fuzzy model is capable for better forecasting of haze episodes over urbanized area than ANN and MLR models.

Abstract

Delhi has been listed as the worst performer across the world with respect to the presence of alarmingly high level of haze episodes, exposing the residents here to a host of diseases including respiratory disease, chronic obstructive pulmonary disorder and lung cancer. This study aimed to analyze the haze episodes in a year and to develop the forecasting methodologies for it. The air pollutants, e.g., CO, O3, NO2, SO2, PM2.5 as well as meteorological parameters (pressure, temperature, wind speed, wind direction index, relative humidity, visibility, dew point temperature, etc.) have been used in the present study to analyze the haze episodes in Delhi urban area. The nature of these episodes, their possible causes, and their major features are discussed in terms of fine particulate matter (PM2.5) and relative humidity. The correlation matrix shows that temperature, pressure, wind speed, O3, and dew point temperature are the dominating variables for PM2.5 concentrations in Delhi. The hour-by-hour analysis of past data pattern at different monitoring stations suggest that the haze hours were occurred approximately 48% of the total observed hours in the year, 2012 over Delhi urban area. The haze hour forecasting models in terms of PM2.5 concentrations (more than 50 μg/m3) and relative humidity (less than 90%) have been developed through artificial intelligence based Neuro-Fuzzy (NF) techniques and compared with the other modeling techniques e.g., multiple linear regression (MLR), and artificial neural network (ANN). The haze hour’s data for nine months, i.e. from January to September have been chosen for training and remaining three months, i.e., October to December in the year 2012 are chosen for validation of the developed models. The forecasted results are compared with the observed values with different statistical measures, e.g., correlation coefficients (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA). The performed analysis has indicated that R has values 0.25 for MLR, 0.53 for ANN, and NF: 0.72, between the observed and predicted PM2.5 concentrations during haze hours invalidation period. The results show that the artificial intelligence implementations have a more reasonable agreement with the observed values. Finally, it can be concluded that the most convincing advantage of artificial intelligence based NF model is capable for better forecasting of haze episodes in Delhi urban area than ANN and MLR models.

Read more here:

(https://www.sciencedirect.com/science/article/abs/pii/S1352231014009157)

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

Abstract

In recent years, the application of titanium dioxide (TiO2) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO2 on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO2 solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

Read more here:

 (https://link.springer.com/article/10.1007/s11356-014-2821-z)

Neuro Fuzzy Modeling Scheme for the Prediction of Air Pollution

Abstract

The techniques of artificial intelligence based in fuzzy logic and neural networks are frequently applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Hybrid of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The structure of the model is based on three-layered neural fuzzy architecture with back propagation learning algorithm. The main objective of this paper is two folds. The first objective is to develop Fuzzy controller, scheme for the prediction of the changing for the NO2 or SO2, over urban zones based on the measurement of NO2 or SO2 over defined industrial sources. The second objective is to develop a neural net, NN; scheme for the prediction of O3 based on NO2 and SO2 measurements.

Read more here:

 (https://pdfs.semanticscholar.org/1fee/92567748cc2fd4530557bd8d8ebf6395d4e5.pdf)

Sensing the Air We Breathe — The OpenSense Zurich Dataset

Abstract

Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem, introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.

Read more here:

 (https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/4896/5158)

This article is good for getting started and gives a dataset to work with!

Development of artificial intelligence based NO2 forecasting models at Taj Mahal, Agra

