Design Of Computer Vision System Research Articles

Analytical research of wind farms damage

Wind farms are expensive and technically complex equipment. Maintaining it in working condition is an important task not only from an economic point of view, but also from a practical point of view. Even minor breakdowns and damages should be quickly eliminated. Wind farms are constantly being upgraded and improved. They are produced under specific operating conditions. However, this cannot completely exclude emergency situations. Therefore, engineering services involved in monitoring the operation of a wind farm should conduct inspections with some frequency. Until recently, such inspections were carried out entirely by people, which was associated with various problems and risks. People put their lives in danger; money is spent on the departure of a team of engineers to perform a relatively simple inspection operation. This study can be useful in the development and design of computer vision systems aimed at operating during inspections of wind farms.

Design of Computer Vision System for Objects Recognition in Automation Industries

The field of machine vision has been developing at quick pace. The development in this field, dissimilar to most settled fields, has been both in expansiveness and profundity of ideas and procedures. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear, and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of different objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial Neural Network (ANN) is used for classification of different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. Invariant example acknowledgment utilizing neural systems is an especially appealing methodology on account of its closeness with natural frameworks. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA.

To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400–720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems.

Neural dynamics of surface perception: Boundary webs, illuminants, and shape-from-shading

A real-time visual processing theory is used to provide a new approach to the analysis of surface perception, notably shape-from-shading. The theory elsewhere has been used to explain data about boundary detection and completion, textural segmentation, depth perception, color and brightness perception, and striate-prestriate cortical interactions. Neural network interactions within a multiple scale boundary contour (BC) system and feature contour (FC) system are used to explain these phenomena. Each spatial scale of the BC system contains a hierarchy of orientationally tuned interactions, which can be divided into two successive subsystems called the OC filter and the CC loop. The OC filter contains two successive stages of oriented receptive fields which are sensitive to different properties of image contrasts. The OC filter generates inputs to the CC loop, which contains successive stages of spatially short-range competitive interactions and spatially long-range cooperative interactions. Feedback between the competitive and cooperative stages synthesizes a coherent, multiple scale structural representation of a smoothly shaded image, called a boundary web. Such a boundary web regulates multiple-scale filling-in reactions within the FC system which generate a percept of form-and-color-in-depth. Computer simulations establish key properties of a boundary web representation: nesting of boundary web reactions across spatial scales, coherent completion and regularization of boundary webs across incomplete image data, and relative insensitivity of boundary webs to illumination level and highlights. The theory clarifies data about interactions between brightness and depth percepts, transparency, influences of highlights on perceived surface glossiness, and shape-from-texture gradients. The total network suggests a new approach to the design of computer vision systems, and promises to provide a universal set of rules for 3D perceptual grouping of scenic edges, textures, and smoothly shaded regions.

Neural dynamics of perceptual grouping: textures, boundaries, and emergent segmentations.

A real-time visual processing theory is used to analyze and explain a wide variety of perceptual grouping and segmentation phenomena, including the grouping of textured images, randomly defined images, and images built up from periodic scenic elements. The theory explains how “local” feature processing and “emergent” features work together to segment a scene, how segmentations may arise across image regions that do not contain any luminance differences, how segmentations may override local image properties in favor of global statistical factors, and why segmentations that powerfully influence object recognition may be barely visible or totally invisible. Network interactions within a Boundary Contour (BC) System, a Feature Contour (FC) System, and an Object Recognition (OR) System are used to explain these phenomena. The BC System is defined by a hierarchy of orientationally tuned interactions, which can be divided into two successive subsystems called the OC filter and the CC loop. The OC filter contains two successive stages of oriented receptive fields which are sensitive to different properties of image contrasts. The OC filter generates inputs to the CC loop, which contains successive stages of spatially short-range competitive interactions and spatially long-range cooperative interactions. Feedback between the competitive and cooperative stages synthesizes a global context-sensitive segmentation from among the many possible groupings of local featural elements. The properties of the BC System provide a unified explanation of several ostensibly different Gestalt rules. The BC System also suggests explanations and predictions concerning the architecture of the striate and prestriate visual cortices. The BC System embodies new ideas concerning the founda-tions of geometry, on-line statistical decision theory, and the resolution of uncertainty in quan-tum measurement systems. Computer simulations establish the formal competence of the BC System as a perceptual grouping system. The properties of the BC System are compared with probabilistic and artificial intelligence models of segmentation. The total network suggests a new approach to the design of computer vision systems, and promises to provide a universal set of rules for perceptual grouping of scenic edges, textures, and smoothly shaded regions.