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The goal of image segmentation is to partition an image into regions that are internally homogeneous and heterogeneous with respect to neighbouring regions. Recently, a link shifting based pyramidal segmentation method was proposed to resolve existing problems with elongated regions. In this paper, we propose further improvements by replacing pixel intensities at the base level with pixel level higher...
In this paper, we propose a statistical scheme for recognizing three-dimensional textures shown in motion images, which we call dynamic textures. The texture characteristics emerges in the distinct movement in the motion images, and the dynamic cues would be useful especially for recognizing ambiguous texture patterns in noisy images. We apply cubic higher-order auto-correlation (CHLAC) to extract...
In this paper, we propose a method for extracting color image features, called color index local auto-correlations. Pixel color is quantized and described sparsely in a manner similar to the color indexing of color histograms. In addition, by utilizing spatial auto-correlations of the color indexes, the characteristics of color texture can be extracted more effectively than ordinary histogram-based...
Counting (identical) objects in images is a simple yet fundamental recognition task that requires exhaustive human effort. Automation of this task would reduce the human load significantly. In this paper, we propose a statistical method to automatically count objects in an image sequence by using higher-order local auto-correlation (HLAC) based image features and multiple regression analysis (MRA)...
Higher order local autocorrelation (HLAC) proposed by Otsu [5] is often used in the recent computer vision application such as gate recognition, object tracking, or video surveillance. The feature value of HLAC is the integral of the product of local pixels' value, and usually the integrals are calculated in entire images. However, in the image recognition, feature selection is often effective for...
Real-time recognition of moving objects is an important problem in video surveillance applications, ITS (Intelligent Transport Systems), robot vision and so on. In this paper, we propose a method to recognize multiple moving objects simultaneously by using Cubic Higher-order Local Auto- Correlation (CHLAC) features. To perform the recognition, we exploit the additivity property of CHLAC which allows...
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