A Multi-Level Colour Thresholding Based Segmentation Approach for Improved Identification of the Defective Region in Leather Surfaces

Vision systems are recently adopted for defect detection in leather surface to overcome difficulties of labour intensive, time consuming manual inspection process. Suitable image processing techniques needs to be developed for accurate detection of leather defects. Existing research works have focused for gray scale based image processing techniques which requires conversion of colour images using an averaging method and it lacks sensitivity for detecting the leather defects due to the random and texture surface of the leather.  This work presents a colour processing approach for improved identification of leather defects using a multi-level thresholding function. In this work, the colour leather images are processed in ‘Lab’ colour domain for improving the human perception of discriminating the leather defects.  In the present work, the specific range of values for the colour attributes of different leather defect in colour leather samples are identified using the colour histogram.  MATLAB software routine is developed for identifying defects in specific ranges of colour attributes and the results are presented.  From the results, it is found that proposed provides a simpler approach for identifying the defective regions based on the colour attributes of the surface with improved human perception. The proposed methodology can be implemented in graphical processing units for efficiently detecting several types of defects using specific thresholds for the automated real-time inspection of leather defects

A Signature Identification Method Based on Strength and Strokes Direction

Abstract. It was much more complex and difficult for off-line signature identification attributable to the limitation of available information. To solve the problem, a signature identification method based on strength and strokes direction was proposed. The signature image acquired was gray-scaled and filtered at the stage of preprocess; then the image was two-valued with different threshold based on strength feature, the regions which grayscale was less than threshold were retained; the strokes which possess distinctive directional feature were extracted by using mathematical morphology and combining different scales/directions structure element based on strokes direction feature; at last judgement was maked for sample in accordance with corresponding feature. Experimental results showed the proposed method can enhance accurate rate effectively, improve real-time performance, which was a try beneficial to apply new methods for off-line signature identification

Single-imager occupant detection based on surface reconstruction

This thesis introduces a novel framework for a real-time occupant detection system capable of extracting both two- and three-dimensional information using a single imager with active illumination. The primary objective of this thesis is to demonstrate the feasibility of such a low-cost classification system with comparable performance to multi-camera based stereo vision systems. Severe illumination conditions characterised by a frequent and wide illumination fluctuation are also challenging problems addressed in this work. The proposed system is designed to solve a problem of classifying three occupant classes being an adult, a forward-facing child seat, and a rear-facing child seat. DoubleFlash is employed to eliminate the influence of ambient illumination and to compress the optical dynamic range of target scenes. The idea underlying this technique is to subtract images flashed by different illumination power levels. The extension of this active illumination technique leads to the development of a novel shadow removal technique, called ShadowFlash. By simulating an artificial infinite illuminating plane over the field of view, the technique produces a shadowless scene without losing image details by composing multiple images illuminated from different directions. The ShadowFlash technique is then extended to the temporal domain by employing the sliding n-tuple strategy, which is introduced to avoid the reduction of the original frame rate. A modified active contour model, facilitated by morphological operations, extracts the boundary of the target object from the shadow-free scenes produced by the ShadowFlash. Based on the brightness information of the image triplet generated by the DoubleFlash, the orientations of the object surface at pixel points are estimated by the photometric stereo method and integrated into the 3D surface by means of global minimisation. The boundary information is used to specify the region of interest to reconstruct. Investigating both the two- and three-dimensional properties of vehicle occupants, 29 features are defined for the training of a neural network. The system is tested on a database of over 84,000 frames collected from a wide range of objects in various illumination conditions. A classification accuracy of 98.9% was achieved within the decision-time limit of three seconds