Development Of Computer Vision System Research Articles

The State of Computer Vision Research in Africa

Despite significant efforts to democratize artificial intelligence (AI), computer vision which is a sub-field of AI, still lags in Africa. A significant factor to this, is the limited access to computing resources, datasets, and collaborations. As a result, Africa’s contribution to top-tier publications in this field has only been 0.06% over the past decade. Towards improving the computer vision field and making it more accessible and inclusive, this study analyzes 63,000 Scopus-indexed computer vision publications from Africa. We utilize large language models to automatically parse their abstracts, to identify and categorize topics and datasets. This resulted in listing more than 100 African datasets. Our objective is to provide a comprehensive taxonomy of dataset categories to facilitate better understanding and utilization of these resources. We also analyze collaboration trends of researchers within and outside the continent. Additionally, we conduct a large-scale questionnaire among African computer vision researchers to identify the structural barriers they believe require urgent attention. In conclusion, our study offers a comprehensive overview of the current state of computer vision research in Africa, to empower marginalized communities to participate in the design and development of computer vision systems.

Tongue Disease Prediction Based on Machine Learning Algorithms

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

Window-based transformer generative adversarial network for autonomous underwater image enhancement

The estimation of high-quality underwater images is an important step towards the development of computer vision systems in marine environments. This fundamental step contains numerous computer vision and robotics applications including marine exploration, robotics manipulation, navigation, object detection, tracking, and sea life monitoring. However, this pre-processing step becomes more challenging in the presence of back-scattering of underwater particles and attenuation issues, which lead to the formation of hazy underwater images. Vision transformers have recently demonstrated outstanding performance in many computer vision applications. Window-based Transformers (WT) show promising enhancement performance by computing self-attention within non-overlapping local windows. WT has been identified as an essential component in improving representation capabilities; however, it has received less attention in improving the performance of Underwater Image Enhancement (UIE). Therefore, we propose a novel end-to-end Underwater window-based Transformer Generative Adversarial Network (UwTGAN). Our proposed algorithm consists of two main components, including a transformer generator that generates a restored underwater image and a transformer discriminator that classifies the generated underwater image. Both components are equipped with Window-based Self-Attention Blocks (WSABs), which maximize efficiency by limiting self-attention computation to non-overlapping local windows and provide relatively low computational costs. WSAB-based transformer generators and discriminators are trained end-to-end. We formulated an efficient loss function to ensure that the variables are closely integrated. Extensive experimental evaluations are performed on four independent underwater image datasets. Our results demonstrate that the proposed UwTGAN algorithm has outperformed several state-of-the-art UIE methods in terms of both quantitative and qualitative metrics by a significant margin.

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. These results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

Plataforma para Procesamiento de Imágenes sobre SoC FPGA de Xilinx

La aplicación de sistemas de procesamiento de imágenes en edge computing resulta cada vez más atractiva y necesaria. Sin embargo, las exigencias en cuanto a consumo de potencia y alto rendimiento impiden que puedan utilizarse plataformas de procesamiento estándares. En este aspecto, los FPGA son una buena opción para el desarrollo de sistemas de visión computacional a causa de su capacidad de explotación del paralelismo. Por otra parte, el flujo de diseño de las herramientas de síntesis de FPGA actuales admiten lenguajes de alta abstracción como descripciones de entrada, en contraposición a los lenguajes de descripción de hardware. La síntesis de alto nivel (HLS) automatiza el proceso de diseño al transformar la descripción algorítmica en hardware digital mientras se satisfacen las limitaciones del diseño. Sin embargo, a los expertos en procesamiento de imágenes puede resultarles compleja la integración hardware obtenida con el resto de los componentes del sistema, como por ejemplo interfaces de captura y visualización. En este trabajo, se presenta un diseño base para la construcción de aplicaciones de procesamiento de imágenes basada en Zynq. Se proporciona además una metodología que posibilita el desarrollo eficiente de soluciones de procesamiento de imágenes embebidas de manera ágil.

Adaptable Architecture for the Development of Computer Vision Systems in FPGA

Computer vision systems are increasingly used in industry for inspection or process control. The more demanding requirements observed today make the implementation of this type of systems a technological challenge. Many of the computational architectures available allow us to meet the main functional requirements related to the use case and also the non-functional ones, such as processing time restrictions and connectivity. However, the requirement for adaptability so that such systems can be easily modified to meet different use cases or even accommodate environmental changes remains a challenge. This work proposes a flexible architecture for computer vision systems using FPGA. This architecture combines components of the processing flow of the vision system implemented in hardware and software to obtain advantages associated with both approaches. The proposed solution is validated against different real use cases existing in the industry. The results obtained allow us to affirm that such architecture brings an interesting advantages since it meets the operational requirements present in industrial applications, demands less development effort and can be easily adapted to new usage scenarios.

A hardware-software architecture for computer vision systems

Computer vision systems are based on capture and processing of images, from which information for the application is extracted. This paper presents a hybrid platform which can be easily configured and efficiently used for a large number of applications in computer vision. It consists of a dedicated processor integrated to the capture element, connected to a general-purpose computer for the high-level application processing. The camera module consists of a DSP executing a middleware which provides all the basic functions, such as image capture, low-level image processing, control, and communication. Experiments showed that good performance is obtained with this platform, and the framework presented may simplify the development of computer vision systems. An application for the hardware-software architecture presented here would be the validation and prototyping of computer vision systems.