Abstract

The statistical regression and specific computational intelligence based models are presented in this paper for the forecasting of hourly NO2 concentrations at a historical monument Taj Mahal, Agra. The model was developed for the purpose of public health oriented air quality forecasting. Last ten–year air pollution data analysis reveals that the concentration of air pollutants increased significantly. It is also observed that the pollution levels are always higher during the months of November at around Taj Mahal, Agra. Therefore, the hourly observed data during November were used in the development of air quality forecasting models for Agra, India. Firstly, multiple linear regression (MLR) was used for building an air quality–forecasting model to forecast the NO2 concentrations at Agra. Further, a novel approach, based on regression models, principal component analysis (PCA) was analyzed to find the correlations of different predictor variables between meteorology and air pollutants. Then, the significant variables were taken as the input parameters to propose the reliable physical artificial neural network (ANN)-multi layer perceptron model for forecasting of air pollution in Agra. MLR and PCA–ANN models were evaluated through statistical analysis. The correlation coefficients (R) were 0.89 and 0.91 respectively, for PCA–ANN and were 0.69 and 0.89 respectively for MLR in the training and validation periods. Similarly, the values of normalized mean square error (NMSE), index of agreement (IOA) and fractional bias (FB) were in good agreement with the observed values. It was concluded that PCA–ANN model performs better and can be used for forecasting air pollution at Taj Mahal, Agra.

Read more here:

 (https://reader.elsevier.com/reader/sd/pii/S1309104215302567?token=9C6D5D566E2D1892B932A804377D82A742BDCA1C793AFEF1C357C03B4004FC916FE4196F6EDEC2F9CA093DB4E1B54E8C)

A Novel Air Quality Early-Warning System Based on Artificial Intelligence

Abstract

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

Read more here:

 (https://www.mdpi.com/1660-4601/16/19/3505)

How AI and IoT could help people combat air pollution issues

It is with little surprise that the UN’s 2019 World Environment Day Is a call to action to #beatairpollution. IT, as a sector, influences air quality in terms of the energy used to drive our electronics, data centers and, indeed, through business travel. With a large-scale industry presence in Asia, home to some of the most polluted cities in the world, we need to do what we can to minimize these impacts.

But technology can also be part of the solution. Last year, Capgemini announced a new global ambition to leverage technology to help organizations with their sustainability challenges, recognizing that this is the biggest impact we can make. Technology can be an enabler to help address prevention at source, helping organizations optimize their operations and reduce their impact. But with 4.2 million deaths every year as a result of exposure to ambient outdoor air pollution, how can we also leverage technology to monitor, inform, and ultimately change the behaviors of those most affected as they head into our many cities?

The advances in technology give us the opportunity to reach people directly and build a more sophisticated monitoring and communication network. We could leverage both artificial intelligence (AI) and the internet of things (IOT) with the capabilities from an increasing range of personal devices whether it be the 2.5 billion smart phones or the estimated 278 million smart watches in the world.[3] Indeed, the wearable health and fitness technology sector is set to grow 10–20% in the next five years, with an expanding set of capabilities. These devices measure elements such as heart rate, blood pressure, and breathing rate, which are indicators of overall health and are also measurables that change with exposure to air pollutants such as PM, nitrogen oxide and sulfur oxides. Yet they also monitor spatial and GPS data, which if combined could demonstrate the impact of the external environment on health factors, and better inform people of the issues. Data from different sources and AI technology could allow us to drill down on very local issues.

If we overlay current air quality monitoring data sources onto an individual, it would allow us to give a very precise prediction of local air quality issues. We could then integrate AI, to both refine and include a wider range of factors such as weather conditions and traffic levels. Added to this, if automatic number plate recognition (ANPR) is integrated, we could discern the proportion of vehicle fuel types being used in specific locations. This is important because diesel vehicles emit 90% of particulate matter.

Data analytics over time would allow people to understand impacts on their health – and change behavior.

Over time, as an individual’s health and diagnostics data are inputted into a data analytics model alongside their own spatial data and air pollution exposure data, they could receive an analysis of how air pollution is impacting their physiology. Based on this, they could receive tailored suggested actions to take as well. The ability to overlay a Google Map of your walk to school or work to the air quality data around you could, instead of highlighting traffic congestion, show air quality issues and provide the options to re-route to avoid, or offer alternative options for time to start a journey.

Read more here:

(https://www.capgemini.com/2019/06/beatairpollution-how-ai-and-iot-could-help-people-combat-air-pollution-issues/)

So, this time we listed some novel AI solutions for solving the environmental air pollution problem. Next time we talk about this topic, expect the idea how we are going to include Smart Imaging and AI in Smart city solution for cleaner air. Do you have any suggestions?

