Computer Vision Learning Techniques Research Articles

HUMAN POSE ESTIMATION

In "Human Pose Estimation" with integrated feedback mechanisms to assess and guide users in achieving correct poses. Utilizing advanced deep learning techniques in computer vision, the system swiftly detects key points on the human body and provides instant feedback on pose accuracy. Built on convolutional neural networks trained on extensive pose datasets, the system includes pose detection, classification, and feedback stages. By comparing detected poses with predefined correct poses, the system delivers positive feedback for accurate poses and corrective guidance for deviations. Key Words: Human Pose Estimation, Pose Detection, Pose Classification, Correct Pose Assessment, Fitness Training, Key Points Detection, Correct Pose Thresholds

A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision

In last years, computer vision has shown important advances, mainly using the application of supervised machine learning (ML) and deep learning (DL) techniques. The objective of this review is to show a brief review of the current state of the field of supervised ML and DL techniques, especially on computer vision tasks. This study focuses on the main ideas, advantages, and applications of DL in computer vision and highlights their main concepts and advantages. This study showed the strengths, limitations, and effects of computer vision supervised ML and DL techniques.

Harnessing Deep Learning in Computer Vision for Effective Face Mask Detection in the Global COVID-19 Crisis

Objective: This study aims to explore the use of deep learning algorithms in computer vision to address the effective detection of face mask use in the global COVID-19 crisis.
 Results: Deep learning techniques enable the execution of tasks by computers and smart devices without human intervention, including image identification and predictions. Its applications have demonstrated significant advances in several areas, particularly in engineering and health research. The study highlights that computer vision scientists can contribute to the prevention, control and management of the fight against COVID-19 and other airborne viruses. Computer vision employs algorithmic tools to process images, perform associations and transmit relevant information.
 Implications for Research, Practice and Social: Construction of a deep learning model using a dataset of people with and without face masks. The developed model, implemented in Python with the help of OpenCV, Keras and TensorFlow libraries, presented highly promising results, reaching an accuracy of 99% in predictions in test images.
 Originality/Value: This study highlights the originality and value of deep learning techniques in computer vision as an effective means of tackling virus-borne pandemics such as COVID-19, and contributing to a preventive, efficient and cost-effective approach to use of face masks.
 Keywords: Computer vision; Convolutional Neural Network; Face mask.

Wild Animal Detection Using CNN

Abstract: Efficient and reliable monitoring of wild animals in their natural habitats is essential to inform conservation and management decisions. Automatic covert cameras or “camera traps” are being an increasingly popular tool for wildlife monitoring due to their effectiveness and reliability in collecting data of wildlife unobtrusively, continuously and in large volume. However, processing such a large volume of images and videos captured from camera traps manually is extremely expensive, time-consuming and also monotonous. This presents a major obstacle to scientists and ecologists to monitor wildlife in an open environment. Leveraging on recent advances in deep learning techniques in computer vision, we propose in this paper a framework to build automated animal recognition in the wild, aiming at an automated wildlife monitoring system. In particular, we use a single-labeled dataset from Wildlife Spotter project, done by citizen scientists, and the state-of-the-art deep convolutional neural network architectures, to train a computational system capable of filtering animal images and identifying species automatically. Our experimental results achieved an accuracy at 96.6% for the task of detecting images containing animal, and 90.4% for identifying the three most common species among the set of images of wild animals taken in Southcentral Victoria, Australia, demonstrating the feasibility of building fully automated wildlife observation. This, in turn, can therefore speed up research findings, construct more efficient citizen science based monitoring systems and subsequent management decisions, having the potential to make significant impacts to the world of ecology and trap camera images analysis.

