一种提取物体线形骨架的新方法

提出了一种提取物体线形骨架的新方法. 该方法首先计算物体距离变换的梯度, 从而得到一个矢量场. 距离变换的梯度对提取物体线形骨架具有重要意义, 可据此获得物体内部的关键点, 其中每一个关键点代表了物体的一个凸部分. 之后, 用搜索梯度最短路径的方法连接关键点, 得到物体的线形骨架. 本文方法得到的线形骨架能很好地反映物体拓扑和形状特征, 并不易受边界噪声干扰. 此外, 本文方法克服了基于距离变换的骨架提取算法的固有缺点, 获得了具有良好连通性的骨架. 因此, 基于本文方法得到的骨架能用于物体识别和匹配等领域. 对大量二维、三维物体的实验取得了令人满意的效果.     

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.     

Ground object recognition and segmentation from aerial image‐based 3D point cloud

Several attempts have been made to grasp three‐dimensional (3D) ground shape from a 3D point cloud generated by aerial vehicles, which help fast situation recognition. However, identifying such objects on the ground from a 3D point cloud, which consists of 3D coordinates and color information, is not straightforward due to the gap between the low‐level point information (coordinates and colors) and high‐level context information (objects). In this paper, we propose a ground object recognition and segmentation method from a geo‐referenced point cloud. Basically, we rely on some existing tools to generate such a point cloud from aerial images, and our method tries to give semantics to each set of clustered points. In our method, firstly, such points that correspond to the ground surface are removed using the elevation data from the Geographical Survey Institute. Next, we apply an interpoint distance‐based clustering and color‐based clustering. Then, such clusters that share some regions are merged to correctly identify a cluster that corresponds to a single object. We have evaluated our method in several experiments in real fields. We have confirmed that our method can remove the ground surface within 20 cm error and can recognize most of the objects.     

3D Hough transform for sphere recognition on point clouds

Three-dimensional object recognition on range data and 3D point clouds is becoming more important nowadays. Since many real objects have a shape that could be approximated by simple primitives, robust pattern recognition can be used to search for primitive models. For example, the Hough transform is a well-known technique which is largely adopted in 2D image space. In this paper, we systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds. In particular, we study and compare four variants which are characterized by the number of points drawn together for surface computation into the parametric space and we formally discuss their models. We also propose a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection. The methods are tested on synthetic and real datasets.     

The Alignment of Objects with Smooth Surfaces

This paper examines the recognition of rigid objects bounded by smooth surfaces, using an alignment approach. The projected image of such an object changes during rotation in a manner that is generally difficult to predict. An approach to this problem is suggested, using the 3D surface curvature at the points along the silhouette. The curvature information requires a single number for each point along the object′s silhouette, the radial curvature at the point. We have implemented this method and tested it on images of complex 3D objects. Models of the viewed objects were acquired using three images of each object. The implemented scheme was found to give accurate predictions of the objects′ appearances for large transformations. Using this method, a small number of (viewer-centered) models can be used to predict the new appearance of an object from any given viewpoint.     

Model construction and shape recognition from occluding contours

A technique is presented for recognizing a 3D object (a model in an image library) from a single 2D silhouette using information such as corners (points with high positive curvatures) and occluding contours, rather than straight line segments. The silhouette is assumed to be a parallel projection of the object. Each model is stored as a set of the principal quadtrees, from which the volume/surface octree of the model is generated. Feature points (i.e. corners) are extracted to guide the recognition process. Four-point correspondences between the 2D feature points of the observed object and 3D feature points of each model are hypothesized, and then verified by applying a variety of constraints to their associated viewing parameters. The result of the hypothesis and verification process is further validated by 2D contour matching. This approach allows for a method of handling both planar and curved objects in a uniform manner, and provides a solution to the recognition of multiple objects with occlusion as demonstrated by the experimental results     