Artificial Intelligence is getting better – latest news and trends in AI concerning image processing

Artificial intelligence is now a part of new, more useful applications and it is getting better. In this blog post we will present you some of these new and interesting AI apps. And, let us just inform you that, from this blog post, every couple of months, we will show and discuss news and trends in image processing field, including new papers, research and applications!

And now, let’s start with news from our favorite, NVIDIA. What is NVIDIA up to?

AI can Detect Open Parking Spaces

With as many as 2 billion parking spaces in the United States, finding an open spot in a major city can be complicated. To help city planners and drivers more efficiently manage and find open spaces, MIT researchers developed a deep learning-based system that can automatically detect open spots from a video feed.

Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots,” the researchers stated in their paper.

Article from:
https://news.developer.nvidia.com/ai-algorithm-aims-to-help-you-find-a-parking-spot/

New AI Imaging Technique Reconstructs Photos with Realistic Results

Researchers from NVIDIA, led by Guilin Liu, introduced a state-of-the-art deep learning method that can edit images or reconstruct a corrupted image, one that has holes or is missing pixels. The method can also be used to edit images by removing content and filling in the resulting holes. The method, which performs a process called “image inpainting”, could be implemented in photo editing software to remove unwanted content, while filling it with a realistic computer-generated alternative.

Our model can robustly handle holes of any shape, size location, or distance from the image borders. Previous deep learning approaches have focused on rectangular regions located around the center of the image, and often rely on expensive post-processing,” the NVIDIA researchers stated in their research paper.

Article from:
https://news.developer.nvidia.com/new-ai-imaging-technique-reconstructs-photos-with-realistic-results/

AI Can Now Fix Your Grainy Photos by Only Looking at Grainy Photos

What if you could take your photos that were originally taken in low light and automatically remove the noise and artifacts? Have grainy or pixelated images in your photo library and want to fix them? This deep learning-based approach has learned to fix photos by simply looking at examples of corrupted photos only. The work was developed by researchers from NVIDIA, Aalto University, and MIT, and was presented at the International Conference on Machine Learning in Stockholm, Sweden.

Recent deep learning work in the field has focused on training a neural network to restore images by showing example pairs of noisy and clean images. The AI then learns how to make up the difference. This method differs because it only requires two input images with the noise or grain.

Without ever being shown what a noise-free image looks like, this AI can remove artifacts, noise, grain, and automatically enhance your photos.

It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars,” the researchers stated in their paper.

Article from:
https://news.developer.nvidia.com/ai-can-now-fix-your-grainy-photos-by-only-looking-at-grainy-photos/

AI Model Can Generate Images from Natural Language Descriptions

To potentially improve natural language queries, including the retrieval of images from speech, Researchers from IBM and the University of Virginia developed a deep learning model that can generate objects and their attributes from natural language descriptions.

We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations, including cartoon-like scenes, object layouts corresponding to real images, and synthetic images,” the researchers stated in their paper.

Article from:
https://news.developer.nvidia.com/ai-model-can-generate-images-from-natural-language-descriptions/

Now, some new research papers with different fields that need AI as well as image processing:

Digital image analysis in breast pathology—from image processing techniques to artificial intelligence 

From: https://www.sciencedirect.com/science/article/pii/S1931524417302955 

Abstract: Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in artificial intelligence (AI) promise to fundamentally change the way we detect and treat breast cancer in the near future. Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis.

Predicting tool life in turning operations using neural networks and image processing

From: https://www.sciencedirect.com/science/article/pii/S088832701730599X 

Abstract: A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

Automatic food detection in egocentric images using artificial intelligence technology 

From:
https://www.cambridge.org/core/journals/public-health-nutrition/article/automatic-food-detection-in-egocentric-images-using-artificial-intelligence-technology/CAE3262B945CC45E4B14C06C83A68F42  

Abstract:

Objective:To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.

Design:To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network.

Results:A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both ‘food’ and ‘drink’ were considered as food images. Alternatively, if only ‘food’ items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively.

