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25% of the total revenue earning is achieved from Textile exports for some countries like Bangladesh. It is thus important to produce defect free high quality garment products. Inspection processes done on fabric industries are mostly manual hence time consuming. To reduce error on identifying fabric defects requires automotive and accurate inspection process. Considering this lacking, this research implements a Textile Defect detector. A multi-layer neural network is determined that best classifies the specific problems. To feed neural network the digital fabric images taken by a digital camera and converts the RGB images are first converted into binary images by restoration process and local threshold techniques, then three different features are determined for the actual input to the neural network, which are the area of the defects, number of the objects in a image and finally the shape factor. The develop system is able to identify two very commonly defects such as Holes and Scratches and other types of minor defects. The developed system is very suitable for Least Developed Countries, identifies the fabric defects within economical cost and produces less error prone inspection system in real time. Keywords: Textile defects, threshold decision tree, multi-layer neural networks, resilient back propagation, cross validation. [ABSTRACT FROM AUTHOR]

Although many systems exist for automatic classification of faces according to their emotional expression, these systems do not explicitly estimate the strength of given expressions. This paper describes and empirically evaluates an algorithm capable of estimating the degree to which a face expresses a given emotion. The system first aligns and normalizes an input face image, then applies a filter bank of Gabor wavelets and reduces the data's dimensionality via principal components analysis. Finally, an unsupervised Fuzzy-C-Mean clustering algorithm is employed recursively on the same set of data to find the best pair of principle components from the amount of alignment of the cluster centers on a straight line. The cluster memberships are then mapped to degrees of a facial expression (i.e. less Happy, moderately happy, and very happy). In a test on 54 previously unseen happy faces., we find an orderly mapping of faces to clusters as the subject's face moves from a neutral to very happy emotional display. Similar results are observed on 78 previously unseen surprised faces. [ABSTRACT FROM AUTHOR]