Deep learning detection network for peripheral blood leukocytes based on improved detection transformer

Leukocytes serve as an important barrier to healthy immunity in the body and play an important role in fighting diseases. Manual morphological examination of leukocytes is the gold standard for the diagnosis of certain diseases but is undoubtedly labour-intensive and requires a high level of expertise. Therefore, conducting research on computer-aided diagnostics is important. With the development of deep learning techniques in computer vision, an increasing number of deep learning-based methods are now being applied in the field of medical imaging. Recently, the detection transformer (DETR) model, which is based on the transformer architecture, has exhibited outstanding performances in object detection tasks and has attracted considerable attention. Our study aims to propose a pure transformer-based end-to-end object detection network based on DETR and apply it to a practical medical scenario of leukocyte detection. First, we introduce the pyramid vision transformer and deformable attention module into the DETR model to improve the model performance and convergence speed. Second, we train the improved model on the challenging Common Objects in Context dataset to obtain the pretrained weights. Third, we perform transfer learning on the modified Raabin leukocyte dataset to obtain the optimal model. The improved DETR shows a mean average precision detection performance of up to 0.961 and is therefore superior to the original DETR and convolutional neural network. The study findings are expected to be useful for the development of a transformer structural model for leukocyte detection.

Application of Three Convolutional Neural Network Algorithms for Occluded Face Identification and Recognition for System Security

Deep Learning techniques in computer vision have become indispensable elements in biometric systems, especially face recognition. Facial recognition can be reliably used as an identification and authentication tool for premises or network access security. The masks wearing, which is one of the problems of concealment, are nowadays part of our habits for preventing COVID-19 disease, which leads to an obstruction of facial recognition. Occulted face recognition is one of the most challenging problems biometrics deals with. This paper presents convolution neural network algorithms for occluded face recognition. Our study presents a robust method using algorithms such as ResNet-50, VGG-19, and DenseNet-201 to contribute to occluded face recognition. Various parameters are used for this experiment, such as the cross-entropy used as a loss function and optimization algorithms adapted to deep learning. These include the SGD, Adam, and RMSProp optimizers. The convolution neural network algorithms were evaluated on the AR database. This experiment gave results that ranged from 94.81 to 99.81% for SGD, from 0 to 96.92 for Adam, and finally from 0 to 96.92 for RMSProp. DenseNet-201 algorithm using the SGD optimizer obtained the best score with 99.81%, and all the performance metrics used such as accuracy, MSE, F-score, recall, and MCC were used to confirm this good performance.

An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model

During the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats to wild areas and forest management. Traditional firefighting methods, which rely on ground operation inspections, have major limits and may threaten firefighters’ lives. As a result, remote sensing techniques, particularly UAV-based remotely sensed techniques, are currently among the most sought-after wildfire-fighting approaches. Current improvements in drone technology have resulted in significant breakthroughs that allow drones to perform a wide range of more sophisticated jobs. Rescue operations and forest monitoring, for example, demand a large security camera, making the drone a perfect tool for executing intricate responsibilities. Meanwhile, growing movement of the deep learning techniques in computer vision offers an interesting perspective into the project’s objective. They were used to identify forest fires in their beginning stages before they become out of control. This research describes a methodology for recognizing the presence of humans in a forest setting utilizing a deep learning framework and a human object detection method. The goal of identifying human presence in forestry areas is to prevent illicit forestry operations like illegal access into forbidden areas and illegal logging. In recent years, a lot of interest in automated wildfire identification utilizes UAV-based visual information and various deep learning techniques. This study focused on detecting wildfires at the beginning stages in forest and wilderness areas, utilizing deep learning-based computer vision algorithms that control and then mitigate massive damages to human life and forest management.

The reconstitution predictive network for precipitation nowcasting

Precipitation nowcasting is an indispensable task for traffic routing and disaster avoidance. Due to its strenuous movement, even the most recent deep learning techniques in computer vision deliver unsatisfactory performance. The main reason can be attributed to the following two aspects: 1) The traditional convolution in the input has a limited field to extract spatial representation. 2) The convolution in state-to-state connection might lead to mismatch problems and inaccurate strength predictions. To address the two drawbacks, we propose an innovative algorithm the Reconstitute Spatiotemporal LSTM (RST-LSTM) based on Convolutional Recurrent Neural Network (ConvRNN). In the proposed model, we present the local and global reconstitution scheme into the current input and the hidden state respectively. The local reconstitution (LR) can adaptively adjust the perceptual field of convolution so as to extract more useful spatial information and exclude invalid representation. Moreover, in the hidden state, the global reconstitution (GR) is embedded to alleviate the problem of mismatching between the current input and hidden state. Experimental results in MovingMNIST++ show that our approach can achieve the best predictions for those data with more drastic changes in the adjacent time. For radar data in CIKM AnalytiCup 2017 (we name it RadarCIKM in this paper), our method outperforms the state-of-the-art competitors. Furthermore, we notice that GR can promote the nowcasting in the high radar echo region and LR can reduce the error.

