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.     

一种具有自终止特性的视点规划方法

提出了一种具有自终止特性的对未知物体模型自动3维测量的视点规划方法。首先在寻找最优视点阶段，利用群矢量链方法获得视觉传感器在下一个视点的观测方向，而视觉传感器在空间的确切位置参数是通过计算矢量场内的边界积分获得，其中获得最大边界积分的空间位置被定为下一个最优视点。同时为了保证对未知物体模型自动3维测量过程具有自终止特性，根据高斯定理，提出由测量所得的点云直接计算出物体体积的方法，并将连续两个视点下物体体积的变化量作为终止测量的依据来判断规划过程是否需要终止或继续。实验结果表明，该具有自终止特性的视点规划方法是可行而且有效的。     

A Comparison of Probabilistic, Possibilistic and Evidence Theoretic Fusion Schemes for Active Object Recognition

One major goal of active object recognition systems is to extract useful information from multiple measurements. We compare three frameworks for information fusion and view-planning using different uncertainty calculi: probability theory, possibility theory and Dempster-Shafer theory of evidence. The system dynamically repositions the camera to capture additional views in order to improve the classification result obtained from a single view. The active recognition problem can be tackled successfully by all the considered approaches with sometimes only slight differences in performance. Extensive experiments confirm that recognition rates can be improved considerably by performing active steps. Random selection of the next action is much less efficient than planning, both in recognition rate and in the average number of steps required for recognition. As long as the rate of wrong object-pose classifications stays low the probabilistic implementation always outperforms the other approaches. If the outlier rate increases averaging fusion schemes outperform conjunctive approaches for information integration. We use an appearance based object representation, namely the parametric eigenspace, but the planning algorithm is actually independent of the details of the specific object recognition environment. Received: June 18, 1998; revised November 17, 1998     

Graph-based recognition of morphological features

Automatic recognition of the shapes of objects represented as solid models is very important in design optimization. Object shape also governs ease of manufacture, ease of orientability, field use and all other life-cycle applications. Characteristic attributes of an object shape such as chamfers, protrusions and depressions, play a significant role in process planning, design for manufacture, etc. These attributes are popularly known as morphological features. In this paper, the problem of identifying such morphological features is divided into two phases: the feature extraction phase and the feature classification phase. In feature extraction, the mechanical part is decomposed into its constituent features such as holes, protrusions and depressions based on the connectivity class in the edge-face graph of the part. In feature classification, on the other hand, the extracted features are identified and classified. This paper describes a feature classification scheme based on topological and geometric attributes of a morphological feature. The feature classification scheme outlined in this paper is capable of identifying new features with minimal human interface.     

基于多视角距离图象的三维物体建模及其在识别中的应用

模型的表示和构建是基于距离图象三维物体识别技术中的关键模块之一.针对已有方法存在的若干问题,提出一个新的综合多个视角距离图象的三维物体模型表示策略和增量式的模型习得算法,并将该模型表示用于三维物体识别中.实验结果验证了算法的有效性.     

In-hand recognition and manipulation of elastic objects using a servo-tactile control strategy

Grasping and manipulating objects with robotic hands depend largely on the features of the object to be used. Especially, features such as softness and deformability are crucial to take into account during the manipulation tasks. Indeed, positions of the fingers and forces to be applied by the robot hand when manipulating an object must be adapted to the caused deformation. For unknown objects, a previous recognition stage is usually needed to get the features of the object, and the manipulation strategies must be adapted depending on that recognition stage. To obtain a precise control in the manipulation task, a complex object model is usually needed and performed, for example using the Finite Element Method. However, these models require a complete discretization of the object and they are time-consuming for the performance of the manipulation tasks. For that reason, in this paper a new control strategy, based on a minimal spring model of the objects, is presented and used for the control of the robot hand. This paper also presents an adaptable tactile-servo control scheme that can be used in in-hand manipulation tasks of deformable objects. Tactile control is based on achieving and maintaining a force value at the contact points which changes according to the object softness, a feature estimated in an initial recognition stage.     

Matching ‘sticks,plates and blobs’ objects using geometric and relational constraints

A recognition scheme using relational and rough geometric information about three-dimensional man-made objects to recognize instances of the objects in single perspectivenormal views of scenes is described. Experiments performed using the matching scheme show that, in most cases, the object in the view can be identified correctly and reasonable estimates can be made of the unknown camera position responsible for generating the given view.The technique is based on the fact that the camera position constrains the appearance of the various parts of the object. The propagation of these constraints from one planar object surface to another through the projection equations is worked out. This constraint propagation guides the matching scheme in the development of the interpretation of the scene. The results provide an estimate of the camera position within 20° of the actual location.     