Conclusions: The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

Bioinformatics and Image Processing—Detection of Plant Diseases 

From:
https://link.springer.com/chapter/10.1007/978-981-13-1580-0_14 

Abstract:

This paper gives an idea of how a combination of image processing along with bioinformatics detects deadly diseases in plants and agricultural crops. These kinds of diseases are not recognizable by bare human eyesight. First occurrence of these diseases is microscopic in nature. If plants are affected with such kind of diseases, there is deterioration in the quality of production of the plants. We need to correctly identify the symptoms, treat the diseases, and improve the production quality. Computers can help to make correct decision as well as can support industrialization of the detection work. We present in this paper a technique for image segmentation using HSI algorithm to classify various categories of diseases. This technique can also classify different types of plant diseases as well. GA has always proven itself to be very useful in image segmentation.

And, at the end, some news from public sector and applied algorithms:

China Now has Facial Recognition Based Toilets 

China has integrated facial recognition in the toilets across the country. Citizens now need WeChat or face scans to get the toilet papers. People will stand in the yellow recognition spot and will bring their face near the face identification machine.  Then after about three seconds, 90 centimeters of toilet paper will come out. People will then go in and use the toilet but only for limited time as alarm will buzz if someone occupies it for too long. In toilet, sensors will assess ammonium amount and spray a deodorant if required. The two bathrooms integrated with face scanners for being “clean and convenient,” and “reducing toilet paper waste.”

Read more here:
https://www.aitechnologies.com/china-now-has-facial-recognition-based-toilets/ 

Apple’s Camera-Toting Watch Band Uses Facial Recognition For Flawless FaceTime Calls 

U.S. Patent and Trademark Office granted a patent to Apple which says that the tech titan wants to widen the set of attributes of its wearable, by integrating an original camera system with the ability to automatically crop subject matter, trace objects such as user’s face and produce angle-adjusted avatars for FaceTime calls. “Image-capturing watch” U.S. Patent No. 10,129,503 of Apple tells a software and hardware solution that creates a camera-toting Apple Watch, that is both handy and feasible. Using a camera-toted Watch, consumers can put aside a heavy handheld device while playing sports, exercising or doing other energetic activities. However, a feasible smartwatch solution is hard to accomplish. The camera captures the motion data and then the watch processes it, after which it is mapped onto the computer produced picture, which imitates a consumer’s facial movements and expressions in real time. On the other hand, source movement data can be utilized to tell about the motion of inhuman avatars such as Apple’s Memoji and Animoji. It still remains unknown whether Apple wants to integrate its Apple Watch camera band tech.

Read more here:
https://www.aitechnologies.com/apples-camera-toting-watch-band-uses-facial-recognition-for-flawless-facetime-calls/

Metropolitan Police London is to Integrate Face Recognition Tech 

London’s police will integrate face recognition tech as an experiment for two days. In the areas of Leicester Square, Piccadilly Circus, and Soho in London, the technology will examine crowds’ faces and compare them with the database of individuals wanted by the courts and Metropolitan Police in London. If the tech founds a match, the police officers in that field will analyze it and perform further tests to make sure the identity of that individual.

Read more here:
https://www.aitechnologies.com/metropolitan-police-london-is-to-integrate-face-recognition-tech/

That’s all for now folks. But, tell me, what do you think, what are some areas where AI is going to bring most benefits? What are areas, by your opinion where there is space for more research? Can you actually believe that it is possible to have AI solutions in every day life?

All news are citations from the mentioned sites, where you can find the whole text about the topic.

Outliers in data and how to deal with them

Term explanation

In image processing, as an area in signal processing, modelling the data and expected values is very important in all kinds of applications. So, the data represent the problem that needs to be addressed. That is why it is necessary for us to know what kind of data to expect, and what are some values that are the result of some measurement errors, faulty data, erroneous procedures, or simply what are the areas where a certain theory might not be valid. So, to improve the model and gain better results of our applications, we must recognize and deal with outliers in the data.

What is an outlier?

In statistics, an outlier is a data point that differs significantly from other observations. Outliers in the data can be very dangerous, since they change the classical data statistics, such as mean value and variance of the data. This affects the results of an algorithm of any kind (image processing, machine learning, deep learning algorithm…). So, when modeling, it is extremely important to clean the data sample to ensure that the observations best represent the problem.

