工程三视图的三维恢复

本文在总结边界表示法（ＢＲ）的结构式立体几何表示法（ＣＳＧＲ）重建三维图形的基础上，提出了一种基于两者结合的三维重建方法。采用ＢＲ方法，把工程图纸的二维矢量化信息作为原始数据，逐步恢复三维点，三维边，面及ＣＳＧＲ意义下的基元体。采用ＣＳＧＲ方法，组合基元体，最终生成复合体，重建三维图形。算法以多面体为研究对象，在ＷＩＮＤＯＷ５３．０环境下实现。     

三维实体重建时投影图数据的预处理

介绍三维实体重建过程中投影图数据的处理算法，绘制同一图层的图形时，视图分割算法添加平行于两坐标轴的辅助直线，移动辅助线使其将三视图分割开来；对基于同一坐标系的投影三视图数据，推出从设备坐标系到各视图坐标系的转换公式并为其设计了转换算法，该算法求出三视图坐标系共有的原点，再根据视图分割的结果将每一图形元素转换到新的坐标系统中；隐含点的求取算法先判断同一视图中图元间的位置关系，再求出它们之间的公共点，这些点将用于线框搜索过程中，以上算法处理的投影图点线数据分别存入点线链表中。     

Geometric operations for visual reasoning of a solid from orthographic projections

The ability of an engineer to visualize and reason about geometric aspects of physical objects and processes is crucial to the success of engineering activities. The article describes the development of geometric operations for a visual reasoning instructional software system using so-called missing view problem. The missing view problem requires construction of a valid solid given two orthographic projections. Sweeping operations, which are the reverse procedure of orthographic projection, are used to solve missing view problems by forming the boundary faces of a solid from the loops of orthographic views. Specifically, this geometric operation which forms a primary user interface of the system is described in detail in the article.     

生成当前视点目标图像的快速逆映射算法

利用极线的属性及深度图像隐含的边界信息,提出了一种可以处理非深度连续图像的快速逆映射算法,以从多幅参考图像精确合成当前视点目标图像.该算法分为3步:首先由参考图像的深度信息得到图像中的边界;然后从参考图像中选出一幅作为主参考图像,利用极线的整体匹配特性和对应点在极线上分布的单调性,逐个处理目标极线,以生成目标图像;最后根据其他参考图像填补目标图像中的空洞.由于在第2步中只需处理参考图像的边界点对即可得到对应目标极线上所有点的深度信息及其在参考极线上的对应点,因此,新算法很好地提高了速度.在填补空洞时,利用参考图像的边界信息及所隐含的遮挡关系,还提出了一些加速的方法.     

Automatic Road Extraction from Aerial Images

The paper presents a knowledge-based method for automatic road extraction from aerial photography and high-resolution remotely sensed images. The method is based on Marr's theory of vision, which consists of low-level image processing for edge detection and linking, mid-level processing for the formation of road structure, and high-level processing for the recognition of roads. It uses a combined control strategy in which hypotheses are generated in a bottom-up mode and a top-down process is applied to predict the missing road segments. To describe road structures a generalized antiparallel pair is proposed. The hypotheses of road segments are generated based on the knowledge of their geometric and radiometric properties, which are expressed as rules in Prolog. They are verified using part–whole relationships between roads in high-resolution images and roads in low-resolution images and spatial relationships between verified road segments. Some results are presented in this paper.     

基于道路分布的移动对象动态组合索引方法

交通管理信息服务中需要高效的索引方法来管理移动对象。针对这一需求,提出了一种基于道路分布的移动对象动态组合索引结构。在此结构的基础上,采用了自底向上的动态更新算法和路段连接方法,提高了移动对象的更新和访问效率。通过实验验证了该组合索引具有更优的更新和查询性能。     

Construction of 3D solid objects from orthographic views

An algorithm to generate three-dimensional solid objects, made up of planar surfaces, from the given three conventional engineering orthographic views is presented in this paper. Consisting of six major steps, the algorithm has been programmed in C on IRIS 1400 graphics workstation. The algorithm generates all possible solutions. The infinite space has been divided into finite subspaces by making use of the surface normals and the direction of travel of the edges that connect the faces. Classification of the probable 3D subobjects into the certain and uncertain ones has proved to be very useful in reducing the time taken by the algorithm. Several illustrative examples, simple as well as complex giving single and multiple solutions, are included.     