Powering Visualization With Deep Learning

The articles in this Special Issue focus on the application of deep learning techniques in visualization. The great success of deep learning techniques in computer vision, natural language processing, and speech recognition offers new opportunities for data visualization and analytics. We can leverage these technologies not only to recognize visual representations but also to understand analytical tasks. However, introducing deep learning techniques into visualization tasks faces new challenges and problems to solve. In this issue, we present six articles that illustrate how deep learning techniques interact with visualization.

Fast mesh data augmentation via Chebyshev polynomial of spectral filtering

Deep neural networks have recently been recognized as one of the powerful learning techniques in computer vision and medical image analysis. Trained deep neural networks need to be generalizable to new data that are not seen before. In practice, there is often insufficient training data available, which can be solved via data augmentation. Nevertheless, there is a lack of augmentation methods to generate data on graphs or surfaces, even though graph convolutional neural network (graph-CNN) has been widely used in deep learning. This study proposed two unbiased augmentation methods, Laplace–Beltrami eigenfunction Data Augmentation (LB-eigDA) and Chebyshev polynomial Data Augmentation (C-pDA), to generate new data on surfaces, whose mean was the same as that of observed data. LB-eigDA augmented data via the resampling of the LB coefficients. In parallel with LB-eigDA, we introduced a fast augmentation approach, C-pDA, that employed a polynomial approximation of LB spectral filters on surfaces. We designed LB spectral bandpass filters by Chebyshev polynomial approximation and resampled signals filtered via these filters in order to generate new data on surfaces. We first validated LB-eigDA and C-pDA via simulated data and demonstrated their use for improving classification accuracy. We then employed brain images of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and extracted cortical thickness that was represented on the cortical surface to illustrate the use of the two augmentation methods. We demonstrated that augmented cortical thickness had a similar pattern to observed data. We also showed that C-pDA was faster than LB-eigDA and can improve the AD classification accuracy of graph-CNN.

Silhouette Pose Feature-Based Human Action Classification Using Capsule Network

Recent years have seen a rise in the use of various machine learning techniques in computer vision, particularly in posing feature-based human action recognition which includes convolutional neural networks (CNN) and recurrent neural network (RNN). CNN-based methods are useful in recognizing human actions for combined motions (i.e., standing up, hand shaking, walking). However, in case of uncertainty of camera motion, occlusion, and multiple people, CNN suppresses important feature information and is not efficient enough to recognize variations for human action. Besides, RNN with long short-term memory (LSTM) requires more computational power to retain memories to classify human actions. This research proposes an extended framework based on capsule network using silhouette pose features to recognize human actions. Proposed extended framework achieved high accuracy of 95.64% which is higher than previous research methodology. Extensive experimental validation of the proposed extended framework reveals efficiency which is expected to contribute significantly in action recognition research.

Image-Based Animal Detection and Breed Identification Using Neural Networks

Having accurate, detailed, and up-to-date information about the behaviour of animals in the wild world would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data through various sources, which could help catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, animal behaviour into “big data” sciences and many more. So extracting information from the pictures remains an expensive, time-consuming, and manual task for us. We demonstrate that such information can be automatically extracted by deep learning and convolutional neural network. Leveraging on recent advances in deep learning techniques in computer vision, we propose in this project a framework to build automated animal recognition in the wild, aiming at an automated wildlife monitoring system. In particular, we use a single-labelled dataset done by citizen scientists, and the state-of-the-art deep convolutional neural network architectures, face biometrics, to train a computational system capable of filtering animal images and identifying species automatically and counting the number of species. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild and this, in turn, can, therefore, speed up research findings, construct more efficient citizen science-based monitoring systems and subsequent management decisions, having the potential to make significant impacts to the world of ecology and trap camera images analysis .

