alexa Machine Vision of Clustering Identical Parts in Cellula
ISSN: 2169-0316

Industrial Engineering & Management
Open Access

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Research Article

Machine Vision of Clustering Identical Parts in Cellular Manufacturing

Prabhu M1* and Ramesh Kumar K2

1Research Scholar, Karpagam University, Coimbatore, Tamil Nadu, India.

2Principal, Asian College of Engineering and Technology, Coimbatore, Tamil Nadu, India.

*Corresponding Author:
Prabhu M
Research Scholar
Karpagam University
Coimbatore, Tamil Nadu, India
Tel: + 91-422-64711
E mail: [email protected]

Received November 17, 2014; Accepted February 25, 2015; Published February 28, 2015.

Citation: Prabhu M, Ramesh Kumar K (2015) Machine Vision of Clustering Identical Parts in Cellular Manufacturing. Ind Eng Manage 4:154. doi:10.4172/2169-0316.1000154.

Copyright: © 2015 Prabhu M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..

 

Abstract

Business process Re-Engineering (BPR) involves the Radical Redesigning complex and dynamic processes in Research and development (R&D) organizations with multi-layer projects is a difficult task. Previous researchers have proposed many perceptive approaches for BPR utilizing intuition and subjective judgment from “experts” at various stages of their implementation. The proposed method facilitates the reorganization of R&D processes to automate assembly lines; we have tested and evaluated various methods on part discrimination for a model conveyer line. An advanced method is developed on tracking and picking up a specified part among a variety of parts on a moving conveyer. The method consists of a part shape discrimination analysis following the image processing of CCD camera shots for on-conveyer parts retrieving stored images from a collection by comparing features automatically extracted from the images themselves. The common features used are mathematical measures of colour, texture or shape; hence virtually all current CBIR systems.

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