Representation and shape matching of 3-d objects

A three-dimensional scene analysis system for the shape matching of real world 3-D objects is presented. Various issues related to representation and modeling of 3-D objects are addressed. A new method for the approximation of 3-D objects by a set of planar faces is discussed. The major advantage of this method is that it is applicable to a complete object and not restricted to single range view which was the limitation of the previous work in 3-D scene analysis. The method is a sequential region growing algorithm. It is not applied to range images, but rather to a set of 3-D points. The 3-D model of an object is obtained by combining the object points from a sequence of range data images corresponding to various views of the object, applying the necessary transformations and then approximating the surface by polygons. A stochastic labeling technique is used to do the shape matching of 3-D objects. The technique matches the faces of an unknown view against the faces of the model. It explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. It is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. The objective here is to match the individual views of the object taken from any vantage point. Details of the algorithm are presented and the results are shown on several unknown views of a complicated automobile casting.     

Recognition and pose estimation of unoccluded three-dimensionalobjects from a two-dimensional perspective view by banks of neuralnetworks

This paper describes a neural network (NN) based system for recognition and pose estimation of an unoccluded three-dimensional (3-D) object from any single two-dimensional (2-D) perspective view. The approach is invariant to translation, orientation, and scale. First, the binary silhouette of the object is obtained and normalized for translation and scale. Then, the object is represented by a set of rotation invariant features derived from the complex orthogonal pseudo-Zernike moments of the image. The recognition scheme combines the decisions of a bank of multilayer perceptron NN classifiers operating in parallel on the same data. These classifiers have different topologies and internal parameters, but are trained on the same set of exemplar perspective views of the objects. Next, two pose parameters, elevation and aspect angles, are obtained by a novel two-stage NN system consisting of a quadrant classifier followed by NN angle estimators. Performance is tested on clean and noisy data bases of military ground vehicles. Comparative studies with three other classifiers (a single NN, the weighted nearest-neighbor classifier, and a binary decision tree) are carried out.     

The automatic construction of a view-independent relational modelfor 3-D object recognition

A view-independent relational model (VIRM) used in a vision system for recognizing known 3-D objects from single monochromatic images of unknown scenes is described. The system inspects a CAD model from a number of different viewpoints, and a statistical interference is applied to identify relatively view-independent relationships among component parts of the object. These relations are stored as a relational model of the object, which is represented in the form of a hypergraph. Three-dimensional components of the object, which can be associated with extended image features obtained by grouping of primitive 2-D features are represented as nodes of the hypergraph. Covisibility of model features is represented by means of hyperedges of the hypergraph, and the pairwise view-independent relations form procedural constraints associated with the hypergraph edges. During the recognition phase, the covisibility measures allow a best-first search of the graph for acceptable matches     

Partial surface and volume matching in three dimensions

In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this problem we are given two objects in 3-space, each represented as a set of points, and the goal is to find a rigid motion of one object which makes a sufficiently large portion of its boundary lying sufficiently close to a corresponding portion of the boundary of the second object. This is an important problem in pattern recognition and in computer vision, with many industrial, medical, and chemical applications. Our method treats separately the rotation and the translation components of the Euclidean motion that we seek. The algorithm steps through a sequence of rotations, in a steepest-descent style, and uses a novel technique for scoring the match for any fixed rotation. Experimental results on various examples, involving data from industrial applications, medical imaging, and molecular biology, are presented, and show the accurate and robust performance of our algorithm     

基于双目立体视觉的西瓜子三维形态检测

为提高西瓜子自动筛选系统的性能,提出一种用于度量和处理西瓜子弯翘度的方法.把双目平行立体视觉与骨骼线相结合,利用双目立体视觉技术重建物体表面的三维信息.将该信息进行正交投影,分别得到西瓜子在前视图和侧视图的轮廓.利用曲率尺度空间与骨骼线相结合的方法对侧视图的瓜子轮廓进行特征提取.实验结果表明,该方法对弯翘瓜子有较高的识别精度.     

