Incorporating shape prior into geodesic active contours for detecting partially occluded object

A new method to incorporate shape prior knowledge into geodesic active contours for detecting partially occluded object is proposed in this paper. The level set functions of the collected shapes are used as training data. They are projected onto a low dimensional subspace using PCA and their distribution is approximated by a Gaussian function. A shape prior model is constructed and is incorporated into the geodesic active contour formulation to constrain the contour evolution process. To balance the strength between the image gradient force and the shape prior force, a weighting factor is introduced to adaptively guide the evolving curve to move under both forces. The curve converges with due consideration of both local shape variations and global shape consistency. Experimental results demonstrate that the proposed method makes object detection robust against partial occlusions.     

Relaxation algorithm for shape matching of two-dimensional objects

This paper describes a relaxation algorithm without a parameter for shape matching of partially occluded two-dimensional objects. This technique is based on a relaxation labelling process of Rosenfeld et al. for reducing or eliminating the ambiguity. The shapes are represented by polygonal approximation. The proposed relaxation method is used to find acceptable line segment pairs of polygons.     

Planar shape matching based on binary tree shape representation

In this paper we suggest a new way of representing planar two-dimensional shapes and a shape matching method which utilizes the new representation. Through merging of the neighboring boundary runs, a shape can be partitioned into a set of triangles. These triangles are inherently connected according to a binary tree structure. Here we use the binary tree with the triangles as its nodes to represent the shape. This representation is found to be insensitive to shape translation, rotation, scaling and skewing changes due to viewer's location changes (or the object's pose changes). Furthermore, the representation is of multiresolution.

In shape matching we compare the two trees representing two given shapes node by node according to the breadth-first tree traversing sequence. The comparison is done from top of the tree and moving downward, which means that we first compare the lower resolution approximations of the two shapes. If the two approximations are different, the comparison stops. Otherwise, it goes on and compares the finer details of the two shapes. Only when the two shapes are very similar, will the two corresponding trees be compared entirely. Thus, the matching algorithm utilizes the multiresolution characteristic of the tree representation and appears to be very efficient.     

基于距离比上下文的形状描述与识别方法

基于形状轮廓上的采样点到形状质心的距离,提出了一种距离比上下文形状描述符,用于形状识别和检索。该描述符计算简单,能有效区分不同形状,本质上具有平移、缩放不变性,且在一定程度上能杭部分遮挡和形变。用动态规划算法度量形状比上下文之间的距离,解决了对起始轮廓点的选择问题。在kimia' s-99形状图像数据库中的实验结果表明,该方法在单目标封闭轮廓的形状图像检索中取得了良好的效果。     

Search strategies for shape regularized active contour

Nonlinear shape models have been shown to improve the robustness and flexibility of contour-based object segmentation when there are appearance ambiguities between the object and the background. In this paper, we focus on a new search strategy for the shape regularized active contour (ShRAC) model, which adopts existing nonlinear shape models to segment objects that are similar to a set of training shapes. The search for optimal contour is performed by a coarse-to-fine algorithm that iterates between combinatorial search and gradient-based local optimization. First, multi-solution dynamic programming (MSDP) is used to generate initial candidates by minimizing only the image energy. In the second step, a combination of image energy and shape energy is minimized starting from these initial candidates using a local optimization method and the best one is selected. To generate diverse initial candidates while reducing invalid shapes, we apply two pruning methods to the search space of MSDP. Our search strategy combines the advantages of global combinatorial search and local optimization, and has shown excellent robustness to local minima caused by distracting suboptimal solutions. Experimental results on segmentation of different anatomical structures using ShRAC, as well as preliminary results on human silhouette segmentation are provided.     

A robust object category detection system using deformable shapes

An object can often be uniquely identified by its shape, which is usually fairly invariant. However, when the search is for a type of object or an object category, there can be variations in object deformation (i.e. variations in body shapes) and articulation (i.e. joint movement by limbs) that complicate their detection. We present a system that can account for this articulation variation to improve the robustness of its object detection by using deformable shapes as its main search criteria. However, existing search techniques based on deformable shapes suffer from slow search times and poor best matches when images are cluttered and the search is not initialised. To overcome these drawbacks, our object detection system uses flexible shape templates that are augmented by salient object features and user-defined heuristics. Our approach reduces computation time by prioritising the search around these salient features and uses the template heuristics to find truer positive matches.

