CAJ | 학술논문

形状分析是计算机视觉领域的经典问题,目前已有大量关于形状分类问题的研究.但是,当处理大的非线性失真、特别是结构上或者关联上的失真时,许多形状分类方法往往无能为力.提出一种利用轮廓关键点集(contour critical point sets,CCPS)进行形状分类的新方法.轮廓关键点的特征用其inner-distance形状上下文(IDSC)表征.关键点的inner-distance形状上下文不仅表征形状的局部特征,也反映其全局特征,这种局部点的全局特征信息对遮挡、非线性失真等有良好的鲁棒性.巧妙地构造关键点的特征向量后,对形状轮廓关键点集、形状类、和全体形状样本建模,进行三级的贝叶斯分类.形状类模型使得可以利用同一类中的不同样本的不同关键点对输入形状进行识别.实验结果表明,这种基于视觉部分的全局特征,三级的贝叶斯分类方法对非线性失真、类内变异、结构变化、遮挡等具有良好的鲁棒性.文中的方法在Kimia形状数据库上达到100%的分类精度,并且分类所有108个测试形状仅需要8 s,是目前已知最好的分类性能.在广泛使用的MPEG-7形状数据库上,也能达到满意的分类结果.
형상분석시계산궤시각영역적경전문제,목전이유대량관우형상분류문제적연구.단시,당처리대적비선성실진、특별시결구상혹자관련상적실진시,허다형상분류방법왕왕무능위력.제출일충이용륜곽관건점집(contour critical point sets,CCPS)진행형상분류적신방법.륜곽관건점적특정용기inner-distance형상상하문(IDSC)표정.관건점적inner-distance형상상하문불부표정형상적국부특정,야반영기전국특정,저충국부점적전국특정신식대차당、비선성실진등유량호적로봉성.교묘지구조관건점적특정향량후,대형상륜곽관건점집、형상류、화전체형상양본건모,진행삼급적패협사분류.형상류모형사득가이이용동일류중적불동양본적불동관건점대수입형상진행식별.실험결과표명,저충기우시각부분적전국특정,삼급적패협사분류방법대비선성실진、류내변이、결구변화、차당등구유량호적로봉성.문중적방법재Kimia형상수거고상체도100%적분류정도,병차분류소유108개측시형상부수요8 s,시목전이지최호적분류성능.재엄범사용적MPEG-7형상수거고상,야능체도만의적분류결과.
Shape analysis has been one of the most studied topics in computer vision. One major task in shape analysis is to study the underlying statistics of shape population and use the information to extract, recognize, and understand physical structures and biological objects. Matching based algorithms perform classification, essentially through exemplar based or nearest neighborhood approach by matching the query shape against all those in the training set. On few training samples, these algorithms are hard to capture the large intra-class variation. On large training samples, it is extremely time consuming to perform shape matching one-by-one. Approaches based on generative models require a large number of parameters, which renders them significantly more expensive computationally, and also increases the possibility of converging to non-optimal local minima. Furthermore, existing Matching based and model-based approaches cannot handle object classes that have different parts or numbers of parts without splitting the class into separate subclasses. Most of the methods for shape classification are based on contour and many researchers have worked on the general shape classification problem. However, approaches for classifying contour shapes can encounter difficulties when dealing with classes that have large nonlinear variability9 especially when the variability is structural or due to articulation. A novel method, using contour critical point sets (CCPS) to perform shape classification task, is proposed in this paper. First, inner-distance shape context (IDSC) is used to characterize the critical points. Of course, other features of the critical points may instead of IDSC. Shapes are represented by a set of points sampled from the shape contours and the shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. It is articulation insensitive and more effective at capturing part structures than thc Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. Humans perception of shape is based on similarity of common parts, to the extent that a single, significant visual part is sufficient to recognize the whole object and part-based representations allow for recognition that is robust in the presence of occlusion, movement, deletion, or growth of portions of an object. It is a simple and natural observation that maximal convex or concave parts of objects determine visual parts. So the contour critical point sets (CCPS) of shapes is utilized to perform shape classification task. The IDSC of critical point is an excellent feature of contour point, which not only contains local features but also the global information. After design the smart feature of shapes, then, Bayesian classification is performed within a three-level framework which consists of models for contour critical point sets, for classes, and for the entire database of training examples. The class model enables different critical points of different exemplars of one class to contribute to the recognition of an input shape. This new method achieves 100% classification accuracy on Kimia database. Furthermore, to classify all 108 test shapes only need 8 seconds, which is the best performance ever reported in the literature. The results on the well-known MPEG7 CE-Shape-1 data set also prove its superiority.