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Image segmentation is an important research topic in the field of computer vision. Spectral Clustering (SC) algorithm is one of the most popular used clustering methods for image segmentation. However, the cluster number must be estimated by expertise users to be determined. This limits its application in image segmentation. In this paper, we proposed an image segmentation method based on saliency...
Texture segmentation is one of the most challenging problems in the field of image segmentation. Segmenting multi-textured image into different classes of textured region with a minimum rate of misclassification is a challenging issue. This paper proposes a framework for improving misclassification rate by using ICA for designing filter bank and Ant Tree Clustering algorithm, inspired by the self...
Human facial features localization is an important process of face recognition, since it helps generating face images in accordance with specified criteria, or building unique face model. This paper presents a novel method for finding facial features through Gabor filtering and k-means clustering analysis. By Gabor filtering, face images are transformed into magnitude responses. In magnitude responses,...
In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers...
Traditional iris recognition systems can achieve excellent performance in both verification and identification. However, most of the existing systems adopted a similar technique to deal with the iris image. In this paper, we propose a novel matching strategy with invariant properties, which is based on the possibilistic fuzzy clustering algorithm, to compare a pair of local feature sets. Moreover,...
This paper describes an effective framework to perform image segmentation and find regions of interest (ROI) in a user input object in an interactive way. Similar image objects are then retrieved from a repository. The repository stores off-line trained feature data of image objects, which was obtained by applying feature extraction and dimension reduction analysis to the ROI. The advantage of our...
The Probabilistic Index Map (PIM) model was originally proposed for video processing to extract background of video frames. In this paper, we introduce the PIM model for texture segmentation. We first extract texture features based on Laws and Gabor filters respectively. Then we present a fuzzy k-means method to generate the index map and palette, and use the PIM model to improve the segmentation...
This paper presents the development of an unsupervised image segmentation framework (referred to as CTex) that is based on the adaptive inclusion of color and texture in the process of data partition. An important contribution of this work consists of a new formulation for the extraction of color features that evaluates the input image in a multispace color representation. To achieve this, we have...
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