Surface construction from planar contours

Many applications of computer graphics involve the display of a three-dimensional solid reconstructed from a sequence of two-dimensional planar contours. Surface construction algorithms accomplish this by mapping individual pairs of contours, forming triangular surface patches, that approximate the original three-dimensional solid. In this paper, we present an expanded algorithm that not only handles the mappings of multiple contours per plane and partial contour mappings, but also allows human interaction to resolve mapping problems. We include a discussion of our algorithm's limitations and the proposed solutions to those limitations.     

Surface shape from the deformation of apparent contours

The spatiotemporal analysis of deforming silhouettes (apparent contours) is here extended using the mathematics of perspective projections and tools from differential geometry. Analysis of the image motion of a silhouette or apparent contour enables computation of local surface curvature along the corresponding contour generator on the surface, assuming viewer motion is known. To perform the analysis, a spatiotemporal parameterization of image-curve motion is needed, but is underconstrained (a manifestation of the well-known aperture problem). It is shown that an epipolar parameterization is most naturally matched to the recovery of surface curvature.One immediate facility afforded by the analysis is that surface patches can be reconstructed in the vicinity of contour generators. Once surface curvature is known, it is possible to discriminate extremal contours from other fixed curves in space. Furthermore, the known robustness of parallax as a cue to depth extends to the case of surface curvature. Its derivative—rate of parallax—is shown theoretically to be a curvature cue that is robust to uncertainties in the known viewer motion. This robustness has been confirmed in experiments.Finally, the power of the new analysis for robotics applications is demonstrated. Illustrations are given of an Adept robot, equipped with a CCD camera, circumnavigating curved obstacles. When further equipped with a suction gripper the robot manipulator can pick up an object by its curved surface, under visual guidance.     

Fast classification of discrete shape contours

A fast and novel nonoverlapping planar shape recognition scheme with efficiency virtually independent of the number of prototypes is developed. It is composed of new contour normalization, control point extraction and discriminant analysis algorithms also presented here. The system thus applies to shapes regardless of their position, size and orientation. A related shape inspection scheme for detecting locations of shape flaws based on similar principles is also demonstrated.     

Learning visual models from shape contours using multiscaleconvex/concave structure matching

A novel approach is proposed for learning a visual model from real shape samples of the same class. The approach can directly acquire a visual model by generalizing the multiscale convex/concave structure of a class of shapes, that is, the approach is based on the concept that shape generalization is shape simplification wherein perceptually relevant features are retained. The simplification does not mean the approximation of shapes but rather the extraction of the optimum scale convex/concave structure common to shape samples of the class. The common structure is obtained by applying the multiscale convex/concave structure-matching method to all shape pairs among given shape samples of the class and by integrating the matching results. The matching method, is applicable to heavily deformed shapes and is effectively implemented with dynamic programming techniques. The approach can acquire a visual model from a few samples without any a priori knowledge of the class. The obtained model is very useful for shape recognition. Results of applying the proposed method are presented     

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.     

Independent shape component-based human activity recognition via Hidden Markov Model

In proactive computing, human activity recognition from image sequences is an active research area. In this paper, a novel human activity recognition method is proposed, which utilizes Independent Component Analysis (ICA) for activity shape information extraction from image sequences and Hidden Markov Model (HMM) for recognition. Various human activities are represented by shape feature vectors from the sequence of activity shape images via ICA. Based on these features, each HMM is trained and activity recognition is achieved by the trained HMMs of different activities. Our recognition performance has been compared to the conventional method where Principal Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with the proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method. Furthermore, by employing Linear Discriminant Analysis (LDA) on IC features, the recognition results further improved significantly in the recognition performance.     

Discrete contours in multiple views: approximation and recognition

Recognition of discrete planar contours under similarity transformations has received a lot of attention but little work has been reported on recognizing them under more general transformations. Planar object boundaries undergo projective or affine transformations across multiple views. We present two methods to recognize discrete curves in this paper. The first method computes a piecewise parametric approximation of the discrete curve that is projectively invariant. A polygon approximation scheme and a piecewise conic approximation scheme are presented here. The second method computes an invariant sequence directly from the sequence of discrete points on the curve in a Fourier transform space. The sequence is shown to be identical up to a scale factor in all affine related views of the curve. We present the theory and demonstrate its applications to several problems including numeral recognition, aircraft recognition, and homography computation.     

A novel application of self-organizing network for facial expression recognition from radial encoded contours

We propose an efficient algorithm for recognizing facial expressions using biologically plausible features: contours of face and its components with radial encoding strategy. A self-organizing network (SON) is applied to check the homogeneity of the encoded contours and then different classifiers, such as SON, multi-layer perceptron and K-nearest neighbor, are used for recognizing expressions from contours. Experimental results show that the recognition accuracy of our algorithm is comparable to that of other algorithms in the literature on the Japanese female facial expression database. We also apply our algorithm to Taiwanese facial expression image database to demonstrate its efficiency in recognizing facial expressions.     

Pattern recognition and categorical shape theory

This article sets out to interpret the simpler elements of categorical shape theory in terms of some of the basic theoretical problems of pattern recognition.     

形状的全尺度可视化表示与识别

目的视觉目标的形状特征表示和识别是图像领域中的重要问题。在实际应用中,视角、形变、遮挡和噪声等干扰因素造成识别精度较低,且大数据场景需要算法具有较高的学习效率。针对这些问题,本文提出一种全尺度可视化形状表示方法。方法在尺度空间的所有尺度上对形状轮廓提取形状的不变量特征,获得形状的全尺度特征。将获得的全部特征紧凑地表示为单幅彩色图像,得到形状特征的可视化表示。将表示形状特征的彩色图像输入双路卷积网络模型,完成形状分类和检索任务。结果通过对原始形状加入旋转、遮挡和噪声等不同干扰的定性实验,验证了本文方法具有旋转和缩放不变性,以及对铰接变换、遮挡和噪声等干扰的鲁棒性。在通用数据集上进行形状分类和形状检索的定量实验,所得准确率在不同数据集上均超过对比算法。在MPEG-7数据集上精度达到99.57%,对比算法的最好结果为98.84%。在铰接和射影变换数据集上皆达到100%的识别精度,而对比算法的最好结果分别为89.75%和95%。结论本文提出的全尺度可视化形状表示方法,通过一幅彩色图像紧凑地表达了全部形状信息。通过卷积模型既学习了轮廓点间的形状特征关系,又学习了不同尺度间的形状特征关系。本文方法...     

基于多尺度形状分析的叶形识别系统

叶形自动识别系统使用一种改进了的边界跟踪算法检测叶片边界,应用多尺度形状分析技术有效地提取出规范锯齿长度和曲率尺度空间图像极大值点集两个曲率特征。综合叶片的离心率、似圆率和这两个边界曲率特征来检索叶片数据库,进行形状匹配,实现叶片自动分类。实验结果表明,该系统使用的技术能大幅提高叶形识别的精确率和检索率。