PLANNING SEQUENCES OF VIEWS FOR 3-D OBJECT RECOGNITION AND POSE DETERMINATION

We present a method for planning sequences of views for recognition and pose (orientation) determination of 3-D objects of arbitrary shape. The approach consists of a learning stage in which we derive a recognition and pose identification plan and a stage in which actual recognition and pose identification take place. In the learning stage, the objects are observed from all possible views and each view is characterized by an extracted feature vector. These vectors are then used to structure the views into clusters based on their proximity in the feature space. To resolve the remaining ambiguity within each of the clusters, we designed a strategy which exploits the idea of taking additional views. We developed an original procedure which analyzes the transformation of individual clusters under changing viewpoints into several smaller clusters. This results in an optimal next-view planning when additional views are necessary to resolve the ambiguities. This plan then guides the actual recognition and pose determination of an unknown object in an unknown pose.     

Information entropy-based viewpoint planning for 3-D object reconstruction

In this paper, we present an information entropy-based viewpoint-planning approach for reconstruction of freeform surfaces of three-dimensional objects. To achieve the reconstruction, the object is first sliced into a series of cross section curves, with each curve to be reconstructed by a closed B-spline curve. In the framework of Bayesian statistics, we propose an improved Bayesian information criterion (BIC) for determining the B-spline model complexity. Then, we analyze the uncertainty of the model using entropy as the measurement. Based on this analysis, we predict the information gain for each cross section curve for the next measurement. After predicting the information gain of each curve, we obtain the information change for all the B-spline models. This information gain is then mapped into the view space. The viewpoint that contains maximal information gain about the object is selected as the next best view. Experimental results show successful implementation of our view planning method for digitization and reconstruction of freeform objects.     

主动目标几何建模研究方法综述

目的 目标建模是机器视觉领域的主要研究方向之一,主动目标建模是在保证建模完整度的情况下,通过有计划地调节相机的位姿参数,以更少的视点和更短的运动路径实现目标建模的智能感知方法。为了反映主动目标建模的研究现状和最新进展,梳理分析了2004年以来的相关文献,对国内外研究方法做出概括性总结。方法 以重构模型类型和规划视点所用信息作为划分依据,将无模型的主动目标建模方法分为基于表面的主动目标建模方法、基于搜索的目标建模方法和两者相结合的方法3大类,重点对前两类方法进行综述,首先解释了每类方法的基本思想,总结每类方法涉及的问题,然后对相关问题的主要研究方法进行归纳和分析,最后将各个问题的解决方法进行合理的搭配组合,形成不同的主动目标建模方法,并对各类方法的优势和局限性进行了总结。结果 各类主动目标建模算法在适用场景范围、计算复杂度等方面存在差异,但相对于传统的被动目标建模方法,当前的主动目标建模算法已经能够极大程度地提高建模任务的质量和降低建模所需代价。结论 基于表面的主动目标建模方法思想相对简单,但仅适用于表面简单的目标建模。基于搜索的目标建模方法能够量化地评价每一个候选视点,适用广泛且涉及的问题相对于基于表面的方法有更大的解决空间,有更多的研究成果产生。将二者涉及问题的不同研究方法相搭配,可以构成不同的主动目标建模方法子类。     

脑皮层视觉认知量化模型及其在目标检测定位中的应用

MIT大学的Rieseshuber和Poggio提出了脑颞叶皮层视觉认知系统的标准量化模型(QMVC),这种前馈式层级模型很好地仿效了脑皮层视觉从简单认知元到复杂认知元的识别机理.本文由QMVC模型提取出一组新的特征矢量,这组特征矢量具有对目标变换的不变性.基于QMVC模型的特征矢量建立了新的目标识别系统结构,新目标识别系统对各类目标具有不错的识别率和ROC特性.最后本文引入了尺度窗技术,将新特征应用于复杂场景中的目标检测和定位,实验结果说明本文的新目标检测方法是有效的.     

