Learning shape classes

This paper is a summary of a new approach to learning shape concepts. In this system, a shape is represented by conjunctions of local shape properties. Conjunctions of local properties are consistent and unique for distinct shapes and are robust enough to represent shape in the presence of occlusion. A new learning method, called property based learning, is developed and used to learn conjunctions of local properties. Unlike other classification methods based on distances or similarities, classification performance does not degrade linearly as the number of classes increases and classification can be done correctly with only partial information of instances. Property based learning is an incremental learning method that selects properties crucial for classification. Two experiments are reported. In the first experiment with tool shapes, this shape learning system is used to classify shapes in the presence of view point changes, local movements such as moving handles of pliers, and occlusion. In the second experiment with hand gestures, the system can classify different gestures regardless of the movement in joints, fingers, and palms     

Combining magnitude and shape features for hyperspectral classification

The spectral angle mapper (SAM) and maximum likelihood classification (MLC) are two traditional classifiers for hyperspectral classification. This paper presents two methods to combine magnitude and shape features, one for each classifier. As the magnitude and shape features are complementary, combining both features can improve the classification accuracy. First, magnitude features are represented by the spectral radiance vector, whereas shape features are represented by the spectral gradient vector. Then, in SAM, each feature vector generates a spectral angle for each class. The two generated angles are added together to obtain a single similarity, which is used for the final classification. Similarly, in MLC, after the dimensionality reduction using Fisher's linear discriminant (FLD), each feature vector in the new feature space generates a likelihood. The two generated likelihoods are multiplied to obtain a single value, which is adopted for the final classification. Experimental results on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set demonstrate that the proposed methods outperform the methods with a single feature set.     

二维形状特征描述及分类识别研究进展综述

二维形状分类识别是计算机视觉和模式识别等领域的一个重要问题，在目标识别、图像理解等应用中发挥着重要作用。针对二维形状分类识别研究，主要从特征描述、形状分类识别、形状标准数据库三个方面综述了该方向近年来最新的研究工作。综合分析了二维形状特征表示方法，主要包括基于轮廓的、基于区域的、基于骨架的以及基于多特征融合的方法，并简要评述；介绍并分析了二维形状分类识别方法，主要包括传统机器学习分类器、集成分类器、深度学习方法等；概述了二维形状识别中常用的标准数据库；展望了二维形状识别分类研究的发展趋势。     

结合LDA主题模型的植物叶片形状描述及分类*

针对传统形状描述算子多侧重于叶片形状再表达,对叶片形状随机特性刻画不足的缺点,结合潜在狄利克雷分布(LDA)主题模型建立有效的植物叶片形状描述算子,并据此构建叶片形状识别分类框架.首先建立叶片形状的多尺度词包模型,将形状空间联系引入形状生成模型.然后结合LDA建立叶片形状生成模型,提取形状分布参数作为叶片形状描述算子.最后使用K近邻进行叶片分类.实验表明,在异类叶片类间形状差异较小的复杂情况下,相比傅里叶、形状上下文等传统算子,结合LDA主题模型的植物叶片形状描述算子的叶片形状识别精度更高.     

结合纹理与形状的Tagged MR图像左心室分割算法

分割带标记线核磁共振(tagged MR)图像是左心室运动重建的前提.由于标记线的加载破坏了左心室的轮廓边缘和区域灰度一致性,再加上乳突肌的存在,使带标记线核磁共振图像的左心室内外轮廓分割变得相当困难.在变分框架下,将纹理分类信息与形状统计先验知识引入Mumford-Shah模型中,提出了一种改进的分割带标记线核磁共振图像的左心室内外轮廓的方法.该方法基于支持向量机对S滤波器组提取的纹理特征的分类结果,构造了一种新的图像能量表示;针对乳突肌及边缘断裂现象,引入形状统计先验信息来约束曲线的演化.因为分割过程利用了有监督学习策略,较好地克服了标记线对左心室区域灰度的影响,提高了分割精度.实验结果表明,该方法较以往方法具有更高的分割精度和更好的稳定性.     

