Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to ...

what: color, motlon what, where? where: depth and reglon This paper describes a statistical integration algorithm for color, motion and stereo disparity, and introduces a realtime stereo system that can tell us where and what objects are moving. Regarding the integration algorithm, motion estimation and depth estimation are simultaneously performed by a clustering process based on motion, stere...

A new color image segmentation method combining mean shift and hierarchical clustering algorithm is presented in this paper. The proposed algorithm preprocesses an input image by mean shift algorithm to form segmented regions that preserve the desirable discontinuity characteristics of image. The number of segmented regions, instead of the number of image pixels, is considered as the input data...

This paper presents a new segmentation strategy, based on a blended procedure whose goal is to combine several segmentation maps in order to finally get a more reliable and accurate segmentation result. The fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associa...

Face detection is a very challenging and interesting problem. In this paper, a new scheme for detection of multiple faces using Haar wavelet packet decomposition based on quantized skin color region merging under unconstrained scene conditions is presented. Color clustering and filtering using approximations of the YCbCr and HSV skin color subspaces are applied on the original image by providin...

When image segmentation is treated as a problem of clustering color pixels, the nite mixture model with EM algorithm can be used to cluster color space samples. With estimated mixture model parameters, we adopt the BYY model selection criterion to determine how many regions should be segmented on a given color image. In this paper, we experimentally investigate the eeect of choosing diierent co...

The log-likelihood energy term in popular model-fitting segmentation methods, e.g. [64, 14, 50, 20], is presented as a generalized “probabilistic” K-means energy [33] for color space clustering. This interpretation reveals some limitations, e.g. over-fitting. We propose an alternative approach to color clustering using kernel K-means energy with well-known properties such as non-linear separati...

We present an automatic iris color classification method suitable for predicting human eye color. The proposed method combines the abilities of intra-palette merging clustering technique with a thorough measurement of the iris color using the MartinSchultz scale. Intra-palette merging ensures the minimum set of group colors suitable for correctly representing the color information within human ...

This paper presents an IF1F2 color space for describing colors in microscopic images. Unlike the classical color spaces that treat the R, G, and B (RGB) components of a pixel’s color as independent elements, the proposed color space treats RGB as a sampled spectral signal, called a RGB signal. Then, based on the Fourier spectrum analysis of the RGB signal, the 1-D discrete Fourier transform is ...

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...