How to deal with outliers in the data

            The thing we know about outliers is that they do not fit the model we assumed, but we don’t know anything else about them, when they will appear or what value will have. We just know that we must stop them messing with our results. But how?

  • First step in determining the outliers is getting to know the data for the specific application. So, we must have some test dataset and start from there.
  • The next step is to find the data distribution (according to the available dataset), which can be tricky task sometimes. Let us assume that the data have normal (Gaussian) distribution.
Normal (Gaussian) distribution
  • When we are familiar with the distribution of the data, now we can identify outliers more easily. So, there is no precise way to define and identify outliers in general, but we must know how to define them for our specific application.
  • We can now use statistical methods to identify observations that appear to be rare or unlikely given the available data. Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution.
  • In the former case one wishes to discard them or use statistics that are robust to outliers, while in the latter case they indicate that the distribution has high skewness and that one should be very cautious in using tools or intuitions that assume a normal distribution.
  • In most larger samplings of data, some data points will be further away from the sample mean than what is deemed reasonable. This can be due to incidental systematic error or flaws in the theory that generated an assumed family of probability distributions, or it may be that some observations are far from the center of the data. In large samples, a small number of outliers is to be expected (and not due to any anomalous condition).
  • Now, we can deal with outliers. We can remove them from our dataset if we are dealing with the offline applications. But, on the other hand, if we are dealing with the real time online processing   than we must use some procedures, in order to make our application more robust.

Remark:

Maybe one thinks that a simple way to handle outliers is to detect them and remove them from the data set. Deleting an outlier, although better than doing nothing, still poses a number of problems:

  • When is deletion justified? Deletion requires a subjective decision.
  • When is an observation “outlying enough” to be deleted?
  • The user or the author of the data may think that “an observation is an observation” (i.e., observations should speak for themselves) and hence feel uneasy about deleting them.
  • Since there is generally some uncertainty as to whether an observation is really atypical, there is a risk of deleting “good” observations, which results in underestimating data variability.

Since the results depend on the user’s subjective decisions, it is difficult to determine the statistical behavior of the complete procedure.

Robust Statistics

Let’s say something about normal distribution assumption. It is very common to assume the Gaussian distribution in different kinds of an engineering problems. The most widely used model formalization is the assumption that the observed data have a normal (Gaussian) distribution. This assumption has been present in statistics as well as engineering for two centuries and has been the framework for all the classical methods in regression, analysis of variance and multivariate analysis. The main justification for assuming a normal distribution is that it gives an approximate representation to many real data sets, and at the same time is theoretically quite convenient because it allows one to derive explicit formulas for optimal statistical methods such as maximum likelihood, likelihood ratio tests, etc. We refer to such methods as classical statistical methods and note that they rely on the assumption that normality holds exactly. The classical statistics are by modern computing standards quite easy to compute. Unfortunately, theoretical and computational convenience does not always deliver an adequate tool for the practice of statistics and data analysis. It often happens in practice that an assumed normal distribution model (e.g., Standard Kalman filter) holds approximately in that it describes the majority of observations, but some observations follow a different pattern or no pattern at all.

Now, we know that such atypical data are called outliers, and even a single outlier can have a large distorting influence on a classical statistical method that is optimal under the assumption of normality or linearity. The kind of “approximately” normal distribution that gives rise to outliers is one that has a normal shape in the central region but has tails that are heavier or “fatter” than those of a normal distribution. One might naively expect that if such approximate normality holds, then the results of using a normal distribution theory would also hold approximately. This is unfortunately not the case.

The robust approach to statistical modeling and data analysis aims at deriving methods that produce reliable parameter estimates and associated tests and confidence intervals, not only when the data follow a given distribution exactly, but also when this happens only approximately in the sense just described.

Robust methods fit the bulk of the data well: if the data contain no outliers the robust method gives approximately the same results as the classical method, while if a small proportion of outliers are present the robust method gives approximately the same results as the classical method applied to the “typical” data. As a consequence of fitting the bulk of the data well, robust methods provide a very reliable method of detecting outliers, even in high-dimensional multivariate situations.