On the verification of hypothesized matches in model-basedrecognition

Model-based recognition methods generally use ad hoc techniques to decide whether or not a model of an object matches a given scene. The most common such technique is to set an empirically determined threshold on the fraction of model features that must be matched to data features. Conditions under which to accept a match as correct are rigorously derived. The analysis is based on modeling the recognition process as a statistical occupancy problem. This model makes the assumption that pairings of object and data features can be characterized as a random process with a uniform distribution. The authors present a number of examples illustrating that real image data are well approximated by such a random process. Using a statistical occupancy model, they derive an expression for the probability that a randomly occurring match will account for a given fraction of the features of a particular object. This expression is a function of the number of model features, the number of data features, and bounds on the degree of sensor noise. It provides a means of setting a threshold such that the probability of a random match is very small     

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.     

A computational model of view degeneracy

We quantify the observation by Kender and Freudenstein (1987) that degenerate views occupy a significant fraction of the viewing sphere surrounding an object. For a perspective camera geometry, we introduce a computational model that can be used to estimate the probability that a view degeneracy will occur in a random view of a polyhedral object. For a typical recognition system parameterization, view degeneracies typically occur with probabilities of 20 percent and, depending on the parameterization, as high as 50 percent. We discuss the impact of view degeneracy on the problem of object recognition and, for a particular recognition framework, relate the cost of object disambiguation to the probability of view degeneracy. To reduce this cost, we incorporate our model of view degeneracy in an active focal length control paradigm that balances the probability of view degeneracy with the camera field of view. In order to validate both our view degeneracy model as well as our active focal length control model, a set of experiments are reported using a real recognition system operating on real images     

Invariant object recognition in the visual system with novel views of 3D objects

To form view-invariant representations of objects, neurons in the inferior temporal cortex may associate together different views of an object, which tend to occur close together in time under natural viewing conditions. This can be achieved in neuronal network models of this process by using an associative learning rule with a short-term temporal memory trace. It is postulated that within a view, neurons learn representations that enable them to generalize within variations of that view. When three-dimensional (3D) objects are rotated within small angles (up to, e.g., 30 degrees), their surface features undergo geometric distortion due to the change of perspective. In this article, we show how trace learning could solve the problem of in-depth rotation-invariant object recognition by developing representations of the transforms that features undergo when they are on the surfaces of 3D objects. Moreover, we show that having learned how features on 3D objects transform geometrically as the object is rotated in depth, the network can correctly recognize novel 3D variations within a generic view of an object composed of a new combination of previously learned features. These results are demonstrated in simulations of a hierarchical network model (VisNet) of the visual system that show that it can develop representations useful for the recognition of 3D objects by forming perspective-invariant representations to allow generalization within a generic view.     

Shape-based indexing scheme for camera view invariant 3-D object retrieval

Camera view invariant 3-D object retrieval is an important issue in many traditional and emerging applications such as security, surveillance, computer-aided design (CAD), virtual reality, and place recognition. One straightforward method for camera view invariant 3-D object retrieval is to consider all the possible camera views of 3-D objects. However, capturing and maintaining such views require an enormous amount of time and labor. In addition, all camera views should be indexed for reasonable retrieval performance, which requires extra storage space and maintenance overhead. In the case of shape-based 3-D object retrieval, such overhead could be relieved by considering the symmetric shape feature of most objects. In this paper, we propose a new shape-based indexing and matching scheme of real or rendered 3-D objects for camera view invariant object retrieval. In particular, in order to remove redundant camera views to be indexed, we propose a camera view skimming scheme, which includes: i) mirror shape pairing and ii) camera view pruning according to the symmetrical patterns of object shapes. Since our camera view skimming scheme considerably reduces the number of camera views to be indexed, it could relieve the storage requirement and improve the matching speed without sacrificing retrieval accuracy. Through various experiments, we show that our proposed scheme can achieve excellent performance.     

基于加速度特征的可拓动作识别方法

针对基于图像和视频的动作识别系统具有特征采集设备复杂、视角固定和需要采集多视角图像等缺点,提出基于加速度特征的可拓动作识别方法。该方法利用物体向不同方向运动时,其关键部位点的三轴加速度具有一定区分度的特点,结合可拓识别方法,实现动作识别。在构建的手臂动作识别系统中,测得动作识别率可达94.4%。该方法可应用于智能监控、医疗电子等领域。     

基于卡尔曼滤波的移动机器人运动目标跟踪

提出了一种基于卡尔曼滤波的运动目标快速跟踪算法。针对复杂背景下彩色运动目标跟踪问题,采用基于颜色特征和形状特征相结合的方法进行目标识别。利用卡尔曼滤波器的预测功能,预测运动目标在下一帧中的位置,将图像全局搜索问题转换为局部搜索,提高了系统的实时性。实验结果表明:该算法满足移动机器人运动控制的实时性要求,实现了对运动目标的快速跟踪。