Visual inspection system of various manufactured geometries is widely adopted in industry whereas the inspection system's intelligence is yet to be improved. Mixing of various geometries is a common problem in coating industry and the inspection is carried out manually. This paper gives a framework for classifying the mixed parts from the images captured without prior information of geometries. The parts were segmented from the image using Otsu method followed by morphological operations. Then the borders were extracted and smoothened by Fourier approximation. The touching objects were separated using curvature analysis. Features such as area and skeleton were extracted from the individual parts. The geometries were then classified by k-means clustering successfully. The developed algorithm works for a variety of geometries and is independent of translation and rotation of the parts. © 2013 © 2013 CAD Solutions, LLC.

Ramanathan Muthuganapathy

Department of Engineering Design

Rajalingappaa Shanmugamani

M Ramanathan

Computer vision

Inspection

INSPECTION EQUIPMENT

Mathematical morphology

CONVEX-TOUCHING OBJECTS

CURVATURE ANALYSIS

Different geometry

FOURIER APPROXIMATIONS

k-means clustering

MORPHOLOGICAL OPERATIONS

Shape classification

VISUAL INSPECTION SYSTEMS

geometry

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Unsupervised Shape Classification of Convexly Touching Coated Parts with Different Geometries

Computer-Aided Design and Applications

Inspection and classification of food grains is a manual process in many of the food grain processing industries. Automation of such a process is going to be beneficial for industries facing shortage of skilled workforce. Machine Vision techniques are some of the popular approaches for developing such automations. Most of the existing works on the topic deal with identification of the rice variety by analyzing images of well separated and isolated rice grains from which a lot of geometrical features can be extracted. This paper proposes techniques to estimate geometrical parameters from the images of scattered as well as heaped rice grains where the grain boundaries are not clearly identifiable. A methodology based on convexity is proposed to separate touching rice grains in the scattered rice grain images and get their geometrical parameters. And in case of heaped arrangement a Pixel-Distance Contribution Function is defined and is used to get points inside rice grains and then to find the boundary points of rice grains. These points are fit with the equation of an ellipse to estimate their lengths and breadths. The proposed techniques are applied on images of scattered and heaped rice grains of different varieties. It is shown that each variety gives a unique set of results. © 2017 SPIE.

Proceedings of SPIE - The International Society for Optical Engineering

Classification of rice grain varieties arranged in scattered and heap fashion using image processing

ABSTRACT: We present a method to extract the contour of geometric objects embedded in binary digital images using techniques in computational geometry. Rather than directly dealing with pixels as in traditional contour extraction methods, we process on object point set extracted from the image. The proposed algorithm works in four phases: point extraction, Euclidean graph construction, point linking and contour simplification. In point extraction phase, all pixels that represent the object pattern are extracted as a point set from the input image. We use the color segmentation to distinguish the object pixels from the background pixels. In the second phase, a geometric graph G=(V,E) is constructed, where V consists of the extracted object point set and E consists of all possible edges whose Euclidean distance is less than a threshold parameter, l ; which can be derived from the available information from the point set. In point linking phase, all border points are connected to generate the contour using the orientation information inferred from the clockwise turn angle at each border point. Finally, the extracted contour is simplified using collinearity check. Experiments on various standard binary images show that the algorithm is capable of constructing contours with high accuracy and achieves high compression ratio in noisy and non-noisy binary images. © 2015, © 2015 CAD Solutions, LLC.

Fulltext

A Graph-based Geometric Approach to Contour Extraction from Noisy Binary Images

Pattern Recognition

A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search

Computers in Industry

Machine vision based automatic separation of touching convex shaped objects

Clump splitting via bottleneck detection and shape classification

Food and Bioprocess Technology

Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables

Biosystems Engineering

A Fourier analysis based algorithm to separate touching kernels in digital images

Journal	Data powered by TypesetComputer-Aided Design and Applications
Publisher	Data powered by TypesetTaylor and Francis Inc.
ISSN	16864360
Open Access	No