Application Of Computer Vision System Research Articles

Flotation Froth Image Recognition Using Vision Transformers

The application of computer vision systems on industrial flotation plants has benefited considerably from advances in deep learning over the last decade, mostly based on the use of convolutional neural networks and transfer learning. More recently, vision transformers (ViTs) have attracted strong interest since their first appearance in 2017, compared to the popular convolutional neural networks (CNNs). Although becoming well-established in many areas, they have not yet been considered meaningfully in machine vision or signal processing applications in mineral processing, despite the obvious benefits that their application could realize. In this paper, it is demonstrated that ViTs are neural network architectures highly capable of discriminating between different froth flotation images. A customized ViT model and a pretrained ViT model using transfer learning were studied and compared. The former achieved satisfactory performance and the latter achieved near perfect performance, both at a significantly lower computational cost than CNNs. These results suggest that ViTs can be a competitive alternative to CNNs in the advancement of computer vision systems on industrial flotation plants.

Prospects for the Application and Development of Computer vision Technologies

The paper presents examples from different fields of human activity in which computer vision technology is used. It is analyzed what is machine vision, how it differs from computer vision. The advantages and disadvantages of the technology are touched upon, an overview of the system components and methods for processing machine vision is presented, and the areas in which it is applied are explored. The study shows how relevant and in demand computer vision is, as well as the ways of its progress in the near future. The issues of application of machine vision systems in monitoring the implementation of safety protocols, in quality management are touched upon. Here, machine vision is implemented as an automated visual control and quality control of production processes. The application of computer vision systems in transport for the classification of vehicles, their counting, traffic analysis, detection of traffic violations: speeding, running a red light, not allowing a pedestrian is considered. This improves road safety. Computer vision is used to detect distracted driving: drowsiness, daydreaming, distraction; up to the determination of the sobriety of the driver. The use of computer vision to monitor order in large cities is affected by determining the deviant behavior of citizens.

Parallel Vision for Long-Tail Regularization: Initial Results From IVFC Autonomous Driving Testing

Long-tail effect, characterized by highly frequent occurrence of normal scenarios and the scarce appearance of extreme “long-tail” scenarios, ubiquitously exists in the vision-related problems in the real-world applications. Though many computer vision methods to date have already achieved feasible performance for most of the normal scenarios, it is still challenging for existing vision systems to accurately perceive the long-tail scenarios. This deficiency largely hinders the practical application of computer vision systems, since long-tail problems may incur fatal consequences, such as traffic accidents, taking the vision systems of autonomous vehicles as an example. In this paper, we firstly propose a theoretical framework named Long-tail Regularization (LoTR), for analyzing and tackling the long-tail problems in the vision perception of autonomous driving. LoTR is able to regularize the scarcely occurred long-tail scenarios to be frequently encountered. Then we present a Parallel Vision Actualization System (PVAS), which consists of closed-loop optimization and virtual-real interaction, to search for challenging long-tail scenarios and produce large-scale long-tail driving scenarios for autonomous vehicles. In addition, we introduce how to perform PVAS in Intelligent Vehicle Future Challenge of China (IVFC), the most durable autonomous driving competition around the world. Results over the past decade demonstrate that PVAS can effectively guide the collection of long-tail data to diminish the cost in the real world, and thus promote the capability of vision systems to adapt to complex environments, alleviating the impact of long-tail effect.

Application of Computer Vision Systems for Monitoring the Condition of Drivers Based on Facial Image Analysis

Application of Computer Vision Systems for Monitoring the Condition of Drivers Based on Facial Image Analysis

Vision based inspection system for leather surface defect detection using fast convergence particle swarm optimization ensemble classifier approach

