Detection and recognition of contour parts based on shape similarity

Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, it is usually impossible to extract complete object contours or to segment the whole objects. However, in many cases parts of contours can be correctly reconstructed either by performing edge grouping or as parts of boundaries of segmented regions. Therefore, recognition of objects based on their contour parts seems to be a promising as well as a necessary research direction.The main contribution of this paper is a system for detection and recognition of contour parts in digital images. Both detection and recognition are based on shape similarity of contour parts. For each contour part produced by contour grouping, we use shape similarity to retrieve the most similar contour parts in a database of known contour segments. A shape-based classification of the retrieved contour parts performs then a simultaneous detection and recognition.An important step in our approach is the construction of the database of known contour segments. First complete contours of known objects are decomposed into parts using discrete curve evolution. Then, their representation is constructed that is invariant to scaling, rotation, and translation.     

Tracking Deformable Templates Using a Shortest Path Algorithm

This paper proposes a new technique to track deformable templates. We extend the typical graph algorithms that have been used for active contour recovery to incorporate shape information. The advantage of graph algorithms is that they are guaranteed to find the global minimum of the energy function. The difficulty with their traditional use for active contours is that they consider only two pixels at a time when recovering the contour, making it impossible to enforce shape constraints. We define the deformable template as a polygonal contour, demonstrate the proper mapping between the image, the contour, and a graph, and show how to apply Dijkstra's algorithm to track contours in image sequences. Examples are shown for deforming contours, articulated objects, and smooth contours being tracked in simple and complicated backgrounds. We also provide an analysis of the computational requirements.     

基于活动轮廓模型的人脸特征提取方法的研究

人脸面部特征提取是自动视觉翻译和人脸识别中的最关键的技术之一。本文对活动轮廓模型理论的基本原理、方法和技术等作了有益的探索和尝试,并在此基础上,提出了提取人脸面部特征的系列算法,特征是精确的轮廓描述而非简单的数字表达,实验中,我们用动态模板方法提取眼睛轮廓,用活动轮廓方法提取眉毛形状,结果证明,该方法具有较强的鲁棒性和自适应性。     

基于轮廓特征的人体行为识别

提出了一种以人的动作序列图像的轮廓为特征、基于隐条件随机场的行为识别方法。首先,利用背景差分法 和阴影消除技术提取运动人体轮廓。星型骨架方法只采用单一质心一边界距离来描述人体轮廓,因此对轮廓的局部特 征描述能力较弱。定义一种新的基于距离组的轮廓描述方法,它将时变的2D轮廓形状转换为对应的1D距离向量。 最后利用判别隐条件随机场对行为进行训练和识别。实验结果表明,本方法的正确识别率达到91. 4%以上,识别结 果较为理想。     

一种改进的围线追踪算法

论文采用边过程的思想,提出了一种基于目标区域边界像素表示的围线追踪算法。该算法能够追踪形状任意复杂区域的围线,包括单像素宽及含有空洞的区域,并且能够得到围线间的包含关系。实验结果表明该算法比现有追踪算法的读点数更少,性能更高。     

轮廓矩不变量及其在物体形状识别中的应用

为了有效地刻画物体的形状特征，在基于区域的Hu矩不变量的基础上，构造了一种基于物体轮廓曲线的新的矩不变量，即轮廓矩不变量。该不变量不仅独立于物体本身的颜色和灰度级，而且具有平移、旋转和尺度不变性，因此可将轮廓矩不变量应用于物体形状的识别，为了能快速地进行物体形状识别，还讨论了小波边缘检测和轮廓的获取问题及其算法。实验表明，基于这种轮廓矩的识别算法具有很好的识别率。     

On recognizing and positioning curved 3-D objects from imagecontours

An approach for explicitly relating the shape of image contours to models of curved three-dimensional objects is presented. This relationship is used for object recognition and positioning. Object models consist of collections of parametric surface patches and their intersection curves; this includes nearly all representations used in computer-aided geometric design and computer vision. The image contours considered are the projections of surface discontinuities and occluding contours. Elimination theory provides a method for constructing the implicit equation of these contours for an object observed under orthographic or perspective projection. This equation is parameterized by the object's position and orientation with respect to the observer. Determining these parameters is reduced to a fitting problem between the theoretical contour and the observed data points. The proposed approach readily extends to parameterized models. It has been implemented for a simple world composed of various surfaces of revolution and tested on several real images     

