一种利用形状片段的物体检测方法

针对物体检测中传统方法计算量大, 对复杂背景敏感, 且大部分物体检测方法只能得到物体所在区域而不能精确定位物体边缘等问题, 本文提出一种基于形状片段特征的物体检测方法. 该方法首先根据训练集得到具有多尺度特性的物体形状片段模型; 对测试图像按照和模型中边缘片段提取一致方法, 根据形状片段间的相似度, 选取出候选形状片段; 然后根据候选形状片段估计出模型中的片段与测试图像中片段之间的旋转角; 最后结合具有全局约束的概率Hough变换, 将物体检测问题转换为Hough空间概率问题; 根据Hough空间求解出的物体中心位置, 对候选形状片段验证, 得到实际物体轮廓片段. 理论分析和实验结果表明, 本文提出的算法具有较好的效果.     

Shapes recognition using the straight line Hough transform: theoryand generalization

A shape matching technique based on the straight line Hough transform (SLHT) is presented. In the &thetas;-ρ space, the transform can be expressed as the sum of the translation term and the intrinsic term. This formulation allows the translation, rotation, and intrinsic parameters of the curve to be easily decoupled. A shape signature, called the scalable translation invariant rotation-to-shifting (STIRS) signature, is obtained from the &thetas;-ρ space by computing the distances between pairs of points having the same &thetas; value. This signature is invariant to translation and can be easily normalized, and rotation in the image space corresponds to circular shifting of the signature. Matching two signatures only amounts to computing a 1D correlation. The height and location of a peak (if it exists) indicate the similarity and orientation of the test object with respect to the reference object. The location of the test object is obtained, once the orientation is known, by an inverse transform (voting) from the &thetas;-ρ space to the x-y plane     

基于局部坐标系和哈希技术的空间曲线匹配算法

针对三维物体识别领域中的问题,提出了一种基于局部坐标系和哈希技术的空间曲线匹配算法,该方法通过提取一条曲线的恒定特征点,构造局部坐标系;然后再计算局部坐标系中的相似不变量,构造哈希表;采哈希技术对这些不变量进行比较,达到匹配曲线的目的。此算法应用于计算机辅助文物复原系统中,经实验表明,给所方法具有运行稳定,高效和适用性强等优点。     

Object detection by global contour shape

We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets.     

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     

A Neural Network Approach to Planar-Object Recognition in 3D Space

Most existing 2D object recognition algorithms are not perspective (or projective) invariant, and hence are not suitable for many real-world applications. By contrast, one of the primary goals of this research is to develop a flat object matching system that can identify and localise an object, even when seen from different viewpoints in 3D space. In addition, we also strive to achieve good scale invariance and robustness against partial occlusion as in any practical 2D object recognition system. The proposed system uses multi-view model representations and objects are recognised by self-organised dynamic link matching. The merit of this approach is that it offers a compact framework for concurrent assessments of multiple match hypotheses by promoting competitions and/or co-operations among several local mappings of model and test image feature correspondences. Our experiments show that the system is very successful in recognising object to perspective distortion, even in rather cluttered scenes. Receiveed: 29 May 1998?,Received in revised form: 12 October 1998?Accepted: 26 October 1998     

Classification of silhouettes using contour fragments

In this paper, we propose a fragment-based approach for classification and recognition of shape contours. According to this method, first the perceptual landmarks along the contours are localized in a scale invariant manner, which makes it possible to extracts the contour fragments. Using a predefined dictionary for the fragments, these landmarks and the parts between them are transformed into a symbolic representation that is a compact representation. Using a string kernel-like approach, an invariant high-dimensional feature space is created from the symbolic representation and later the most relevant lower dimensions are extracted by principal component analysis. Finally, support vector machine is used for classification of the feature space. The experimental results show that the proposed method has similar performance to the best approaches for shape recognitions while it has lower complexity.     

Roof plane extraction from airborne lidar point clouds

Planar patches are important primitives for polyhedral building models. One of the key challenges for successful reconstruction of three-dimensional (3D) building models from airborne lidar point clouds is achieving high quality recognition and segmentation of the roof planar points. Unfortunately, the current automatic extraction processes for planar surfaces continue to suffer from limitations such as sensitivity to the selection of seed points and the lack of computational efficiency. In order to address these drawbacks, a new fully automatic segmentation method is proposed in this article, which is capable of the following: (1) processing a roof point dataset with an arbitrary shape; (2) robustly selecting the seed points in a parameter space with reduced dimensions; and (3) segmenting the planar patches in a sub-dataset with similar attributes when region growing in the object space. The detection of seed points in the parameter space was improved by mapping the accumulator array to a 1D space. The range for region growing in the object space was reduced by an attribute similarity measure that split the roof dataset into candidate and non-candidate subsets. The experimental results confirmed that the proposed approach can extract planar patches of building roofs robustly and efficiently.     

