Shape recognition is an important task in the field of computer vision. Many algorithms were developed to extract features from captured images of valuable objects. The traditional polygonal approximation algorithm (PAA) is a well-known algorithm in shape analysis for digital image processing and pattern recognition. The main objective of this algorithm is to convert a set of connected points in digitized images into a list of fitted lines, which represent a two-dimensional polygon. The main problem with this algorithm is that it lacks the ability to extract curved objects from the digitized images. In this paper, a new algorithm called an enhanced polygonal approximation algorithm (EPAA) is developed, based on the PAA, to extract both straight and curved features from digitized images of twodimensional products. The EPAA is capable of classifying line and arc segments as well as circles. A description of the EPAA and an example of its application in the field of production engineering are presented.
The construction of a three-dimensional (3D) model of objects is a key element of many interesting applications such as computer graphics and computer-aided design (CAD). Although many techniques for the construction of 3D models are available, these techniques are still the focus of attention from researchers in the computer vision community. In the current paper, a low-cost vision system has been employed to construct 3D models of small mechanical products using the image-based modelling technique and the basic principles of the orthographic views. The system has been calibrated to compensate for the variations in size of the captured images resulting from the difference in dimensions of the product to be reconstructed. The 3D models are constructed from two captured images, which represent two different views of the product. The constructed 3D model can be viewed through a developed software or exported to any CAD software, such as AutoCAD, for further editing. The mathematical model of the employed 3D construction technique is presented and the verification of the proposed system is introduced.
In this work, a proposed Computer Vision System (CVS) has been applied to measure the porosity of iron oxide (Fe 3 O 4) specimens. Measurement of the porosity using CVS reduces time, effort and cost compared to the conventional methods. Fe 3 O 4 was received from iron rolling process in the form of thin layer, and then converted to powder by grinding process and filtering by 200 μm sieve. Fe 3 O 4 specimens were then manufactured by powder metallurgy (PM) technique using steel die subjected to a compression load (300 KN) to form cylindrical discs with 27 mm diameter and 10 mm height. Optical microscope with digital camera has been used in capturing images of the surfaces of Fe 3 O 4 specimens. The proposed CVS uses k-means algorithms, which divide the intended surfaces into distinct regions and by computing the ratio between these regions, the specimen porosity is obtained. Layers of the specimens are removed to measure the average porosity inside the specimens. The average value of porosity of specimens has been obtained for the original specimen and its mean value is ∼26.39%. In comparison, the porosity computed by CVS was very close to that obtained using conventional method, which confirms the efficiency of this technique.
Abstract-In this paper, a new Intelligent system based on neurofuzzy for detecting and diagnostics the wear and damage of the milling cutter is presented. The compatibility between the computer vision and neurofuzzy techniques is introduced. The proposed approaches consists of capturing the milling cutter image, Fuzzy edge detection, Chain code technique for feature extraction and finally, apply the neural network on the feature. The results of the study are three different diagnostics models , The first is diagnostic model for the original profile of the perfect cutter, the second is model for the wearied profile and the third is model for the damage profile. Experimental test results show that the proposed system is reliable, practical and can be used for the easy distinguish between the wear and damage automatically.
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