结合轮廓与形状特征的仿射形状匹配

目的 针对仿射变换下形状匹配中存在的描述子对形状的描述能力不足,以及描述子计算耗时大的问题,改进基于所有图像点投影的方法,提出一种利用轮廓计算投影面积的仿射形状匹配算法。方法 该算法分为粗匹配和精匹配两个阶段。粗匹配阶段以CSS角点作为备选特征点,首先统计轮廓投影面积分布作为特征点描述子;然后利用动态规划蚁群算法匹配两幅图片公共特征点序列,并将匹配好的特征点序列记为对应的新特征点;最后采用该新特征点划分目标曲线,得到对应的轮廓曲线;这一阶段的目的是对形状的筛选以及寻找一致的轮廓特征点,同时完成轮廓曲线的划分。精匹配阶段,采用小波仿射不变描述子,对粗匹配阶段匹配代价最小的5%的目标进行对应曲线匹配,得到精匹配阶段的匹配代价,从而实现对仿射目标的识别;精匹配弥补了描述子对轮廓细节描述不足的问题。结果 算法的平均检索速度比传统基于形状投影分布描述子提高44.3%,在MPEG-7图像库上的检索效果为98.65%,在MPEG-7仿射图像库上的查准率与查全率综合评价指标比传统的基于形状投影分布描述子高3.1%,比形状上下文高25%。结论 本文算法匹配效果好,效率高,抗噪性强,解决了仿射描述子计算速度慢、描述能力不足的问题,能有效地应用于仿射形状匹配与检索领域。     

Object detection by global contour shape

We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets.     

基于轮廓的形状射线描述模型

提出一种新的基于轮廓的形状描述和匹配方法。提取物体的轮廓并在轮廓上进行等间隔采样,利用参考点到采样点的距离、采样点处的轮廓方向及采样点间的空间关系来直观地表达目标的形状特征;通过在不同尺度、方向和位置进行最大表决来获得形状匹配的尺度、旋转和平移不变性;提出了结合局部和整体特征的相似度评分机制来实现目标的匹配和检测。实验表明,形状的射线描述模型不仅能对具有清晰轮廓的目标进行有效的检索和匹配,也可在复杂的图像背景中检测目标。     

Orientation, scale, and discontinuity as emergent properties of illusory contour shape

A recent neural model of illusory contour formation is based on a distribution of natural shapes traced by particles moving with constant speed in directions given by Brownian motions. The input to that model consists of pairs of position and direction constraints, and the output consists of the distribution of contours joining all such pairs. In general, these contours will not be closed, and their distribution will not be scale-invariant. In this article, we show how to compute a scale-invariant distribution of closed contours given position constraints alone and use this result to explain a well-known illusory contour effect.     

Pulse-coupled neural networks for contour and motion matchings

Two neural networks based on temporal coding are proposed in this paper to perform contour and motion matchings. Both of the proposed networks are three-dimensional (3D) pulse-coupled neural networks (PCNNs). They are composed of simplified Eckhorn neurons and mimic the structure of the primary visual cortex. The PCNN for contour matching can segment from the background the object with a particular contour, which has been stored as prior knowledge and controls the network activity in the form of spike series; The PCNN for motion matching not only detects the motion in the visual field, but also extracts the object moving in an arbitrarily specified direction. The basic idea of these two models is to encode information into the timing of spikes and later to decode this information through coincidence detectors and synapse delays to realize the knowledge-controlled object matchings. The simulation results demonstrate that the temporal coding and the decoding mechanisms are powerful enough to perform the contour and motion matchings.     

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.     

An extremum principle for shape from contour

An extremum principle is developed that determines three-dimensional surface orientation from a two-dimensional contour. The principle maximizes the ratio of the area to the square of the perimeter, a measure of the compactness or symmetry of the three-dimensional surface. The principle interprets regular figures correctly and it interprets skew symmetries as oriented real symmetries. The maximum likelihood method approximates the principle on irregular figures, but we show that it consistently overestimates the slant of an ellipse.     