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

AbstractCasting concrete at different ages for new construction and repairing or retrofitting concrete structures requires a sufficient bond between concrete casts. The bond strength between different casts is attributed to surface roughness. Surface roughness can be achieved in many ways, such as water‐jetting or sandblasting. To evaluate the degree of surface roughness, qualitative and quantitative methods are introduced by many researchers; however, several drawbacks are associated with most of these methods, including cost, availability, human errors, and inability to assess old structures from prior inspection records. Two novel industrial implementation methods are introduced in this paper to estimate, quantitatively, the concrete surface roughness from images with sufficient resolution. In the first application method, a digital image processing method is proposed to distinguish the coarse aggregate from cement paste, and a new index is presented as a function of aggregate proportional area to the surface area. In the second application method, data augmentation and transfer learning techniques in computer vision and machine learning are utilized to classify new images based on predefined images during the learning process. Both application methods were related to a well‐established method of 3D laser scanning from sandblasted concrete surfaces. Finally, a brand new set of images of sandblasted surfaces was used to test and validate both methods. The results show that both methods successfully estimate the concrete surface roughness with an accuracy of more than 93%.

Advanced Agro Field & Crop Surveillance Systems

Agriculture becoming the major driver for Indian economy, applying some of the latest technological digital innovations to solve critical Agri-based challenges are becoming vital to improve the productivity and lower the cost of operations. Primary productivity index of agriculture is directly dependent on how much the crops escaped from attacks either by pests or by external intruders. Applying some of the advanced machine learning techniques in Computer Vision and multiple object detection algorithms in the field of Agriculture surveillance generates huge interest among farmer communities. In this paper, an aapproach which includes deployment of sensors to monitor the whole cultivation area, fixing appropriate cameras and detecting motions in the agro field, is proposed for Agro field surveillance. An orchestrated deployment of necessary sensing devices such as motion-sensing, capturing video based on demand and passes it on to the deep learning algorithms for further synthesis. The model is developed and trained leveraging technologies such as tensorflow, keras with google Colab, Jupyter notebook environment that runs entirely in the google cloud that requires very minimal setup. To evaluate the model, the authors create a test set which contains 200 captured events, more than 60,000 images that are relevant for this scope and available in public to train Deep Learning CNN based models.

Unsupervised Learning for Depth, Ego-Motion, and Optical Flow Estimation Using Coupled Consistency Conditions

Herein, we propose an unsupervised learning architecture under coupled consistency conditions to estimate the depth, ego-motion, and optical flow. Previously invented learning techniques in computer vision adopted a large amount of the ground truth dataset for network training. A ground truth dataset, including depth and optical flow collected from the real world, requires tremendous effort in pre-processing due to the exposure to noise artifacts. In this paper, we propose a framework that trains networks while using a different type of data with combined losses that are derived from a coupled consistency structure. The core concept is composed of two parts. First, we compare the optical flows, which are estimated from both the depth plus ego-motion and flow estimation network. Subsequently, to prevent the effects of the artifacts of the occluded regions in the estimated optical flow, we compute flow local consistency along the forward–backward directions. Second, synthesis consistency enables the exploration of the geometric correlation between the spatial and temporal domains in a stereo video. We perform extensive experiments on the depth, ego-motion, and optical flow estimation on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset. We verify that the flow local consistency loss improves the optical flow accuracy in terms of the occluded regions. Furthermore, we also show that the view-synthesis-based photometric loss enhances the depth and ego-motion accuracy via scene projection. The experimental results exhibit the competitive performance of the estimated depth and the optical flow; moreover, the induced ego-motion is comparable to that obtained from other unsupervised methods.

Special Issue:Deep Learning in Computer Graphics

The five papers in this special section focus on the use of deep learning for computer graphics. The great success of deep learning techniques in computer vision, speech recognition, and natural language processing has recently attracted much attention. It is expected that the use of deep learning techniques can significantly advance the performance of many state-of-theart graphics and visualization algorithms.