A CAD Model Based System for Object Recognition

3D object recognition is a difficult and yet an important problem in computer vision. A 3D object recognition system has two major components, namely: an object modeller and a system that performs the matching of stored representations to those derived from the sensed image. The performance of systems wherein the construction of object models is done by training from one or more images of the objects, has not been very satisfactory. Although objects used in a robotic workcell or in assembly processes have been designed using a CAD system, the vision systems used for recognition of these objects are independent of the CAD database. This paper proposes a scheme for interfacing the CAD database of objects and the computer vision processes used for recognising these objects. CAD models of objects are processed to generate vision oriented features that appear in the different views of the object and the same features are extracted from images of the object to identify the object and its pose.     

Efficient data structures for model-based 3-D object recognitionand localization from range images

An effective method of surface characterization of 3D objects using surface curvature properties and an efficient approach to recognizing and localizing multiple 3D free-form objects (free-form object recognition and localization) are presented. The approach is surface based and is therefore not sensitive to noise and occlusion, forms hypothesis by local analysis of surface shapes, does not depend on the visibility of complete objects, and uses information from a CAD database in recognition and localization. A knowledge representation scheme for describing free-form surfaces is described. The data structure and procedures are well designed, so that the knowledge leads the system to intelligent behavior. Knowledge about surface shapes is abstracted from CAD models to direct the search in verification of vision hypotheses. The knowledge representation used eases processes of knowledge acquisition, information retrieval, modification of knowledge base, and reasoning for solution     

Genetic object recognition using combinations of views

Investigates the application of genetic algorithms (GAs) for recognizing real 2D or 3D objects from 2D intensity images, assuming that the viewpoint is arbitrary. Our approach is model-based (i.e. we assume a pre-defined set of models), while our recognition strategy relies on the theory of algebraic functions of views. According to this theory, the variety of 2D views depicting an object can be expressed as a combination of a small number of 2D views of the object. This implies a simple and powerful strategy for object recognition: novel 2D views of an object (2D or 3D) can be recognized by simply matching them to combinations of known 2D views of the object. In other words, objects in a scene are recognized by "predicting" their appearance through the combination of known views of the objects. This is an important idea, which is also supported by psychophysical findings indicating that the human visual system works in a similar way. The main difficulty in implementing this idea is determining the parameters of the combination of views. This problem can be solved either in the space of feature matches among the views ("image space") or the space of parameters ("transformation space"). In general, both of these spaces are very large, making the search very time-consuming. In this paper, we propose using GAs to search these spaces efficiently. To improve the efficiency of genetic searching in the transformation space, we use singular value decomposition and interval arithmetic to restrict the genetic search to the most feasible regions of the transformation space. The effectiveness of the GA approaches is shown on a set of increasingly complex real scenes where exact and near-exact matches are found reliably and quickly     

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     

Constrained pose refinement of parametric objects

Pose refinement is an essential task for computer vision systems that require the calibration and verification of model and camera parameters. Typical domains include the real-time tracking of objects and verification in model-based recognition systems. A technique is presented for recovering model and camera parameters of 3D objects from a single two-dimensional image. This basic problem is further complicated by the incorporation of simple bounds on the model and camera parameters and linear constraints restricting some subset of object parameters to a specific relationship. It is demonstrated in this paper that this constrained pose refinement formulation is no more difficult than the original problem based on numerical analysis techniques, including active set methods and lagrange multiplier analysis. A number of bounded and linearly constrained parametric models are tested and convergence to proper values occurs from a wide range of initial error, utilizing minimal matching information (relative to the number of parameters and components). The ability to recover model parameters in a constrained search space will thus simplify associated object recognition problems.     