基于形状特征的字母手势的分类及识别算法

提出了一种基于形状特征的字母手势的分类及识别算法。根据手势图像中手指的方向及数目进行粗分类，然后在边界图像及二值图像中提取手势的形状特征向量，进行基于类似度的模板匹配，实现对字母手势的细分类。实验证明，基于形状特征的粗分类能够排除完全不匹配的手势，减少了识别过程中的匹配搜索时间，提高了识别率。     

Local shape approximation from shading

Shading can be used as an independent cue for exact shape recovery, or it can be used as a supplementary cue for shape interpolation between features whose depths are known from other cues. Exact shape cannot be inferred from a local analysis of shading. However, for shape interpolation a crude local approximation may be sufficient. This paper explores the limits of such local approximations that are easy to compute. In particular, the shape of shading is used to approximate the surface in areas of monotonic change of intensity. This analysis is accompanied by a method for computing the direction of a single-point light source from the shading on occluding contours. A qualitative classification of shape near shading singularities is also discussed.This work was performed at the Massachusetts Institute of Technology, Center for Biological Information Processing.     

A Survey on Shape Correspondence

We review methods designed to compute correspondences between geometric shapes represented by triangle meshes, contours or point sets. This survey is motivated in part by recent developments in space–time registration, where one seeks a correspondence between non‐rigid and time‐varying surfaces, and semantic shape analysis, which underlines a recent trend to incorporate shape understanding into the analysis pipeline. Establishing a meaningful correspondence between shapes is often difficult because it generally requires an understanding of the structure of the shapes at both the local and global levels, and sometimes the functionality of the shape parts as well. Despite its inherent complexity, shape correspondence is a recurrent problem and an essential component of numerous geometry processing applications. In this survey, we discuss the different forms of the correspondence problem and review the main solution methods, aided by several classification criteria arising from the problem definition. The main categories of classification are defined in terms of the input and output representation, objective function and solution approach. We conclude the survey by discussing open problems and future perspectives.     

基于边缘特征的水下目标分类识别算法

在水下声纳图像目标检测与识别技术中,水雷等目标的识别问题是关键技术之一.基于形状相似度的概念,利用目标边缘轮廓特征,给出一种基于距离多集方法的水雷目标形状分类算法.首先对检测到的水雷目标阴影区域进行规格化处理,采用距离多集方法对常见水雷形状进行分类识别,提出了一种模板更新策略.通过对球形、圆柱形和圆台形三类目标的阴影区域进行仿真分类计算可知,该方法具有仿射不变性,分类准确率较高.     

基于形状不变矩的t-混合模型的图像分类

提出一种基于物体形状不变矩特征,采用t-混合模型计算后验概率的统计形状分类的方法:采用Hu形状不变矩作为图像特征,用t-混合模型的EM迭代算法,估计出模型中未知参数,计算出形状类别的后验概率。实验结果表明,物体序列的形状不变矩具有良好的统计分布性质,按其后验概率分类的准确性较高。     

Object‐oriented classification of sidescan sonar data for mapping benthic marine habitats

This research presents an object‐oriented technique for habitat classification at different segmentation levels based on the use of imagery from an Edgetech 272 side scan sonar. We investigate the success of object parameters such as shape and size as well as texture in discriminating reef from sand habitat. The results are evaluated using traditional digitization, based on visual assessment of the sidescan imagery, and video transects. Whereas the application of traditional pixel‐based classification results in a pixelized (salt and pepper) representation of habitat distribution, the object‐based classification technique results in habitat objects (raster or vector). The object‐oriented classification results are cross‐validated using confusion matrices in image classification software and error matrices from underwater video transects showing an overall accuracy of 80% based on two classes within the image at three segmentation levels and an overall accuracy of 60% based on three classes at two segmentation levels. This is compared with the digitized layer accuracy of 81% for two classes and 72% for three classes, and this demonstrates the successful application of object‐oriented methods for habitat mapping. This technique retains spatially discrete habitat pattern information in a classified vector shape file with methods that are automated, repeatable, objective, and capable of processing many sidescan records in a more efficient manner.     