We note that one approach to dealing with outliers is the diagnostic approach. Diagnostics are statistics generally based on classical estimates that aim at giving numerical or graphical clues for the detection of data departures from the assumed model. There is a considerable literature on outlier diagnostics, and a good outlier diagnostic is clearly better than doing nothing. However, these methods present two drawbacks. One is that they are in general not as reliable for detecting outliers as examining departures from a robust fit to the data. The other is that, once suspicious observations have been flagged, the actions to be taken with them remain the analyst’s personal decision, and thus there is no objective way to establish the properties of the result of the overall procedure.

Robust methods have a long history that can be traced back at least to the end of the nineteenth century. But the first great steps forward occurred in the 1960s, and the early 1970s with the fundamental work of John Tukey (1960, 1962), Peter Huber (1964, 1967) and Frank Hampel (1971, 1974). The applicability of the new robust methods proposed by these researchers was made possible by the increased speed and accessibility of computers.

In this post we will not talk about Robust Statistics any more. If you want to find out more, a new post will be published soon, or you can get some information from the references given at the end. This was just a beginning and a warm up for those who need more information about getting started in designing more robust applications.

What is real time processing (online VS offline)

If you are a beginner in the area of the image and video processing, you may often hear the term real time processing. In this post, we will try to explain the term and list some typical concerns related to this term.

Real time processing – circuit board

Real time image processing is related with typical frame rate. Current standard for capture is typically 30 frames per second. Real time processing would require processing all the frames as soon as they are captured. So broadly speaking, if capture rate is 30 FPS then 30 frames needs to be processed in one second. That comes to around 33 milliseconds (1000 ms / 30 frames = 33 ms/frame). Similar calculation can be done for any frame rate to get required processing time per frame.

In image and video processing, the source of our signal is a camera. So, what real time image processing really means is: produce output simultaneously with the input. What is actually meant is that the algorithm will run at the rate of the source (e.g. a camera) supplying the images, so the algorithm can process images at the frame rate of the camera.

Source of image signal is camera

Human vision:

Just out of curiosity, let’s see how the human vision works:

The first thing to understand is that we perceive different aspects of vision differently. Detecting motion is not the same as detecting light. Another thing is that different parts of the eye perform differently. The center of vision is good at different stuff than the periphery. And another thing is that there are natural, physical limits to what we can perceive. It takes time for the light that passes through your cornea to become information on which your brain can act, and our brains can only process that information at a certain speed.

Another important concept: the whole of what we perceive is greater than what any one element of our visual system can achieve. This point is fundamental to understanding our perception of vision.

The temporal sensitivity and resolution of human vision varies depending on the type and characteristics of visual stimulus, and it differs between individuals. The human visual system can process 10 to 12 images per second and perceive them individually, while higher rates are perceived as motion. Modulated light (such as a computer display) is perceived as stable by the majority of participants in studies when the rate is higher than 50 Hz through 90 Hz. This perception of modulated light as steady is known as the flicker fusion threshold. However, when the modulated light is non-uniform and contains an image, the flicker fusion threshold can be much higher, in the hundreds of hertz. Regarding image recognition, people have been found to recognize a specific image in an unbroken series of different images, each of which lasts as little as 13 milliseconds. Persistence of vision sometimes accounts for very short single-millisecond visual stimulus having a perceived duration of between 100 ms and 400 ms. Multiple stimuli that are very short are sometimes perceived as a single stimulus, such as a 10 ms green flash of light immediately followed by a 10 ms red flash of light perceived as a single yellow flash of light.

Human vision

Applications:

The real-time aspect is critical in many real-world devices or products such as mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-assisted intelligent robots, spectral imaging systems, and many other embedded image or video processing systems.

With the increasing capabilities of imaging systems like cameras with very high-density captures having 16 or more megapixels, it is extremely difficult to get real time performance for many applications.

Applications

What applications need real time performance and what applications do not:

When talking about the numerous applications of image and video processing, we can say that some applications in some systems need real time processing, and some don’t. That is why we will talk about online (real time) and offline processing.