Three-dimensional object recognition from single two-dimensional images

A computer vision system has been implemented that can recognize three-dimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model-based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision.     

Solid reconstruction from orthographic opaque views using incremental extrusion

The reconstruction of 3-D solids from 2-D projections is an important research topic in reverse engineering. The reconstruction can be grouped into two categories: single-view approach and multiple-view approach. Each approach can be classified as wireframe, BRep or CSG. However, not many CSG approaches have been reported in the literature. The methods are also restricted to uniform-thickness objects or require user interaction. The method proposed in this paper employs the CSG approach. A 3-D solid computer model is reconstructed from 2-D line drawings of six orthographic opaque views, viz. top, front, left, right, bottom and rear views. Firstly, the six views are grouped into three pairs. For each pair of views, segmented areas from one of the two views (called g-view) is incrementally extruded according to the information in the neighbouring view (called d-view). Extrusion primitive solids are generated during the incremental extrusion. All primitive solids are then unioned into an extrusion solid. Finally, all extrusion solids are intersected to give a unique 3-D solution object.     

Solid reconstruction using recognition of quadric surfaces from orthographic views

The reconstruction of 3D objects from 2D orthographic views is crucial for maintaining and further developing existing product designs. A B-rep oriented method for reconstructing curved objects from three orthographic views is presented by employing a hybrid wire-frame in place of an intermediate wire-frame. The Link-Relation Graph (LRG) is introduced as a multi-graph representation of orthographic views, and quadric surface features (QSFs) are defined by special basic patterns of LRG as well as aggregation rules. By hint-based pattern matching in the LRGs of three orthographic views in an order of priority, the corresponding QSFs are recognized, and the geometry and topology of quadric surfaces are recovered simultaneously. This method can handle objects with interacting quadric surfaces and avoids the combinatorial search for tracing all the quadric surfaces in an intermediate wire-frame by the existing methods. Several examples are provided.     

Shape Based Detection and Top-Down Delineation Using Image Segments

We introduce a segmentation-based detection and top-down figure-ground delineation algorithm. Unlike common methods which use appearance for detection, our method relies primarily on the shape of objects as is reflected by their bottom-up segmentation. Our algorithm receives as input an image, along with its bottom-up hierarchical segmentation. The shape of each segment is then described both by its significant boundary sections and by regional, dense orientation information derived from the segment’s shape using the Poisson equation. Our method then examines multiple, overlapping segmentation hypotheses, using their shape and color, in an attempt to find a “coherent whole,” i.e., a collection of segments that consistently vote for an object at a single location in the image. Once an object is detected, we propose a novel pixel-level top-down figure-ground segmentation by “competitive coverage” process to accurately delineate the boundaries of the object. In this process, given a particular detection hypothesis, we let the voting segments compete for interpreting (covering) each of the semantic parts of an object. Incorporating competition in the process allows us to resolve ambiguities that arise when two different regions are matched to the same object part and to discard nearby false regions that participated in the voting process. We provide quantitative and qualitative experimental results on challenging datasets. These experiments demonstrate that our method can accurately detect and segment objects with complex shapes, obtaining results comparable to those of existing state of the art methods. Moreover, our method allows us to simultaneously detect multiple instances of class objects in images and to cope with challenging types of occlusions such as occlusions by a bar of varying size or by another object of the same class, that are difficult to handle with other existing class-specific top-down segmentation methods.     