ASL Recognition by the Layered Learning Model Using Clustered Groups

American Sign Language (ASL) images can be used as a communication tool by determining numbers and letters using the shape of the fingers. Particularly, ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons, because sign language is their only channel of expression. Representative ASL recognition methods primarily adopt images, sensors, and pose-based recognition techniques, and employ various gestures together with hand-shapes. This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers. In the proposed model, the collected ASL images were clustered based on similarities in shape, and clustered group classification was first performed, followed by reclassification within the group. The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition. After selecting the optimized group, we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing. The proposed model exhibited an improved performance compared with the general classification model.     

基于形状轮廓特征的金字塔匹配算法

形状检索在计算机视觉中一直是一个具有挑战性的问题，其中对形状特征直方图距离的测量是评价形状检索算法优劣的一个重要因素。针对轮廓特征的直方图距离测量，算法引进一种在图像分类领域中应用广泛的金字塔匹配算法。不同于其他传统的直方图度量算法，金字塔匹配算法将形状的轮廓分成若干块，给每一块分配相应的权重，然后分别统计块中的特征，再计算特征的加权和进行相似度的测量。通过在不同形状数据集下实验，该方法能够有效地进行形状匹配和检索，且能得到较好的形状匹配精度。     

Hybrid genetic optimization and statistical model based approachfor the classification of shadow shapes in sonar imagery

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process: 1) the detection problem of a region of interest in the input image is stated as the minimization of a cost function; and 2) the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique and two stochastic relaxation methods: simulated annealing and a hybrid genetic algorithm. This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results     

Structural Shape Indexing with Feature Generation Models

Structural indexing is a potential approach to efficient classification and retrieval of image patterns with respect to a very large number of models. This technique is based on the idea of distributing features associated with model identifiers over a large data structure prepared for a model set, along with classification by voting for models with reference to the extracted features. Essential problems caused by mapping image features to discrete indices are that indexing is sensitive to noise, scales of observation, and local shape deformations, and thata prioriknowledge and feature distributions of corrupted instances are not available for each class when a large number of training data are not presented. To cope with these problems, shape feature generation techniques are incorporated into structural indexing. An analysis of feature transformations is carried out for some particular types of shape deformations, leading to feature generation rules composed of a small number of distinct cases. The rules are exploited to generate features that can be extracted from deformed patterns caused by noise and local shape deformations. In both processes of model database organization and classification, the generated features by the transformation rules are used for structural indexing and voting, as well as the features actually extracted from contours. The effectiveness of the proposed method is demonstrated by experimental trials with a large number of sample data. Furthermore, its application to shape retrieval from image databases is mentioned. The shape feature generation significantly improves the classification accuracy and efficiency.     

基于骨架接合节点特征的形状识别与分类

介绍了一种利用形状的骨架信息进行形状识别和分类的方法。利用从形状中提取的骨架接合节点信息,将形状相似性问题转化为骨架接合节点距离的计算。采用神经网络的方法,根据输入形状的骨架接合节点距离判断出形状所属类别。该方法可以很好地处理非刚性物体形状识别问题,与现有方法相比,具有更好的形状识别效果。     

基于傅里叶变换的形状上下文描述方法

提出了一种新的基于傅里叶变换的形状上下文描述方法,与以前的形状上下文描述子相比,增加了天然的旋转不变性,同时描述子本身也更加简洁.该傅里叶形状描述子,将全局采样点的分布信息有机地结合到了每个采样点的特征描述中去,从而使代表不同形状的采样点集可以得到准确的鉴别匹配.进一步考虑点集之间仿射变换的代价,从而可以很好地运用到二维形状分类识别问题中去.该方法满足平移、缩放、旋转三个不变性,在形状有遮挡、缺损的情况下都可以取得较好的容错识别效果.