Surface defect inspection plays a vital role in leather manufacturing. Current practice involves an expert to inspect each piece of leather individually and detect defects manually. However, such a manual inspection is highly subjective and varies quite considerably from one assorter to another. Computer vision system for natural material like leather is a challenging research problem. This study describes the application of computer vision system to capture leather surface images and use of a novel Fast Convergence Particle Swarm Optimization (FCPSO) algorithm on a set of handcrafted texture features viz., GLCM and classified using supervised classifiers viz., Multi Layer Perceptron (MLP), Decision Tree (DT), SVM, Naive Bayes, K-Nearest Neighbors (KNN) and Random Forest (RF). FCPSO using modified fitness function by selective band Shannon entropy is implemented to segment industrial leather images. Segmentation efficiency of the proposed FCPSO algorithm is evaluated and its performance is compared with other optimization algorithms. Efficiency of the segmentation algorithms is evaluated using performance measures such as average difference (AD), Area Error Rate (AER), Edge-based structural similarity index (ESSIM), F-Score, Normalized correlation coefficient (NK), Overlap Error (OE), structural content (SC), Structural similarity index (SSIM) and Zijdenbos similarity index (ZSI). Correlation of the segmented area using FCPSO with the experts’ ground truth is found to be high with R value of 0.84. Feature extraction is carried out using GLCM texture features and the most prominent features were selected using statistical t-test and correlation coefficient. Experimental results showed encouraging results for random forest classifier confirming the potential of the proposed system for automatic leather defect classification.

Development and an Application of Computer Vision System for Nondestructive Physical Characterization of Mangoes

This research reports the comparison study of manual (digital vernier caliper, DVC) method and application of computer vision system (CVS) for evaluation of size attributes (length, width and thickness) of two mango cultivars (Chausa and Dashehari). The color RGB (red, green and blue) camera was used in CVS for capturing of required profile image of fruit. The profile image was then processed and developed algorithms for image analysis and extraction of fruit’s quality attributes. Various morphological features such as area, perimeter, Max Feret diameter (MFD), hydraulic radius, Waddel disk diameter (WDD), elongation factor, compactness factor, Heywood circulatory factor and type factor of both cultivars were also evaluated using developed algorithms and attempted to describe the morphology of the outline and shape of the biological object. Area, perimeter, Max Feret diameter and Waddel disk diameter, further, correlated with the weight and volume of fruit. Various multilinear regression (MLR) models were developed for determining weight and volume of mango fruit of both cultivars using dimensional characteristics (length, width and thickness) of mango fruit evaluated by CVS. The major diameter (single variable) and all three (major, intermediate and minor) diameter (multiple variables)-based models were identified as the best models. Similarly, CVS has yielded above 97.9%, 93.5% and 92.5% accuracy in estimating mango fruits major, intermediate and minor diameters of both cultivars.

Comparison of innovative and non-invasive methods in estimating the fat content in pork trimmings

The purpose of research was to determine a possibility of application of computer vision systems (CVS) for estimation of fat content in pork trimmings in comparison with methods based on DXR (dual energy X-ray) and NIR (near-infrared reflectance spectroscopy). Research was conducted on 232 samples of pork trimmings. In order to verify the fat content determined by CVS, DXR, and NIR methods, fat content was also determined by the Soxhlet reference method. It was found that CVS can be used to estimate fat content in pork trimmings with a standard error of prediction between 4.9 and 5.6%. In order to achieve higher efficiency, it seems advisable to grind and standardise meat in a meat grinder with a kidney shaped plate.

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems.

An Application of Computer Vision Systems to Solve the Problem of Unmanned Aerial Vehicle Control

Abstract The paper considers an approach for application of computer vision systems to solve the problem of unmanned aerial vehicle control. The processing of images obtained through onboard camera is required for absolute positioning of aerial platform (automatic landing and take-off, hovering etc.) used image processing on-board camera. The proposed method combines the advantages of existing systems and gives the ability to perform hovering over a given point, the exact take-off and landing. The limitations of implemented methods are determined and the algorithm is proposed to combine them in order to improve the efficiency.

Computer vision technology for real-time food quality assurance during drying process

Drying process causes many changes in the mechanical, sensorial, and nutritional properties of food products. One main challenge in the production of dried food products with acceptable shape, size, color, and texture is to monitor and control their appearance in real-time manner. Currently, there is an increasing demand for real-time approaches such as computer vision technology to monitor and control the food quality indicators including shape, size, color, and texture during drying process. This note briefly describes the potential application of computer vision system in the monitoring and controlling of the food drying process in order to enhance the dried product quality and identifies prospects for future investigations.