Integration of geometric elements, Euclidean relations, and motion curves for parametric shape and motion estimation

This paper presents an approach to shape and motion estimation that integrates heterogeneous knowledge into a unique model-based framework. We describe the observed scenes in terms of structured geometric elements (points, line segments, rectangles, 3D corners) sharing explicitly Euclidean relationships (orthogonality, parallelism, colinearity, coplanarity). Camera trajectories are represented with adaptative models which account for the regularity of usual camera motions. Two different strategies of automatic model building lead us to reduced models for shape and motion estimation with a minimal number of parameters. These models increase the robustness to noise and occlusions, improve the reconstruction, and provide a high-level representation of the observed scene. The parameters are optimally computed within a sequential Bayesian estimation procedure that gives accurate and reliable results on synthetic and real video imagery.     

基于轮廓矢量化的形状匹配快速算法

针对现有形状匹配算法匹配速度慢、可靠性差的问题,提出了一种基于轮廓矢量化的形状匹配算法。将轮廓曲线点集离散化,使用多组向量对轮廓线性进行逼近。匹配时,结合线段在几何中的匹配方法,通过加权求和,计算源图像与目标图像中每组对应向量的相似度,将它们的平均值作为匹配结果。以焊盘表面轮廓为实验对象,使用所提出的算法进行匹配,结果表明,通过正确选取向量组数能有效避免匹配精度所受的影响,算法对不同类型的焊盘区分性较强,且匹配速度能满足实际应用的需要。     

基于轮廓特征提取的叶形相似性研究

叶片的形态特征多样,它的叶形特征对植物的研究具有重要的意义。为了判断不同叶形之间的轮廓相似性,利用轮廓特征提取,将叶子的叶形存储在一维序列中。结合统计学,引用Pearson相关性进行特征分布的相关系数计算。实验结果显示,不同叶形的相关系数之间有较为显著的差异,测试的叶子叶形判别准确率较高,可作为判断叶形相似性的重要依据。     

Partial shape classification using contour matching in distancetransformation

An algorithm is presented to recognize and locate partially distorted 2D shapes without regard to their orientation, location, and size. The algorithm first calculates the curvature function from the digitized image of an object. The points of local maxima and minima extracted from the smooth curvature are used as control points to segment the boundary and to guide the boundary-matching procedure. The boundary-matching procedure considers two shapes at a time, one shape from the template databank, and the other from the object being classified. The procedure tries to match the control points in the unknown shape to those of a shape from the template databank, and estimates the translation, rotation, and scaling factors to be used to normalize the boundary of the unknown shape. The chamfer 3/4 distance transformation and a partial distance measurement scheme constitute the final step in measuring the similarity between the two shapes. The unknown shape is assigned to the class corresponding to the minimum distance. The algorithm has been successfully tested on partial shapes using two sets of data, one with sharp corners and the other with curve segments. This algorithm not only is computationally simple, but also works reasonably well in the presence of a moderate amount of noise     

An active contour model with shape regulation scheme

This paper presents an active method for locating target objects in images, which is aimed at improving the performance of detecting object boundaries by enhancing the behavioral characteristics of an active contour. The proposed active contour model simulates a mechanical system consisting of two main parts: the first is a rigid fixture, called the 'core', specifying the expected shape of target boundaries, while the second is an elastic rod attached to the rigid fixture. The elastic rod deforms or moves relative to the rigid core according to the classical laws of the mechanical system. When the initial contour is applied to an image data, it is attracted near the dominant image features, but tries to keep its home shape and simultaneously make the deformation smooth if a deformation is more natural for force equilibrium. This mechanism significantly improves the performance of detecting object boundaries in the presence of some disturbing image features. The active contour is scale invariant, thereby significantly relieving the difficulty in selecting proper values for the model parameters. The values for the model parameters can be selected to make the contour have the desired behaviors around the equilibrium position through the analysis of the vibration mode of the mechanical system. The performance of the proposed method is validated through a series of experiments, which include detection of heavily degraded objects, tracking of objects under non-rigid motion and comparisons with the original snake models.     

基于先验分布活动轮廓模型的纹理缺陷检测

在工业产品的表面缺陷检测中,计算机视觉逐渐取代人工视觉,这是工业自动化的重要标志之一.而产品的表面纹理对缺陷检测的干扰一直是个难点.从图像分割的角度出发,以缺陷为目标,将纹理表面作为背景提取产品的表面缺陷.基于非参数统计活动轮廓模型提出一种先验分布模型,即以纹理的灰度分布作为背景的先验信息,使得算法更容易区分纹理背景和缺陷.实验结果表明,所提出的算法适用于不同纹理背景的缺陷检测,能准确地提取缺陷位置.     

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.