Generic model abstraction from examples

The recognition community has typically avoided bridging the representational gap between traditional, low-level image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models using simple scenes containing idealized, textureless objects or by bringing the models closer to the images using 3D CAD model templates or 2D appearance model templates. In this paper, we attempt to bridge the representational gap for the domain of model acquisition. Specifically, we address the problem of automatically acquiring a generic 2D view-based class model from a set of images, each containing an exemplar object belonging to that class. We introduce a novel graph-theoretical formulation of the problem in which we search for the lowest common abstraction among a set of lattices, each representing the space of all possible region groupings in a region adjacency graph representation of an input image. The problem is intractable and we present a shortest path-based approximation algorithm to yield an efficient solution. We demonstrate the approach on real imagery.     

System for reconstruction of three-dimensional micro objects from multiple photographic images

We introduce a system to reconstruct a three-dimensiojnal (3D) polygonal model of 3D micro objects with outer dimensions ranging from several hundred microns to several millimeters from multiple two-dimensional (2D) images of an object taken from different views. The data acquisition system consists of a digital microscope that captures still images at a resolution of 1600 × 1200 pixels and a computer-controlled turntable. We employ the shape-from-silhouette (SFS) method to construct a voxel-based 3D model from silhouette images. The concave shapes are further carved by using the space carving technique. In order to make the resulting model compatible with a commercial CAD/CAM system, the voxel model is converted into a triangular mesh using the marching cubes algorithm. Because the mesh generated from the voxel model by using the marching cubes algorithm inherits the staircase effect, the mesh is adjusted to recover the object precisely by using silhouette images. Finally, we evaluate the accuracy of the proposed method. The reconstructed models of complex micro objects indicate the effectiveness of the 3D shape reconstruction system for micro objects.     

基于形态和约束结构的几何哈希法

几何哈希法,作为一种有效的模型搜索算法,在物体识别中有着重要的应用。现有的几何哈希法仅适合于仿射变换下的二维景物识别,论文提出了适合透视投影变换下三维物体识别的几何哈希方法。该方法利用物体的三维形态和物体中具有射影不变量的几何约束结构来构造哈希表。一方面,几何约束结构提供了物体模型的索引功能;另一方面,物体的三维形态提供了物体成像位姿的有关信息,使后续的匹配验证得以简化。实验中使用人造物体对该方法进行了验证,实验表明该方法正确有效。     

基于CAD模型的3D散乱数据点三角剖分方法

提出了一种针对空间大规模散乱数据点三角剖分的方法。该方法基于可用的CAD模型,采用“分而治之”的思想。对齐测量数据点与CAD模型、记录数据点及在CAD裁剪NURBS曲面实体上投影点。分别对每块实体的参数区域(u,v)相应点2D-Delaunay三角化、根据R2区域的连通结构反构造出3D三角网。进行冗余三角形删除和网格片缝合等优化处理。与其他方法不同的是,它不受测量数据的分布方式和物体曲面形状的拓扑结构限制。实际的算例结果表明,该方法高效且可靠实用。     

Integration of Multiple Feature Groups and Multiple Views into a 3D Object Recognition System

This paper proposes two approaches for utilizing the information in multiple entity groups and multiple views to reduce the number of hypotheses passed to the verification stage in a model-based object recognition system employing invariant feature indexing (P. J. Flynn and A. K. Jain, CVGIP: Image Understand. 55(2), 1992, 119-129). The first approach is based on a majority voting scheme that keeps track of the number of consistent votes cast by prototype hypotheses for particular object models. The second approach examines the consistency of estimated object pose from multiple groups of entities (surfaces) in one or more views. A salient feature of our system and experiment design compared to most existing 3D object recognition systems is our use of a large object database and a large number of test images. Monte Carlo experiments employing 585 single-view synthetic range images and 117 pairs of synthetic range images with a large CAD-based 3D object database (P. J. Flynn and A. K. Jain, IEEE Trans. Pattern Anal. Mach. Intell. 13(2), 1991, 114-132) show that a large number of hypotheses (about 60% for single views and 90% for multiple views on average) can be eliminated through use of these approaches. The techniques have also been tested on several real 3D objects sensed by a Technical Arts 100X range scanner to demonstrate a substantial improvement in recognition time.     