引入形状统计先验的轮廓提取的概率方法

提出了轮廓提取的概率方法,同时将物体形状的统计先验信息和轮廓线的平滑性结合到轮廓的概率估计中．首先应用主元分析对低维空间概率采样生成高维形状统计分布的样本,根据形状样本得到轮廓控制点样本,而后结合描述平滑性的先验概率,运用序列蒙特卡罗方法实现轮廓估计．对实际拍摄的人手图像的实验表明,针对特定的形状,该方法在杂乱背景、遮挡和强噪声干扰情况下依然能够得到满意的物体轮廓线．     

Extracting eyebrow contour and chin contour for face recognition

This paper proposes a novel method for extraction of eyebrow contour and chin contour. We first segment rough eyebrow regions using spatial constrained sub-area K-means clustering. Then eyebrow contours are extracted by Snake method with effective image force. For chin contour extraction, we first estimate several possible chin locations which are used to build a number of curves as chin contour candidates. Based on the chin like edges extracted by proposed chin edge detector, the curve with the largest likeliness to be the actual chin contour is selected. Finally, the credible extracted eyebrow contour and the estimated chin contours are used as geometric features for face recognition. Experimental results show that the proposed algorithms can extract eyebrow contours and chin contours with good accuracy and the extracted features are effective for improving face recognition rates.     

Representation and self-similarity of shapes

Representing shapes in a compact and informative form is a significant problem for vision systems that must recognize or classify objects. We describe a compact representation model for two-dimensional (2D) shapes by investigating their self-similarities and constructing their shape axis trees (SA-trees). Our approach can be formulated as a variational one (or, equivalently, as MAP estimation of a Markov random field). We start with a 2D shape, its boundary contour, and two different parameterizations for the contour (one parameterization is oriented counterclockwise and the other clockwise). To measure its self-similarity, the two parameterizations are matched to derive the best set of one-to-one point-to-point correspondences along the contour. The cost functional used in the matching may vary and is determined by the adopted self-similarity criteria, e.g., cocircularity, distance variation, parallelism, and region homogeneity. The loci of middle points of the pairing contour points yield the shape axis and they can be grouped into a unique free tree structure, the SA-tree. By implicitly encoding the (local and global) shape information into an SA-tree, a variety of vision tasks, e.g., shape recognition, comparison, and retrieval, can be performed in a more robust and efficient way via various tree-based algorithms. A dynamic programming algorithm gives the optimal solution in O(N/sup 1/), where N is the size of the contour.     

Tracking deforming objects using particle filtering for geometric active contours

Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time. Tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape, but these are dependent on the chosen parametrization and cannot handle changes in curve topology. Geometric active contours provide a framework which is parametrization independent and allow for changes in topology, in the present work, we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects. To the best of our knowledge, this is the first attempt to implement an approximate particle filtering algorithm for tracking on a (theoretically) infinite dimensional state space.     

基于超声导波SC-DTW的金属板微损检测方法

针对金属板材的质量控制与服役性能评估问题,在对金属板材微损伤进行超声导波检测的基础上,结合形状上下文(shape context, SC)与动态时间规整方法对其损伤劣化程度进行量化评估.该方法以无损Lamb波信号为基准,采用动态时间规划(dynamic time warping, DWT)算法对损伤信号进行相似匹配分析对比,确定基准信号与损伤信号的最佳匹配路径.引入SC的轮廓识别方法对Lamb波的局部波形信息进行统计分析,以波形形状距离代替传统欧氏距离匹配方法,解决DTW相似匹配中的病态对齐问题.最后,将无损Lamb波与损伤Lamb波信号间SC-DTW匹配距离作为损伤程度的量化指标,采用随机闭合裂纹的有限元仿真模型和铝板弯折实验对所提出方法进行验证,结果表明,基于Lamb波SC-DTW的损伤量化指数对铝板早期裂纹具有较高的敏感性和较好的量化表征能力.该方法无需对波包进行识别,也不必进行复杂的特征提取,具有简单高效和抗噪声能力强等优点,在金属板服役性能评估与质量控制中有较好的实用性和推广价值.     

