In this paper a procedure is described for the segmentation and tracking of objects in color image sequences. For this purpose, we propose the novel procedure of K-Means with connectivity constraint algorithm as a general segmentation algorithm combining several types of information including color, motion and compactness. In this algorithm, the use of spatiotemporal regions is introduced since...

Image Segmentation is the process of partitioning a digital image into non-overlapping distinct regions, so that significant information about the image could be retrieved and various analysis could be performed on that segmented image. The aim of this study is to reduce the noise, enhance the image quality by considering the spatial information without losing any important information about th...

Image segmentation is an important process of image analysis. Most of the published approaches for image segmentation need to set appropriate parameter values to cope with the uncertainty problem. However, the parameter values are usually problem dependent and hardly obtained. In this paper, a simple and fast GA-SA hybrid image segmentation algorithm (HISA) is proposed. In HISA, the well-known ...

Abstract: In this paper, a modified GrabCut algorithm is proposed using a clustering technique to reduce image noise. GrabCut is an image segmentation method based on GraphCut starting with a user-specified bounding box around the object to be segmented. In the modified version, the original image is filtered using the median filter to reduce noise and then the quantized image using K-means alg...

Algorithm k-means is useful for grouping operations; however, when is applied to large amounts of data, its computational cost is high. This research propose an optimization of k-means algorithm by using parallelization techniques and synchronization, which is applied to image segmentation. In the results obtained, the parallel k-means algorithm, improvement 50% to the algorithm sequential k-me...

In this paper we focus on image segmentation by proposing a new algorithm based on Haar wavelet decomposition and Kmeans algorithm. When Haar wavelet decomposition is applied to an image it gives an idea about high frequency components. If higher levels of decomposition are performed, different texture region information can be captured. The paper deals with the texture segmentation of an image...

During the unfolding measures that are taken for the purpose of leukemia detection, segmentation of blood cells is a vital step. In this paper two approaches of such segmentation technique is proposed. While one uses K-means clustering, other uses color image based segmentation method. Both the processes segment the image into two regions, blasts & backgrounds. These blasts are our area of inte...

Image segmentation places an important role in image processing. This segmentation process can be done using various techniques like clustering, thresholding, edge detection and region extraction. This paper gives introduction to image processing operations and clustering process. Then the overview and algorithmic process of each clustering technique such as K-Means clustering, Kernel K-Means c...

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy CMeans clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features fr...

This paper presents a new and simple segmentation method based on the K-means clustering procedure and a two-step process. The first step relies on an original de-texturing procedure which aims at converting the input natural textured color image into a color image, without texture, that will be easier to segment. Once, this de-textured (color) image is estimated, a final segmentation is achiev...