Using geometric distance fits for 3-D object modeling andrecognition

Addresses the problems of automatically constructing algebraic surface models from sets of 2D and 3D images and using these models in pose computation, motion and deformation estimation, and object recognition. We propose using a combination of constrained optimization and nonlinear least-squares estimation techniques to minimize the mean-squared geometric distance between a set of points or rays and a parameterized surface. In modeling tasks, the unknown parameters are the surface coefficients, while in pose and deformation estimation tasks they represent the transformation which maps the observer's coordinate system onto the modeled surface's own coordinate system. We have applied this approach to a variety of real range, computerized tomography and video images     

CurveNet: Curvature-Based Multitask Learning Deep Networks for 3D Object Recognition

In computer vision fields, 3D object recognition is one of the most important tasks for many real-world applications. Three-dimensional convolutional neural networks (CNNs) have demonstrated their advantages in 3D object recognition. In this paper, we propose to use the principal curvature directions of 3D objects (using a CAD model) to represent the geometric features as inputs for the 3D CNN. Our framework, namely CurveNet, learns perceptually relevant salient features and predicts object class labels. Curvature directions incorporate complex surface information of a 3D object, which helps our framework to produce more precise and discriminative features for object recognition. Multitask learning is inspired by sharing features between two related tasks, where we consider pose classification as an auxiliary task to enable our CurveNet to better generalize object label classification. Experimental results show that our proposed framework using curvature vectors performs better than voxels as an input for 3D object classification. We further improved the performance of CurveNet by combining two networks with both curvature direction and voxels of a 3D object as the inputs. A Cross-Stitch module was adopted to learn effective shared features across multiple representations. We evaluated our methods using three publicly available datasets and achieved competitive performance in the 3D object recognition task.      

基于方面图技术的三维运动目标识别

三维目标在不同的视点下呈现不同的姿态 ,所得的二维视图也不尽相同 ,因此三维目标识别是一个较为复杂的问题 .为此提出了通过图象序列和图象序列之间的转移关系 ,根据胜者为王的原则来识别三维目标的方法 .该方法采用极指数栅格技术和傅立叶变换相结合得到目标的轮廓不变量 ;用神经网络结合方面图技术 ,通过识别运动目标图象序列来识别三维运动目标 ,实现了一个目标识别系统 .实验结果证明 ,此方法可以有效地用于三维运动目标的识别     

2.5D face recognition using Patch Geodesic Moments

In this paper, we propose a novel Patch Geodesic Distance (PGD) to transform the texture map of an object through its shape data for robust 2.5D object recognition. Local geodesic paths within patches and global geodesic paths for patches are combined in a coarse to fine hierarchical computation of PGD for each surface point to tackle the missing data problem in 2.5D images. Shape adjusted texture patches are encoded into local patterns for similarity measurement between two 2.5D images with different viewing angles and/or shape deformations. An extensive experimental investigation is conducted on 2.5 face images using the publicly available BU-3DFE and Bosphorus databases covering face recognition under expression and pose changes. The performance of the proposed method is compared with that of three benchmark approaches. The experimental results demonstrate that the proposed method provides a very encouraging new solution for 2.5D object recognition.     

An effective 3D target recognition model imitating robust methods of the human visual system

This paper presents a model of 3D object recognition motivated from the robust properties of human vision system (HVS). The HVS shows the best efficiency and robustness for an object identification task. The robust properties of the HVS are visual attention, contrast mechanism, feature binding, multi-resolution, size tuning, and part-based representation. In addition, bottom-up and top-down information are combined cooperatively. Based on these facts, a plausible computational model integrating these facts under the Monte Carlo optimization technique was proposed. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process is used to initialize parameters in a discriminative way; the top-down process is used to optimize them in a generative way. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation in visible and infrared band images.