IMAGE SEGMENTATION BASED ON THE STATISTICAL VARIATIONAL FORMULATION USING THE LOCAL REGION INFORMATION

Variational Image Segmentation by Unifying Region and Boundary Information

This paper presents a novel variational image segmentation technique that unifies both geodesic active contours and geodesic active regions. The originality of the method is the automatic and dynamic global weighting of the respective local equations of motion. A new stopping function for the geodesic active contours is also introduced, which proves to have a better behavior in the vicinity of ...

Statistical shape knowledge in variational image segmentation

When interpreting an image, a human observer takes into account not only the external input given by the intensity or color information in the image, but also internally represented knowledge. The present work is devoted to modeling such an interaction by combining in a segmentation process low-level image cues and statistically encoded prior knowledge about the shape of expected objects. To th...

Texture-based Color Image Segmentation Using Local Contrast Information

A novel texture-based segmentation algorithm for color images is proposed in this paper. Many powerful color image segmentation algorithms such as J-Segmentation (JSEG)suffer from over segmentation. Analysis of undesirable results from JSEG segmentation has led to the development of an improved JSEG method called improved contrast JSEG, or IC-JSEG. The proposed method first constructs a contras...

Non-local Unsupervised Variational Image Segmentation Models

New image denoising models based on non-local image information have been recently introduced in the literature. These so-called ”non-local” denoising models provide excellent results because these models can denoise smooth regions or/and textured regions simultaneously, unlike standard denoising models. Standard variational models s.a. Total Variation-based models are defined to work in a smal...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models