基于轮廓的形状射线描述模型

提出一种新的基于轮廓的形状描述和匹配方法。提取物体的轮廓并在轮廓上进行等间隔采样,利用参考点到采样点的距离、采样点处的轮廓方向及采样点间的空间关系来直观地表达目标的形状特征;通过在不同尺度、方向和位置进行最大表决来获得形状匹配的尺度、旋转和平移不变性;提出了结合局部和整体特征的相似度评分机制来实现目标的匹配和检测。实验表明,形状的射线描述模型不仅能对具有清晰轮廓的目标进行有效的检索和匹配,也可在复杂的图像背景中检测目标。     

From A Medial Surface To A Mesh

Medial surfaces are well‐known and interesting surface skeletons. As such, they can describe the topology and the geometry of a 3D closed object. The link between an object and its medial surface is also intuitively understood by people. We want to exploit such skeletons to use them in applications like shape creation and shape deformation. For this purpose, we need to define medial surfaces as Shape Representation Models (SRMs). One of the very first task of a SRM is to offer a visualization of the shape it describes. However, achieving this with a medial surface remains a challenging problem. In this paper, we propose a method to build a mesh that approximates an object only described by a medial surface. To do so, we use a volumetric approach based on the construction of an octree. Then, we mesh the boundary of that octree to get a coarse approximation of the object. Finally, we refine this mesh using an original migration algorithm. Quantitative and qualitative studies, on objects coming from digital modeling and laser scans, shows the efficiency of our method in providing high quality surfaces with a reasonable computational complexity.     

Stroke‐guided Image Synthesis for Skeletal Structure Editing

Creating variations of an image object is an important task, which usually requires manipulating the skeletal structure of the object. However, most existing methods (such as image deformation) only allow for stretching the skeletal structure of an object: modifying skeletal topology remains a challenge. This paper presents a technique for synthesizing image objects with different skeletal structures while respecting to an input image object. To apply this technique, a user firstly annotates the skeletal structure of the input object by specifying a number of strokes in the input image, and draws corresponding strokes in an output domain to generate new skeletal structures. Then, a number of the example texture pieces are sampled along the strokes in the input image and pasted along the strokes in the output domain with their orientations. The result is obtained by optimizing the texture sampling and seam computation. The proposed method is successfully used to synthesize challenging skeletal structures, such as skeletal branches, and a wide range of image objects with various skeletal structures, to demonstrate its effectiveness.     

Combinatorial manifold mesh reconstruction and optimization from unorganized points with arbitrary topology

In this paper, a novel approach is proposed to reliably reconstruct the geometric shape of a physically existing object based on unorganized point cloud sampled from its boundary surface. The proposed approach is composed of two steps. In the first step, triangle mesh structure is reconstructed as a continuous manifold surface by imposing explicit relationship among the discrete data points. For efficient reconstruction, a growing procedure is employed to build the 2-manifold directly without intermediate 3D representation. Local and global topological operations with ensured completeness and soundness are defined to incrementally construct the 2-manifold with arbitrary topology. In addition, a novel criterion is proposed to control the growing process for ensured geometric integrity and automatic boundary detection with a non-metric threshold. The reconstructed manifold surface captures the object topology with the built-in combinatorial structure and approximates the object geometry to the first order. In the second step, new methods are proposed to efficiently obtain reliable curvature estimation for both the object surface and the reconstructed mesh surface. The combinatorial structure of the triangle mesh is then optimized by changing its local topology to minimize the curvature difference between the two surfaces. The optimized triangle mesh achieves second order approximation to the object geometry and can serve as a basis for many applications including virtual reality, computer vision, and reverse engineering.     

Crawl through Neighbors: A Simple Curve Reconstruction Algorithm

Given a planar point set sampled from an object boundary, the process of approximating the original shape is called curve reconstruction. In this paper, a novel non‐parametric curve reconstruction algorithm based on Delaunay triangulation has been proposed and it has been theoretically proved that the proposed method reconstructs the original curve under ε‐sampling. Starting from an initial Delaunay seed edge, the algorithm proceeds by finding an appropriate neighbouring point and adding an edge between them. Experimental results show that the proposed algorithm is capable of reconstructing curves with different features like sharp corners, outliers, multiple objects, objects with holes, etc. The proposed method also works for open curves. Based on a study by a few users, the paper also discusses an application of the proposed algorithm for reconstructing hand drawn skip stroke sketches, which will be useful in various sketch based interfaces.     

Approximate General Sweep Boundary of a 2D Curved Object

This paper presents an algorithm to compute an approximation to the general sweep boundary of a 2D curved moving object which changes its shape dynamically while traversing a trajectory. In effect, we make polygonal approximations to the trajectory and to the object shape at every appropriate instance along the trajectory so that the approximated polygonal sweep boundary is within a given error bound ϵ > 0 from the exact sweep boundary. The algorithm interpolates intermediate polygonal shapes between any two consecutive instances, and constructs polygons which approximate the sweep boundary of the object. Previous algorithms on sweep boundary computation have been mainly concerned about moving objects with fixed shapes; nevertheless, they have involved a fair amount of symbolic and/or numerical computations that have limited their practical uses in graphics modeling systems as well as in many other applications which require fast sweep boundary computation. Although the algorithm presented here does not generate the exact sweep boundaries of objects, it does yield quite reasonable polygonal approximations to them, and our experimental results show that its computation is reasonably fast to be of a practical use.     