Deformation similarity measurement in quasi-conformal shape space

This paper presents a novel approach based on the shape space concept to classify deformations of 3D models. A new quasi-conformal metric is introduced which measures the curvature changes at each vertex of each pose during the deformation. The shapes with similar deformation patterns follow a similar deformation curve in shape space. Energy functional of the deformation curve is minimized to calculate the geodesic curve connecting two shapes on the shape space manifold. The geodesic distance illustrates the similarity between two shapes, which is used to compute the similarity between the deformations. We applied our method to classify the left ventricle deformations of myopathic and control subjects, and the sensitivity and specificity of our method were 88.8% and 85.7%, which are higher than other methods based on the left ventricle cavity, which shows our method can quantify the similarity and disparity of the left ventricle motion well.     

一种鲁棒的二维图像形状检索方法

以往的基于极坐标变换的轮廓描述方法都是以形状质心作为参考点,使得其对噪声过于敏感,轮廓边界发生轻微的变化就可能导致形状匹配的失败。为此,文中提出一种用于描述和匹配二维图像形状的鲁棒性方法。该方法以广义霍夫变换参数映射的极值点作为待检索图像的参考点,以此来建立对应的极坐标变换轮廓描述曲线,从而通过计算描述曲线间所包围的累积误差面积来实现图像匹配。在3个图像库上的检索实验表明文中方法在具有平移、尺度、旋转不变性的同时还具有较好的鲁棒性,可有效对存在部分遮挡和局部缺损的图像进行检索。     

离散形状的特征区域匹配识别

本文用形心到外轮廓的距离序列表示形状,从而将二维的形状分析问题转化成较简单的 一维波形曲线的分析问题,对于这种波形曲线的识别,提出了一种特征区域的局部匹配识别 算法.实验证明,本文方法具有较高的识别率,且该算法是平移、旋转和比例变化不变的.     

Notes on shape orientation where the standard method does not work

In this paper we consider some questions related to the orientation of shapes with particular attention to the situation where the standard method does not work. There are irregular and non-symmetric shapes whose orientation cannot be computed in a standard way, but in the literature the most studied situations are those where the shape under consideration has more than two axes of symmetry or where it is an n-fold rotationally symmetric shape with n>2. The basic reference for our work is [W.H. Tsai, S.L. Chou, Detection of generalized principal in rotationally symmetric shapes, Pattern Recognition 24 (1991) 95-104]. We give a very simple proof of the main result from [W.H. Tsai, S.L. Chou, Detection of generalized principal in rotationally symmetric shapes, Pattern Recognition 24 (1991) 95-104] and suggest a modification of the proposal on how the principal axes of rotationally symmetric shapes should be computed. We show some desirable property in defining the orientation of such shapes if the modified approach is applied. Also, we give some comments on the problems that arise when computing shape elongation.     

Efficient polygon decomposition into singular and regular regions via Voronoi diagrams

A new polygon decomposition into regular and singular regions is defined; it is a concept that is useful for skeleton extraction and part analysis of elongated shapes. Polygon regions that are narrow according to the Voronoi diagram of the polygon are extended through the boundary that is adjacent and quasiparallel. Regular regions are the narrow ones surrounded by smooth quasiparallel contour segments, while singular regions are the polygon regions that are not regular. We present an efficient algorithm to calculate the decomposition and make a comparative study with previous algorithms.Received: 21 December 2001, Accepted: 10 January 2003, Published online: 6 June 2003     

Polygonal representation of digital planar curves through dominant point detection—a nonparametric algorithm

We describe a new algorithm that detects a set of feature points on the boundary of an 8-connected shape that constitute the vertices of a polygonal approximation of the shape itself. The set of feature points (nodes) is a ranked subset of the original shape points whose connected left and right arm extents cover the entire shape. Nodes are ranked based on their strength (in terms of their importance to other boundary points), length of support region, and distance from the centroid. The polygon obtained by linking the detected nodes approximates the contour in an intuitive way. The proposed algorithm does not require an input parameter and works well for shapes with features of multiple sizes.