融合颜色和增量形状先验的目标轮廓跟踪

基于主动轮廓的跟踪方法被广泛应用于移动摄像机下运动物体轮廓的跟踪. 针对传统方法容易受噪音、部分遮挡、背景干扰等因素影响的缺点, 提出了一个分层的基于水平集(Level sets)的跟踪框架. 该框架将颜色信息和形状先验有效地结合起来. 在框架的第一层, 初始轮廓首先根据颜色信息进化, 通过引入一个反映邻域像素之间关系的惩罚因子来改进传统的速度模型. 然后, 基于Mahalanobis距离的判别式被用来决定是否需要引入形状先验, 如果不需要, 则第一层基于颜色进化的结果就作为最终的跟踪结果; 否则, 第一层得到的轮廓需要在第二层中在形状先验的约束下继续进化. 在第二层轮廓进化中, 本文提出了一个权重形状距离因子(Weighted shape distance term, WSDT), 用来融合全局的形状信息和局部的颜色信息. 形状先验模型建立在主成分分析(Principal component analysis, PCA)子空间并通过增量学习算法在线更新. 实验结果证明了方法的有效性和鲁棒性.     

Adaptive locally affine-invariant shape matching

Matching deformable objects using their shapes are an important problem in computer vision since shape is perhaps the most distinguishable characteristic of an object. The problem is difficult due to many factors such as intra-class variations, local deformations, articulations, viewpoint changes and missed and extraneous contour portions due to errors in shape extraction. While small local deformations have been handled in the literature by allowing some leeway in the matching of individual contour points via methods such as Chamfer distance and Hausdorff distance, handling more severe deformations and articulations has been done by applying local geometric corrections such as similarity or affine. However, determining which portions of the shape should be used for the geometric corrections is very hard, although some methods have been tried. In this paper, we address this problem by an efficient search for the group of contour segments to be clustered together for a geometric correction using dynamic programming by essentially searching for the segmentations of two shapes that lead to the best matching between them. At the same time, we allow portions of the contours to remain unmatched to handle missing and extraneous contour portions. Experiments indicate that our method outperforms other algorithms, especially when the shapes to be matched are more complex.     

Silhouette-based isolated object recognition through curvaturescale space

A complete, fast and practical isolated object recognition system has been developed which is very robust with respect to scale, position and orientation changes of the objects as well as noise and local deformations of shape (due to perspective projection, segmentation errors and non-rigid material used in some objects). The system has been tested on a wide variety of three-dimensional objects with different shapes and material and surface properties. A light-box setup is used to obtain silhouette images which are segmented to obtain the physical boundaries of the objects which are classified as either convex or concave. Convex curves are recognized using their four high-scale curvature extrema points. Curvature scale space (CSS) representations are computed for concave curves. The CSS representation is a multi-scale organization of the natural, invariant features of a curve (curvature zero-crossings or extrema) and useful for very reliable recognition of the correct model since it places no constraints on the shape of objects. A three-stage, coarse-to-fine matching algorithm prunes the search space in stage one by applying the CSS aspect ratio test. The maxima of contours in CSS representations of the surviving models are used for fast CSS matching in stage two. Finally, stage three verifies the best match and resolves any ambiguities by determining the distance between the image and model curves. Transformation parameter optimization is then used to find the best fit of the input object to the correct model     

A shape prior constraint for implicit active contours

We present a shape prior constraint to guide the evolution of implicit active contours. Our method includes three core techniques. Firstly, a rigid registration is introduced, using a line search method within a level set framework. The method automatically finds the time step for the iterative optimization processes. The order for finding the optimal translation, rotation and scale is derived experimentally. Secondly, a single reconstructed shape is created from a shape distribution of a previously acquired learning set. The reconstructed shape is applied to guide the active contour evolution. Thirdly, our method balances the impact of the shape prior versus the image guidance of the active contour. A mixed stopping condition is defined based on the stationarity of the evolving curve and the shape prior constraint. Our method is completely non-parametric and avoids taking linear combinations of non-linear signed distance functions, which would cause problems because distance functions are not closed under linear operations. Experimental results show that our method is able to extract the desired objects in several circumstances, namely when noise is present in the image, when the objects are in slightly different poses and when parts of the object are invisible in the image.