A spherical representation for recognition of free-form surfaces

Introduces a new surface representation for recognizing curved objects. The authors approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ellipsoid, until it fits the surface of the object. The authors define local regularity constraints that the mesh must satisfy. The authors then define a canonical mapping between the mesh describing the object and a standard spherical mesh. A surface curvature index that is pose-invariant is stored at every node of the mesh. The authors use this object representation for recognition by comparing the spherical model of a reference object with the model extracted from a new observed scene. The authors show how the similarity between reference model and observed data can be evaluated and they show how the pose of the reference object in the observed scene can be easily computed using this representation. The authors present results on real range images which show that this approach to modelling and recognizing 3D objects has three main advantages: (1) it is applicable to complex curved surfaces that cannot be handled by conventional techniques; (2) it reduces the recognition problem to the computation of similarity between spherical distributions; in particular, the recognition algorithm does not require any combinatorial search; and (3) even though it is based on a spherical mapping, the approach can handle occlusions and partial views     

On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes

3D object recognition from local features is robust to occlusions and clutter. However, local features must be extracted from a small set of feature rich keypoints to avoid computational complexity and ambiguous features. We present an algorithm for the detection of such keypoints on 3D models and partial views of objects. The keypoints are highly repeatable between partial views of an object and its complete 3D model. We also propose a quality measure to rank the keypoints and select the best ones for extracting local features. Keypoints are identified at locations where a unique local 3D coordinate basis can be derived from the underlying surface in order to extract invariant features. We also propose an automatic scale selection technique for extracting multi-scale and scale invariant features to match objects at different unknown scales. Features are projected to a PCA subspace and matched to find correspondences between a database and query object. Each pair of matching features gives a transformation that aligns the query and database object. These transformations are clustered and the biggest cluster is used to identify the query object. Experiments on a public database revealed that the proposed quality measure relates correctly to the repeatability of keypoints and the multi-scale features have a recognition rate of over 95% for up to 80% occluded objects.     

Registering multiview range data to create 3D computer objects

Concerns the problem of range image registration for the purpose of building surface models of 3D objects. The registration task involves finding the translation and rotation parameters which properly align overlapping views of the object so as to reconstruct from these partial surfaces, an integrated surface representation of the object. The registration task is expressed as an optimization problem. We define a function which measures the quality of the alignment between the partial surfaces contained in two range images as produced by a set of motion parameters. This function computes a sum of Euclidean distances from control points on one surfaces to corresponding points on the other. The strength of this approach is in the method used to determine point correspondences. It reverses the rangefinder calibration process, resulting in equations which can be used to directly compute the location of a point in a range image corresponding to an arbitrary point in 3D space. A stochastic optimization technique, very fast simulated reannealing (VFSR), is used to minimize the cost function. Dual-view registration experiments yielded excellent results in very reasonable time. A multiview registration experiment took a long time. A complete surface model was then constructed from the integration of multiple partial views. The effectiveness with which registration of range images can be accomplished makes this method attractive for many practical applications where surface models of 3D objects must be constructed     

基于局部指纹曲面片的点云三维物体识别

提出一种新的基于局部描述符的点云物体识别算法。算法根据点云的位置信息提取出邻域以及曲率信息,进而得到形状索引信息。根据形状索引提取到特征点,在每个特征点根据样条拟合原理得到测地距离和矢量夹角分割曲面得到曲面片集。每个曲面片的等距测地线构成了曲面片指纹,通过矢量和半径的变化描述,可以把每个模型物体得到的曲面片集描述存入数据库。对于给定的一个物体,根据上面步骤同样得到其曲面片集描述,通过和数据库中模型物体曲面片集的比对,得到初始识别结果。对每对初始识别结果进行对应滤波后,通过最近点迭代方法得到最终的识别结果。最后通过具体的实验说明了算法的有效性和高效性。     

A complete and extendable approach to visual recognition

A framework for 3D object recognition is presented. Its flexibility and extensibility are accomplished through a uniform, parallel, and modular recognition architecture. Concurrent and stacked parameter transforms reconstruct a variety of features from the input scene. At each stage, constraint satisfaction networks collect and fuse the evidence obtained through the parameter transforms, ensuring a globally consistent interpretation of the input scene and allowing for the integration of diverse types of information. The final interpretation of the scene is a small consistent subset of the many initial hypotheses about partial features, primitive features, feature assemblies, and 3D objects computed by the various parameter transforms. A complete, integrated, and implemented system that extracts planar surfaces, patches of quadrics of revolution, and planar intersection curves of these surfaces from a depth map viewing 3D objects is described. Experimental results on the recognition behavior of the system are presented     