Planning multiple observations for object recognition

Most computer vision systems perform object recognition on the basis of the features extracted from a single image of the object. The problem with this approach is that it implicitly assumes that the available features are sufficient to determine the identity and pose of the object uniquely. If this assumption is not met, then the feature set is insufficient, and ambiguity results. Consequently, much research in computer vision has gone toward finding sets of features that are sufficient for specific tasks, with the result that each system has its own associated set of features. A single, general feature set would be desirable. However, research in automatic generation of object recognition programs has demonstrated that predetermined, fixed feature sets are often incapable of providing enough information to unambiguously determine either object identity or pose. One approach to overcoming the inadequacy of any feature set is to utilize multiple sensor observations obtained from different viewpoints, and combine them with knowledge of the 3-D structure of the object to perform unambiguous object recognition. This article presents initial results toward performing object recognition by using multiple observations to resolve ambiguities. Starting from the premise that sensor motions should be planned in advance, the difficulties involved in planning with ambiguous information are discussed. A representation for planning that combines geometric information with viewpoint uncertainty is presented. A sensor planner utilizing the representation was implemented, and the results of pose-determination experiments performed with the planner are discussed.     

Recognizing large isolated 3-D objects through next view planning using inner camera invariants.

Most model-based three-dimensional (3-D) object recognition systems use information from a single view of an object. However, a single view may not contain sufficient features to recognize it unambiguously. Further, two objects may have all views in common with respect to a given feature set, and may be distinguished only through a sequence of views. A further complication arises when in an image, we do not have a complete view of an object. This paper presents a new online scheme for the recognition and pose estimation of a large isolated 3-D object, which may not entirely fit in a camera's field of view. We consider an uncalibrated projective camera, and consider the case when the internal parameters of the camera may be varied either unintentionally, or on purpose. The scheme uses a probabilistic reasoning framework for recognition and next-view planning. We show results of successful recognition and pose estimation even in cases of a high degree of interpretation ambiguity associated with the initial view.     

基于异构特征聚合的局部视图扭曲型纸币识别

如何识别同一物体的不同结构的表现形式,对于机器而言,是一个比较困难的识别工作.本文以易变形的纸币为例,提出了一种基于异构特征聚合的局部视图扭曲型纸币识别方法.首先利用灰度梯度共生矩阵、Haishoku算法和圆形LBP分别获得纹理风格、色谱风格和纹理,这些特征从不同的角度描述了局部纸币图像,然后通过VGG-16、ResN...     

New methods for matching 3-d objects with single perspective views

In this paper we analyze the ability of a computer vision system to derive properties of the three-dimensional (3-D) physical world from viewing two-dimensional (2-D) images. We present a new approach which consists of a model-based interpretation of a single perspective image. Image linear features and linear feature sets are backprojected onto the 3-D space and geometric models are then used for selecting possible solutions. The paper treats two situations: 1) interpretation of scenes resulting from a simple geometric structure (orthogonality) in which case we seek to determine the orientation of this structure relatively to the viewer (three rotations) and 2) recognition of moderately complex objects whose shapes (geometrical and topological properties) are provided in advance. The recognition technique is limited to objects containing, among others, straight edges and planar faces. In the first case the computation can be carried out by a parallel algorithm which selects the solution that has received the largest number of votes (accumulation space). In the second case an object is uniquely assigned to a set of image features through a search strategy. As a by-product, the spatial position and orientation (six degrees of freedom) of each recognized object is determined as well. The method is valid over a wide range of perspective images and it does not require perfect low-level image segmentation. It has been successfully implemented for recognizing a class of industrial parts.     

Structural indexing: efficient 3-D object recognition

The authors present an approach for the recognition of multiple 3-D object models from three 3-D scene data. The approach uses two different types of primitives for matching: small surface patches, where differential properties can be reliably computed, and lines corresponding to depth or orientation discontinuities. These are represented by splashes and 3-D curves, respectively. It is shown how both of these primitives can be encoded by a set of super segments, consisting of connected linear segments. These super segments are entered into a table and provide the essential mechanism for fast retrieval and matching. The issues of robustness and stability of the features are addressed in detail. The acquisition of the 3-D models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3-D curves. The authors present results with the current system (3-D object recognition based on super segments) and discuss further extensions     

Human gesture recognition system for TV viewing using time-of-flight camera

We developed a new device-free user interface for TV viewing that uses a human gesture recognition technique. Although many motion recognition technologies have been reported, no man–machine interface that recognizes a large enough variety of gestures has been developed. The difficulty was the lack of spatial information that could be acquired from normal video sequences. We overcame the difficulty by using a time-of-flight camera and novel action recognition techniques. The main functions of this system are gesture recognition and posture measurement. The former is performed using the bag-of-features approach, which uses key-point trajectories as features. The use of 4-D spatiotemporal trajectory features is the main technical contribution of the proposed system. The latter is obtained through face detection and object tracking technology. The interface is useful because it does not require any contact-type devices. Several experiments proved the effectiveness of our proposed method and the usefulness of the system.