Proximity Contours: Vision based Detection and Tracking of Objects in Manufacturing Plants using Industrial Control Systems In a typical manufacturing setup, field sensors are used to detect the presence of moving material on the production line. Sensory information is interfaced with a centralized control systems for continuous and long distance monitoring. However, the field sensors are prone to failures and may cause frequent downtimes which in turn causes reduction in productivity. In this paper, a practical and comprehensive visual inspection system is proposed to automatically control the plant operations. The proposed approach improves productivity and solves several challenges including the speed of execution to support realtime operations, number and choice of image processing stages as well as associated techniques and algorithms.The presented solution inspects a production process deployed in metal industry and detects the moving rods on a conveyor platform. The shape of the moving rods is characterized using a combination of GrabCut segmentation, and clusteringbased image thresholding methods. To detect and track the movement of rods, proximity contours are identified using lowlevel pixel information of only two images: one when the conveyor is fully occupied with the rods and the other when the conveyor is empty. Further, this visual sensory information is used to automatically generate control logic for tracking and monitoring a conveyor platform and can be smoothly integrated with industrial control system enabling realtime closed loop operations.

LSD Based Vision Detection System for Industrial Robot under Complex Illumination Conditions In this paper, we present an object vision detection and localization method approach to the industrial robotic system under complex illumination conditions. In industrial applications of machine vision, many of feature extraction algorithms depend on edge detection, which can be affected by ambient illumination variation and loses efficacy. We propose a robust method that combines image enhancement, edge detection and feature information extraction to detect and locate target components. The key idea is to use a histogram key point constrained homomorphic filtering to enhance the image followed by line and arc segments feature detection. Finally, the experimental results on probe endface image dataset and steelmaking automation manufacture demonstrate the ability of the proposed method to perform more robust and accurate effectiveness under illumination variant conditions.