Offline processing is processing already recorded video sequence or image. So, digital video stabilization, video enhancement, video coloring, or any application can work with already prepared video. These applications can be found in marketing, industry, medical imaging, film industry or in some ordinary commercial applications, such as a user that wants to stabilize and enhance some video from the phone library.

Offline processing enables using more complex and computationally demanding algorithms, therefore usually gives better results than real time processing. That is why offline processing tools are used a lot in academic research and in some kinds of challenges.

Some of Deep Learning tools for offline processing (on CPU) are:

On the other hand, some applications have a demand for real time processing. For example, traffic monitoring, target tracking in military applications, surveillance and monitoring, real time video games, etc. are apps that demand real time feedback and processed image from sensor.

The algorithms that work in real time do not have the luxury of high complexity, since the processing time for each frame is determined by source frame rate and resolution. New hardware solutions nowadays offer better processing speeds, but there are still limitations, depending of the specific application.

Systems with multiple complex applications working in parallel:

Sometimes the application demands multiple complex algorithms working in parallel. That is the time when not only the complexity of the algorithms is considered, but also which algorithm will be processed first and how this affects the desired performance of the application. One good example is when video enhancement and digital video stabilization algorithm work in parallel.

Video stabilization and video dehazing algorithms in the same video processing pipeline can affect the results of each other. This interesting topic is described in a paper [Dehazing Algorithms Influence on Video Stabilization Performance] given in references at the end of the post. When there is no severe haze, noise or low contrast in the scene, it is important to perform video stabilization algorithm prior to video dehazing algorithm. On the other hand, when the feature level in the scene is low, which happens because of severe haze or low contrast in image, the stabilization algorithm cannot perform well, since it cannot calculate global motion accurately. That is why, for the sake of the better stabilization performance, the proposed pipeline performs video dehazing algorithm prior to video stabilization.

Dehazing Algorithms Influence on Video Stabilization Performance

At the end, we will mention some of the possibilities for real time image processing platforms:

  • FPGA – very good for complex parallel operations, example of the application in paper [High-performance electronic image stabilization for shift and rotation correction] given in references.
  • Nvidia Jetson TX1, TX2, Xavier

Get real-time Artificial Intelligence (AI) performance where you need it most with the high-performance, low-power NVIDIA Jetson AGX systems. Processing of complex data can now be done on-board edge devices. This means you can count on fast, accurate inference in everything from robots and drones to enterprise collaboration devices and intelligent cameras. Bringing AI to the edge unlocks huge potential for devices in network-constrained environments.”  – from Nvidia site, given in references.

What can we actually do with Deep Learning in Image Processing

Computers today can do almost everything we imagine with images and videos. Deep Learning, as a powerful tool in the area of Artificial Intelligence, can be very helpful state of the art gadget in Image Processing.

Application areas are numerous: from civil and industrial applications such as mobile telephone industry, augmented reality, home automation, gaming, retail and infotainment to some serious surveillance applications. If you ever wondered what is behind some FacebookAmazon or Pinterest algorithms for image classification and searches, it is Deep Learning.

Here are some concrete ideas of what we can actually accomplish with this tool:

Different stages of image and video processing examples

Note: If you are not familiar with the theory behind Deep Learning (read our next text about Deep Learning theory and Convolutional Neural Networks (CNN))

Pre-processing stage

Pre-processing represents the stage where we prepare our images for some future applications (from preparation to post on Instagram or showing on surveillance monitors, to preparation to use them in some complex applications, such as tracking, video stabilization…)

1.Noise Reduction for Image Restoration:

The process can go like this:

  • Prepare dataset, which consists of clean and corrupted image pairs (used to train a CNN)
  • Given a noisy image, predict clean image (using CNN)
  • Learn how to map corrupted image patches to clean ones, implicitly capturing the characteristic appearance of noise in natural images

Here is an example of thermal image denoising:

Noise reduction in thermal image

2. Image dehazing:

  • The presence of haze (dust, smoke, fog, mist, rain, snow…) directly influences visibility of the scene by reducing contrast and obscuring objects.  In severe haze conditions the image can practically loose the most of visual information.
  • This problem is where powerful optics cannot provide solution, and digital image processing is a must.
  • Video dehazing based on haze imaging includes estimation of haze transmission map, which needs to be “subtracted” or removed from the hazy image.
Removing haze from image