Development of computer vision system to predict peroxidase and polyphenol oxidase enzymes to evaluate the process of banana peel browning using genetic programming modeling

Abstract The process of enzymatic browning is one of the most important chemical reactions, which effects on color, appearance and quality of fruits and vegetables. Polyphenol oxidase (PPO) and peroxidase (POD) enzymes are associated with enzymatic browning in the tissue of agricultural products. Quality of banana as a climacteric fruit is reduced by enzymatic browning during storage. Therefore, to evaluate enzymatic browning in banana, first, images of the fruits were taken at 25 °C for 9 days. Then, these images were investigated using digital image processing in order to predict and study POD and PPO enzymes during the browning process of banana peel. To this end, seventeen color parameters ( R ¯ , G ¯ , B ¯ , VR, VG; VB, r, g, b, C1, C2, C3, C4, C5, C6, C7, C8) were extracted from each image as non-destructive parameters. In the following, PPO and POD both were measured through the laboratory methods Finally, using genetic programming (GP) modeling, two equations were obtained which can be used to predict and detect the changes of the activity of the PPO and POD enzymes during the storage period (9 days). The correlation coefficients between the measured values and the predicted values for PPO and POD enzymes were 0.98 and 0.97, respectively. Furthermore, there were no significant differences between predicted values with measured values of PPO and POD enzymes (p > 0.05); these results indicate the proper performance of the designed models.

Development of Computer Vision System to Extract Kansei Information from Facial Expressions

Kansei engineering is one field of engineering where the feelings of humans are applied to manufacturing. If Kansei can be extracted, the design, development, and evaluation of various things are possible. This study aims to build an automatic design and evaluation system for agricultural products and landscapes and then utilize the system for the evaluation and improvement of educational effects. In this presentation, we built a low price and near real-time system to extract Kansei information from the facial expressions of users and obtained knowledge about Kansei. Additionally, we created an algorithm to discriminate Kansei. We built a system to obtain facial expression information of a user from a camera image as our experimental equipment. The characteristic values calculated from them were the variation (or the normalized polygon area change ratio) of the four items; the inner ends of the eyebrows, upper parts of the eyes, lower parts of the eyes, and the corners of the mouth. The target Kansei was three items: positive Kansei (liking, fun, and happy) and negative Kansei (unpleasantness and hatred). The results are as follows:First, for the target three Kansei, changes could be captured through the four specified normalized polygon area change rates. As for the positive Kansei, the changes in the corners of the mouth were greater than those of the others in particular. As for the negative Kansei, the changes in the lower parts of the eyes were slightly greater than those of the others and, as for the Kansei of surprise, the changes in the corners of the mouth were slightly greater. The direction of the change showed a tendency where the positive Kansei increased at the corners of the mouth in particular. The negative Kansei showed a strong tendency to decrease at the inner ends of the eyebrows and the lower parts of the eyes in particular. The Kansei of surprise showed a tendency where the normalized polygon area change rates increased as a whole. Next, we selected one subject because the relationship between the facial expressions and Kansei varies among individuals, and then we built an algorithm to discriminate the three Kansei features. As for the Kansei of positive – negative and negative – surprise, it became possible to discriminate at a rate of nearly 100% from the information of the corners of the mouth. It became possible to discriminate the positive-surprise at a rate of about 80% from the information of the upper parts of the eyes. As described above, although it is necessary to prepare the template image of the seven points of the target person and adjust the parameters after measuring the Kansei facial expression in advance, a low price and near real-time computer vision system to extract facial expression Kansei could be built.

LabelMe: Online Image Annotation and Applications

Central to the development of computer vision systems is the collection and use of annotated images spanning our visual world. Annotations may include information about the identity, spatial extent, and viewpoint of the objects present in a depicted scene. Such a database is useful for the training and evaluation of computer vision systems. Motivated by the availability of images on the Internet, we introduced a web-based annotation tool that allows online users to label objects and their spatial extent in images. To date, we have collected over 400 000 annotations that span a variety of different scene and object classes. In this paper, we show the contents of the database, its growth over time, and statistics of its usage. In addition, we explore and survey applications of the database in the areas of computer vision and computer graphics. Particularly, we show how to extract the real-world 3-D coordinates of images in a variety of scenes using only the user-provided object annotations. The output 3-D information is comparable to the quality produced by a laser range scanner. We also characterize the space of the images in the database by analyzing 1) statistics of the co-occurrence of large objects in the images and 2) the spatial layout of the labeled images.

Active, Foveated, Uncalibrated Stereovision

Biological vision systems have inspired and will continue to inspire the development of computer vision systems. One biological tendency that has never been exploited is the symbiotic relationship between foveation and uncalibrated active, binocular vision systems. The primary goal of any binocular vision system is the correspondence of the two retinal images. For calibrated binocular rigs the search for corresponding points can be restricted to epipolar lines. In an uncalibrated system the precise geometry is unknown. However, the set of possible geometries can be restricted to some reasonable range; and consequently, the search for matching points can be confined to regions delineated by the union of all possible epipolar lines over all possible geometries. We call these regions epipolar spaces. The accuracy and complexity of any correspondence algorithm is directly proportional to the size of these epipolar spaces. Consequently, the introduction of a spatially variant foveation strategy that reduces the average area per epipolar space is highly desirable. This paper provides a set of sampling theorems that offer a path for designing foveation strategies that are optimal with respect to average epipolar area.