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This paper proposes a content-based color trademark retrieval system. First, the edges of each color trademark are detected. Then, the smallest circle that covers the trademark is derived. Based on the smallest circle and edges, the feature called hit statistic on a dartboard of the color trademark is extracted. Using this feature, this paper constructs an efficient and simple color trademark retrieval system, which is robust to rotation, translation, scaling and some geometric distortions. Some experiments are conducted to compare the proposed system with the existing one using Zernike moment, and the results show that the proposed system is superior to that using Zernike moment.

This study has developed an object detection and segmentation technique for processing cytoplasm and cell nucleus on ThinPrep-cervical smear images at various magnifications. Both edge detection techniques and region growing for adaptive threshold were applied to a segment cell nucleus, a cytoplasm, and backgrounds using a cervical cell image.

To validate the accuracy and feasibility of the proposed method, we took a variety of cervical cell images to perform a series of experiments. The images were of superficial cells, intermediate cells, and abnormal cells, with each taken from ThinPrep smears at various magnifications. The results indicate that the proposed method can automatically segment cell nucleus and cytoplasm regions while accurately extracting object contours. These results can serve as a reference for examiners of cell pathologies.

Decision making is one of the smouldering problems in day to day works. Human emotions play crucial role in decision-making systems. While person is in high emotion he cannot make proper decision. Robust local binary pattern (RLBP) operator is more powerful to recognize the emotions and extends the features of local binary pattern (LBP). However, there are some precincts like discriminating bright faces against dark features and vice versa and intra-class variances increase. The RLBP solves this problem by finding minimum of LBP codes and their complements. However, it miss the mark for different local structures a similar feature is obtained, weak contrast local patterns and similar strong contrast local patterns. Hence, the discriminative robust local binary pattern (DRLBP) method is proposed to retain the contrast information of image patterns next to considering both edge and texture information. Nevertheless, LBP family methods are highly sensitive to noise. To trounce these drawbacks this paper extends fuzzy rule-based DRLBP which is more robust to noise, low contrasted, uneven lighting conditions, variations in expressions and rotation variant images.

Reduction of search region in stereo correspondence can increase performances of the matching process, in the context of execution time and accuracy. For an edge-based stereo matching, we establish relationships between the search space and the parameters like relative displacement of edges, disparity under consideration, image resolution, CCD (Charge-Coupled Device) dimension and focal length of the stereo system. Then, we propose a novel matching strategy for the edge-based stereo. Afterward, we develop a fast edge-based stereo algorithm with combination of the obtained matching strategy and a multiresolution technique using Haar wavelet. Considering the conventional multiresolution technique using Haar wavelet, the execution times of our proposed method are decreased between 26% to 47% in the feature matching stage. Moreover, the execution time of the overall algorithms (including feature extraction and feature matching) is decreased between 15% to 20%. Theoretical investigation and experimental results show that our algorithm has a very good performance; therefore this new algorithm is very suitable for fast edge-based stereo applications like stereo robot vision.

Various theorems for classical piecewise-linear (PL) knots are shown to carry over to PL virtual knots. We then classify PL virtual knots up to edge index six.