Model-based recognition of 3D objects from single images

In this work, we treat major problems of object recognition which have received relatively little attention lately. Among them are the loss of depth information in the projection from a 3D object to a single 2D image, and the complexity of finding feature correspondences between images. We use geometric invariants to reduce the complexity of these problems. There are no geometric invariants of a projection from 3D to 2D. However, given certain modeling assumptions about the 3D object, such invariants can be found. The modeling assumptions can be either a particular model or a generic assumption about a class of models. Here, we use such assumptions for single-view recognition. We find algebraic relations between the invariants of a 3D model and those of its 2D image under general projective projection. These relations can be described geometrically as invariant models in a 3D invariant space, illuminated by invariant “light rays,” and projected onto an invariant version of the given image. We apply the method to real images     

户外交通标志检测和形状识别

提出了一种新的识别户外不同交通标志形状的算法。为了减少数字噪声的影响和分离提取独立的交通标志形状,基于颜色分割输出的外边缘可通过离散曲线演变进行简化和分解。正切空间中弧线的相似程度决定离散曲线演变的程度。形状的识别是通过模板匹配来实现的,待识别形状与模板之间的最小几何差异决定形状的类别。实验结果表明本算法是平移、旋转和尺度恒定的,能够在复杂的交通场景中进行可靠的形状识别。     

Detection and recognition of contour parts based on shape similarity

Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, it is usually impossible to extract complete object contours or to segment the whole objects. However, in many cases parts of contours can be correctly reconstructed either by performing edge grouping or as parts of boundaries of segmented regions. Therefore, recognition of objects based on their contour parts seems to be a promising as well as a necessary research direction.The main contribution of this paper is a system for detection and recognition of contour parts in digital images. Both detection and recognition are based on shape similarity of contour parts. For each contour part produced by contour grouping, we use shape similarity to retrieve the most similar contour parts in a database of known contour segments. A shape-based classification of the retrieved contour parts performs then a simultaneous detection and recognition.An important step in our approach is the construction of the database of known contour segments. First complete contours of known objects are decomposed into parts using discrete curve evolution. Then, their representation is constructed that is invariant to scaling, rotation, and translation.     

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.     

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.     

仿射不变的自适应局部线性嵌入

目的: 为将流形学习有效应用于图像的降维与识别中,并消除图像的仿射变换对流形结构产生的影响,本文提出一种仿射不变的自适应局部线性嵌入算法。方法: 该算法在局部线性嵌入的基础上,为适应产生各种仿射变换的图像样本,引入切线距离计算各样本之间的相似程度,以此描述样本空间中的距离,并通过图像相似度函数自适应计算样本空间中每一点的邻域数量。结果: 实验结果表明,该算法能够构造出更合理的低维流形结构,并有效提升统计识别的正确率。结论: 本文算法对仿射变换不敏感,表现出更强的稳健性。     

Directed mean Hausdorff distance of parameterized freeform shapes in 3D: a case study

Shape matching in 3D is a process underlying several applications, including engineering, medicine, and animation. Searching, recognition, and matching of shape patterns directly in 3D model-space is far from a mature technique. In this paper we report an investigation of the computation of shape similarity based on Hausdorff-like measures. We have assessed a number of properties of the directed Hausdorff distance between 3D shapes, namely accuracy, robustness, and computational complexity of the algorithms. The driving application here was the reuse of digitized shapes in geometric modeling. The shapes involved in the analysis were from sampled physical object surfaces, or they were synthetic shapes. To support the application it was necessary to extend the search space beyond rigid and affine transformation spaces; a number of deformation parameters (intrinsic shape parameters) needed to be introduced. The sensitivity of the Hausdorff distance to pose parameters and to intrinsic shape parameters was investigated. Shape parameter estimation turned out to be feasible when an appropriate fitting strategy was selected. Besides a short straight ridge, ridges developing along a 3D spine can also be successfully registered. Finally, it is demonstrated how the performance of the algorithms is improved by a 3D binning technique.     

Dynamic Multi‐View Exploration of Shape Spaces

Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model models the distribution in a high dimensional shape space, where each shape is represented by a single point. We present a design study on the intuitive exploration and visualization of shape spaces and shape models. Our approach focuses on the dual‐space nature of these spaces. The high‐dimensional shape space represents the population, whereas object space represents the shape of the 3D object associated with a point in shape space. A 3D object view provides local details for a single shape. The high dimensional points in shape space are visualized using a 2D scatter plot projection, the axes of which can be manipulated interactively. This results in a dynamic scatter plot, with the further extension that each point is visualized as a small version of the object shape that it represents. We further enhance the population‐object duality with a new type of view aimed at shape comparison. This new “shape evolution view” visualizes shape variability along a single trajectory in shape space, and serves as a link between the two spaces described above. Our three‐view exploration concept strongly emphasizes linked interaction between all spaces. Moving the cursor over the scatter plot or evolution views, shapes are dynamically interpolated and shown in the object view. Conversely, camera manipulation in the object view affects the object visualizations in the other views. We present a GPU‐accelerated implementation, and show the effectiveness of the three‐view approach using a number of real‐world cases. In these, we demonstrate how this multi‐view approach can be used to visually explore important aspects of a statistical shape model, including specificity, compactness and reconstruction error.