In this paper we present a method for constraining the deformations of Snakes (Active Contour Models) when segmenting a known class of objects. The method we propose is similar to both Active Shape Models (ASM) but without the landmark identification and correspondence requirement, and to Active Contour Models (ACM), but armed with a priori information about shape variation. Rather than represe...

This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPTAFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM fo...

The active contour model [8,9,2] is one of the most well-known variational methods in image segmentation. In a recent paper by Bresson et al. [1], a link between the active contour model and the variational denoising model of Rudin-Osher-Fatemi (ROF) [10] was demonstrated. This relation provides a method to determine the global minimizer of the active contour model. In this paper, we propose a ...

In this paper we present a novel approach for representing and evolving deformable active contours. The method combines piecewise regular Bézier models and curve evolution defined by local Free Form Deformation. The contour deformation is locally constrained which allows contour convergence with almost linear complexity while adapting to various shape settings and handling topology changes of t...

In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation. GCBAC approach is a combination of the iterative deformation idea of active contours and the optimization tool of graph cuts. It differs from traditional active contours in that it uses graph cuts to iteratively deform the contour and its cost function is defined as the summation of edge weights...

This is a short guide to explain how to quickly minimize a large class of segmentation models called active contours. We believe that the proposed theory and algorithm, introduced in a series of papers [6, 4, 10, 11], produce so far one of the most efficient minimization methods for the active contour segmentation problem. For example, the well-know cameraman picture, which size is 256× 256, is...

Local Segmentation is the fundamental task for image processing. Consider to the problem of low segmentation precision and contour control instability for image local segmentation, a local segmentation theory is researched that based on SSCACM (spectral clustering with spatial coherence property jointing active contour model). First, by applying spatial coherence property constraint of image pi...

This paper investigates novel LBP-guided active contour approaches to texture segmentation. The Local Binary Pattern (LBP) operator is well suited for texture representation, combining efficiency and effectiveness for a variety of applications. In this light, two LBP-guided active contours have been formulated, namely the scalar-LBP Active Contour (s-LAC) and the vector-LBP Active Contour (v-LA...

This work introduces a novel active contour-based scheme for unsupervised segmentation of protein spots in two-dimensional gel electrophoresis (2D-GE) images. The proposed segmentation scheme is the first to exploit the attractive properties of the active contour formulation in order to cope with crucial issues in 2D-GE image analysis, including the presence of noise, streaks, multiplets and fa...

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties r...