CONTACTLESS INTEGRATED INTERFACE OF PRODUCTION LINES

The article presents an analysis of methods and means of computer vision. Revealed are the hallmarks of the methods used in the development of provinces and differences of methods from existing analogues. To identify and determine the direction of the gaze, we developed the algorithm for contactless control system, based on next models: the model of the appearance of eye area and evaluation of gaze direction. The method of tracking gaze on the basis of fixing the position of the eyeball proposed. For the application of computer vision systems developed algorithm of subsystem of smooth motion and a subsystem of saccades. We developed intellectual methods to track eye position when various positions of the head. These methods allow an operator of control room of flaw detection to control a production process without using hands. DOI: http://dx.doi.org/10.12731/2227-930X-2014-1-6

Application Of Computer Vision Systems For Passenger Counting In Public Transport

This paper presents a passengers counting system based on computer vision. System prototype were created and installed in Kaunas public city transport. Four algorithms were created to calculate passengers on public transport and their advantages and disadvantages were analyzed. Qualitative detection algorithms analysis carried out. Promising results were obtained with the Algorithm of barrier simulation for zones (ABSZ) which has low false rate and it is effective for people-counting. Counting results information can be used for public transport optimization or service quality improvement. DOI: http://dx.doi.org/10.5755/j01.eee.19.3.1232

Application of Computer Vision Systems in Colour Evaluation of Kunda: A Heat Desiccated Dairy Product

Application of Computer Vision Systems in Colour Evaluation of Kunda: A Heat Desiccated Dairy Product

Application of computer vision systems for estimation of fat content in poultry meat

The paper presents the possibility of use a computer vision system (CVS) for estimation fat content in poultry meat. The estimation of this ingredient content was made in the material obtained in industrial conditions. Chicken and turkey carcasses were selected randomly, from which the thigh muscles (from chickens and turkeys) and chicken breast muscles were taken. Analysis of correlation was made between content of white spots achieved with CVS method and fat content determined using the reference Soxhlet method. According to the achieved results, it was concluded that the computer vision system for images of turkey thigh muscles can be used to estimate its fat content.

Application of Computer Vision Systems to the Inspection of Contact Wire Wear

Measuring systems which automatically detect the degree of wire wear are necessary in order to carry out efficient infrastructure management and maintenance based on contact wire condition. The problem of wear measurement characterized by some important determining factors like rate of sampling and auscultation conditions is studied in detail. This paper describes different developments, based on computer vision, to measure contact wire wear. In general, computer vision in railways contributes towards improved service quality, reduced costs and increased security. This paper concludes with a comparative analysis of different computer vision systems to measure contact wire wear. For the covering abstract see ITRD E129315.

Hydrocyclone Underflow Analysis using Video-Graphic Techniques

The application of computer vision systems for the monitoring and control of hydrocyclone classifiers is considered. It is shown that by capturing and digitizing videographic images of the underflow of a hydrocylone, it is possible to accurately determine the spray angle of the underflow by means of suitable edge detection techniques. This spray angle can consequently be related to the performance of the cyclone.

Development and application of computer vision systems for use in livestock production

This paper examines the feasibility of applying computer vision systems to improve health, welfare and efficiency in livestock production. Very little directly relevant literature was revealed when reviewing the subject, so it is examined from first principles. After briefly describing the value of computer vision as a sensor with powerful observational and interpretative ability, the different steps in vision system development are identified and explored. Where possible this examination is related to computer vision work on livestock as well as other biological objects which by their typically varied nature offer meaningful paradigms for the livestockrelated work. The analysis suggests that most operations in livestock production tend to be at the complex end of the spectrum of vision-related problems currently being tackled in agriculture. Hence, only applications which have a significant production or welfare effect will be viable. Another vital element necessary for success in this application is a simultaneous understanding by the system designers of a diverse set of mechanisms (the production process, the interaction between process and sensor, vision algorithm building, and software and hardware systems). This calls for a multi-disciplinary, interactive approach to develop optimal solutions.