3D wavelet-based multiresolution object representation

This paper presents a technique for computing multiresolution shape models of 3D objects acquired as clouds of 3D points. The procedure is fully automated and is able to compute approximations for any object, overcoming sampling irregularity if present (sampling irregularity is a common feature of most 3D acquisition techniques; a typical example is stereo vision). The method described here starts by computing an intermediate mesh that meets the subdivision connectivity requirement needed to allow the computation of the wavelet transform. The mesh is then adjusted to the 3D input data using an iterative deformation process. Finally, a spherical wavelet transform is computed to obtain the object's 3D multiresolution model. This paper shows a number of real objects acquired with different techniques, including hand-held 3D digitizers. The paper also gives some examples of how multiresolution representations can be used in tasks such as acquisition noise filtering, mesh simplification and shape labelling.     

Recognition of shapes by attributed skeletal graphs

In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classification. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of different objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and effective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and occlusions.     

Example‐Based Materials in Laplace–Beltrami Shape Space

We present a novel method for flexible and efficient simulation of example‐based elastic deformation. The geometry of all input shapes is projected into a common shape space spanned by the Laplace–Beltrami eigenfunctions. The eigenfunctions are coupled to be compatible across shapes. Shape representation in the common shape space is scale‐invariant and topology‐independent. The limitation of previous example‐based approaches is circumvented that all examples must have identical topology with the simulated object. Additionally, our method allows examples that are arbitrary in size, similar but not identical in shape with the object. We interpolate the examples via a weighted‐energy minimization to find the target configuration that guides the object to desired deformation. Large deformation between examples is handled by a physically plausible energy metric. This optimization is efficient as the eigenfunctions are pre‐computed and the problem dimension is small. We demonstrate the benefits of our approach with animation results and performance analysis.     

Untangling the Blum Medial Axis Transform

For over 30 years, Blum's Medial Axis Transform (MAT) has proven to be an intriguing tool for analyzing and computing with form, but it is one that is notoriously difficult to apply in a robust and stable way. It is well documented how a tiny change to an object's boundary can cause a large change in its MAT. There has also been great difficulty in using the MAT to decompose an object into a hierarchy of parts reflecting the natural parts-hierarchy that we perceive. This paper argues that the underlying cause of these problems is that medial representations embody both the substance of each part of an object and the connections between adjacent parts. A small change in an object's boundary corresponds to a small change in its substance but may involve a large change in its connection information. The problems with Blum's MAT are generated because it does not explicitly represent this dichotomy of information. To use the Blum MAT to it's full potential, this paper presents a method for separating the substance and connection information of an object. This provides a natural parts-hierarchy while eliminating instabilities due to small boundary changes. The method also allows for graded, fuzzy classifications of object parts to match the ambiguity in human perception of many objects.     

Deformable M-Reps for 3D Medical Image Segmentation

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures—each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure.A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects.The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.     

基于模糊逻辑的多相机非重叠场景的物体匹配

An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algorithm (FLMA). The approach uses the information of object motion, shape and camera topology for matching objects across camera views. The motion and shape information of targets are obtained by tracking them using a combination of ConDensation and CAMShift tracking algorithms. The information of camera topology is obtained and used by calculating the projective transformation of each view with the common ground plane. The algorithm is suitable for tracking non-rigid objects with both linear and non-linear motion. We show videos of tracking objects across multiple cameras based on FLMA. From our experiments, the system is able to correctly match the targets across views with a high accuracy.     

A set theoretic approach for time-to-contact estimation in dynamic vision

This paper deals with the estimation of the time-to-contact in dynamic vision. It is well known that differential invariants of the image velocity field can be used to characterize the shape changes of objects in the scene, due to relative motion between the observer and the scene. Under the hypothesis of constant velocity along the optical axis, the time-to-contact turns out to be a function of the area enclosed by the object contour and its time derivative.In the paper, a novel approach based on set membership estimation theory is proposed to estimate the variables involved in the computation of the time-to-contact. Both errors in the motion model and image measurement noise are described as unknown-but-bounded disturbances, without requiring any statistical assumption. The proposed technique allows for the computation of guaranteed bounds on the time-to-contact estimates in finite time, a crucial issue in all problems where a robust evaluation of the time-to-contact is in order.     

Extracting a valid boundary representation from a segmented rangeimage

A new approach is presented for extracting an explicit 3D shape model from a single range image. One novel aspect is that the model represents both observed object surfaces, and surfaces which bound the volume of occluded space. Another novel aspect is that the approach does not require that the range image segmentation be perfect. The low-level segmentation may be such that the model-building process encounters topology versus geometry conflicts. The model-building process is designed to be “fail soft” in the face of such problems. The portion of the 3D model where a problem presents itself is “glued” together in a manner meant to minimize the disturbance in the 3D shape. The goal is to produce a valid boundary-representation which can be processed by higher-level routines. A third novel aspect of this work is that the implementation has been evaluated on over 200 real range images of polyhedral objects, with no operator intervention and all parameters held constant, and obtained a 97% success rate in creating valid b-reps