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Automated vision inspection has become a vital part of the quality monitoring process. This paper compares the development and performance of two methodologies for a machine vision inspection system online for high speed conveyor. The first method developed is the Thresholding technique image processing algorithms and the second method is based on the edge detection. A case study was conducted to benchmark these two methods. Special effort has been put in the design of the defect detection algorithms to reach two main objectives: accurate feature extraction and on-line capabilities, both considering robustness and low processing time. An on-line implementation to inspect bottles is reported using new communication technique with GigE Vision camera and industrial Gigabit Ethernet network. The system is validated on olive oil bed. The implementation of our algorithm results in an effective real-time object tracking. The validity of the approach is illustrated by the presentation of experiment results obtained using the methods described in this paper.

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Human Action Recognition methods have prospered during the last decade. They seek to automatically analyze ongoing activities in different camera views by using machinelearning algorithms in video sequences. Various human action recognition methods match local features and global features using action class labels in which abundant visual spatio-temporal information can hardly be generalized. To overcome this problem, we propose a novel notion of mid-level representations to construct a discriminative and informative semantic concept for human action recognition. This work introduces a mid-level representation based on the Optical Flow (OF) method, Hu and Zernike moment together. First we extract from each video, U h and Uv motion vectors by forming motions curvatures. Second, we determine the Hu moment and Zernike that serve as the feature vector of an action. Our method was tested and evaluated through a classification of the KTH and Weizmann datasets, with an Artificial Neural Network classifier (ANN). The results prove the accuracy of the suggested approach.