3. Image Enhancement:

  • Improve the visibility in the night conditions with CNNs
Improving visibility in night conditions
  • Improve the visibility in low contrast conditions with CNNs
Improve contrast in image for better visibility

4. Artefact Reduction

  • An efficient neural network can be used for seamless attenuation of different compression artefacts
  • Reduce JPEG compression artefacts
  • Reduce Twitter compression artefacts

Image processing stage

This is the stage where we actually use the information from images to artificially create a content and add meaning to group of pixels.

5. Boundary Detection

  • Detect object boundaries by using CNNs
Boundary detection

6. Feature learning – context encoders

  • Context Encoders – a CNN trained to generate the contents of an arbitrary image region conditioned on its surroundings
  • Context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s).
Missing part of image retreival

7. Object Detection

Here we will explain the terms object classification, object localization, object detection and image segmentation:

  • Object detection models identify single or multiple relevant objects in a single image
  • The localization of the objects is provided, in comparison with image classification
Classification, localization, detection and segmentation explained

Object detection is VERY popular topic nowadays in scientific community, so several datasets have been released for object detection challenges:

     – PASCAL Visual Object Classification (PASCAL VOC) dataset

     – ImageNet

     – Common Objects in COntext (COCO) dataset

Researchers publish results of their algorithms applied to these challenges.

Overview of the scores on the 2007, 2010, 2012 PASCAL VOC and 2015, 2016 COCO datasets, by using different networks

8. Image Segmentation

  • The real time demo of Image Segmentation with SegNet can be seen here:

http://mi.eng.cam.ac.uk/projects/segnet/

  • SegNet is a deep encoder-decoder architecture for multi-class pixelwise segmentation researched and developed by members of the Computer Vision and Robotics Group at the University of Cambridge, UK.

9. Object recognition

Object recognition is explained with the image below:

Detection, action classification, image captioning explained
  • Convolutional Neural Networks (CNN) have become an important tool for object recognition.
  • Convolutional Neural Networks for Visual Recognition example in real time can be found here:

http://cs231n.stanford.edu/

Video processing

Video processing actually represents image processing on the set of frames (which represent the sequence).

Processing can be offline and online (http://www.smartimagingblog.com/2019/04/07/what-is-real-time-processing-online-vs-offline/).

This actually means the sequence can be processed in real time (real time processing), frame by frame (and we should monitor the processing time between frames), or the sequence can be recorded and then processed, so there is no need for taking care of processing time.

In surveillance and monitoring, it is necessary to have real time video processing, which affects the complexity of the algorithm used. On the other hand, when we need post-processing in, for example, our recorded videos, complexity of the algorithms can rise. 

10. Object Tracking

  • Object tracking has always been a challenging problem in a field of computer vision.
  • Popular challenges or contests like the Visual Object Tracking (VOT) challenge and Visual Object Tracking in Thermal Infrared VOT-TIR challenge are a proof that object tracking is an ongoing demanding problem.
  • Deep Learning methods can also be applied in the task of single or multiple object tracking.

11. Digital Video Stabilization

  • Digital video stabilization is a task of removing jitter and shaking in video, due to unwanted camera motion (because of holding camera in hand or platform where camera is mounted is shaking)
  • Digital video stabilization is a task that can be performed on surveillance or monitoring cameras, therefore in real time or we might want to remove hand-shaking from our pre-recorded videos (offline stabilization).
  • Deep Learning framework enables faster algorithms, which can be applied succesfully in real time processing.
(a) Shaky video sequence from Matlab examples
(b) Stabilized video sequence

12. Video enhancement

  • Image enhancement techniques, mentioned in pre-processing section, can also be applied on the video sequence, in the same way.
  • The only concern can be about the complexity of the algorithm, in the case of real time processing.

This would be all for the post today, but if you want to study the topic more, here are some scientific papers that talk about the mentioned algorithms.

References