基于双目的三维点云数据的获取与预处理

在计算机辅助几何设计、医学诊断、物体识别与定位等领域的应用需求下,三维点云数据的获取与处理技术受到越来越多的关注。现在有多种不同的方式可以获取现实世界中物体的三维点云数据,并对数据进行相应处理。为了能够很好地对三维数据点云进行前期的预处理,首先通过双目摄像机获取物体的三维点云,并采用八叉树法对点云数据进行相应的预处理,然后在逆向工程软件中描述出来,从逆向工程软件中可以看出得到的物体与实际物体比较接近,从而可以证明所获取的点云数据可以用来描述物体,并且点云数据的处理技术是可行的。     

Object representation and recognition in shape spaces

In this paper, we describe a shape space based approach for invariant object representation and recognition. In this approach, an object and all its similarity transformed versions are identified with a single point in a high-dimensional manifold called the shape space. Object recognition is achieved by measuring the geodesic distance between an observed object and a model in the shape space. This approach produced promising results in 2D object recognition experiments: it is invariant to similarity transformations and is relatively insensitive to noise and occlusion. Potentially, it can also be used for 3D object recognition.     

A Normalization Method of Moment Invariants for 3D Objects on Different Manifolds

3D objects can be stored in computer of different describing ways, such as point set, polyline, polygonal surface and Euclidean distance map. Moment invariants of different orders may have the different magnitude. A method for normalizing moments of 3D objects is proposed, which can set the values of moments of different orders roughly in the same range and be applied to different 3D data formats universally. Then accurate computation of moments for several objects is presented and experiments show that this kind of normalization is very useful for moment invariants in 3D objects analysis and recognition.     

Strategies of Multi-view and Multi-matching for 3D Object Recognition

A multi-view representation scheme and a multi-matching strategy for 3D object recognition are described; 3D objects are represented in terms of their 2D appearances so that 2D techniques can be applied to 3D recognition. Appearances of objects in the representation scheme are further organized in a hierarchical manner so that the matching process can reduce its search space by examining only the optimal view at every level of the representation scheme. In our multi-matching strategy, the matching module is composed of four components: point matcher, string matcher, vector matcher, and chamfer matcher. Each matcher is associated with a termination rule so that impossible views can be rejected at the early stages of the matching process. Experimental results reveal that the proposed strategies are feasible for 3D object recognition.     

用于遥感图像人造目标识别的三维建模方法研究

该文研究了用于遥感图像人造地物目标识别的三维建模方法,文中分析了识别任务的特点,比较了一般的建模方法,介绍了一种基于广义锥思想的几何表示方法,并利用面向对象的技术来表示模型内部数据及其操作。     

一种基于Hausdorff距离的运动物体跟踪算法

针对视频处理中运动物体的检测和跟踪问题,提出了一种基于Hausdorff距离的目标跟踪算法。新算法提出首先采用多尺度分水岭变换获取运动物体模型,消除了传统基于分水岭变换算法存在的缺陷;然后使用部分Hausdorff距离实现后续帧中运动物体模型的匹配;最后再次使用多尺度分水岭算法完成运动物体模型的更新。实验表明,该算法可以有效地跟踪多个刚体或非刚体目标。     

基于路径轮廓的三维目标识别

骨架能更有效地反映出目标的拓扑结构和细节变化, 因而在三维目标识别中得到广泛应用, 但存在的基于骨架的识别方法均要求骨架端点位于轮廓曲线上, 并且识别精度受骨架端点排序的影响。针对该问题, 提出了一种新的基于路径轮廓的三维目标识别算法。该算法首先定义了一种新的特征点——骨切点, 并根据骨切点在轮廓曲线上的顺序关系, 对骨架端点进行排序; 然后利用路径轮廓对目标轮廓进行分割; 再构造一种新的局部不变特征, 并结合hash表以识别三维目标。实验结果表明, 该算法对存在部分遮挡或缺损的三维目标仍有较好的识别效果。