Single and Multiple View Support Order Prediction in Clutter for Manipulation

Robotic manipulation of objects in clutter remains a challenging problem to date. The challenge is posed by various levels of complexity involved in interaction among objects. Understanding these semantic interactions among different objects is important to manipulate in complex settings. It can play a significant role in extending the scope of manipulation to cluttered environment involving generic objects, and both direct and indirect physical contact. In our work, we aim at learning semantic interaction among objects of generic shapes and sizes lying in clutter involving physical contact. We infer three types of support relationships: “support from below”, “support from side”, and “containment”. Subsequently, the learned semantic interaction or support relationship is used to derive a sequence or order in which the objects surrounding the object of interest should be removed without causing damage to the environment. The generated sequence is called support order. We also extend understanding of semantic interaction from single view to multiple views and predict support order in multiple views. Using multiple views addresses those cases that are not handled when using single view such as scenarios of occlusion or missing support relationships. We have created two RGBD datasets for our experiments on support order prediction in single view and multiple views respectively. The datasets contains RGB images, point clouds and depth maps of various objects used in day-to-day life present in clutter with physical contact and overlap. We captured many different cluttered settings involving different kinds of object-object interaction and successfully learned support relationship and performed Support Order Prediction in these settings.     

Labeling Line Drawings with Hidden-Part-Drawn of Planar Object with Trihedral Vertices

Although line drawings consist of only line segments on a plane, they convey much information about the three-dimensional object structures. For a computer interpreting line drawings, some intelligent mechanism is required to extract three-dimensional information from the two-dimensional line drawings. In this paper, a new labeling theory and method are proposed for the two-dimensional line drawing with hidden-part-draw of a three-dimensional planar object with trihedral vertices. Some rules for labeling line drawing are established. There are 24 kinds of possible junctions for line drawing with hidden-part-draw, in which there are 8 possible Y and 16 W junctions. The three problems are solved that Sugihara's line drawing labeling technique exists. By analyzing the projections of the holes in manifold planar object, we have put forward a labeling method for the line drawing. Our labeling theory and method can discriminate between correct and incorrect hidden-part-draw natural line drawings. The hidden-part-draw natural line drawings can be labeled correctly by our labeling theory and method, whereas the labeling theory of Sugihara can only label the hidden-part-draw unnatural line drawings in which some visible lines must be drawn as hidden lines, and some invisible lines must be drawn as continuous lines.     

Estimating motion and structure from correspondences of linesegments between two perspective images

Presents an algorithm for determining 3D motion and structure from correspondences of line segments between two perspective images. To the author's knowledge, this paper is the first investigation of use of line segments in motion and structure from motion. Classical methods use their geometric abstraction, namely straight lines, but then three images are necessary for the motion and structure determination process. In this paper the author shows that it is possible to recover motion from two views when using line segments. The assumption used is that two matched line segments contain the projection of a common part of the corresponding line segment in space, i.e., they overlap. Indeed, this is what the author uses to match line segments between different views. This assumption constrains the possible motion between two views to an open set in motion parameter space. A heuristic, consisting of maximizing the overlap, leads to a unique solution. Both synthetic and real data have been used to test the proposed algorithm, and excellent results have been obtained with real data containing a relatively large set of line segments     

Using B<Superscript>+</Superscript>-trees for processing of line segments in large spatial databases

Points, lines, and regions are the three basic entities for constituting vector-based objects in spatial databases. Many indexing methods (G-tree, K-D-B tree, Quad-tree, PMR-tree, Grid-file, R-tree, and so on) have been widely discussed for handling point or region data. These traditional methods can efficiently organize point or region objects in a space into a hashing or hierarchical directory. They provide efficient access methods to meet the requirement of accurate retrievals. However, two problems are encountered when their techniques are applied to deal with line segments. The first is that representing line segments by means of point or region objects cannot exactly and properly preserve the spatial information about the proximities of line segments. The second problem is derived from the large dead space and overlapping areas in external and internal nodes of the hierarchical directory caused by the use of rectangles to enclose line objects. In this paper, we propose an indexing structure for line segments based on B ⁺ -tree to remedy these two problems. Through the experimental results, we demonstrate that our approach has significant improvement over the storage efficiency. In addition, the retrieval efficiency has also been significantly prompted as compared to the method using R-tree index scheme. These improvements derive mainly from the proposed data processing techniques and the new indexing method.     

Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run     

Determining interference between pairs of solids defined constructively in computer animation

This paper presents theory and implementation of a method for detecting interference between a pair of solid objects. Often at times, when performing simulations, two solids may unwittingly interpenetrate each other. The two components of the system presented in this paper are: (1) a surface representation method to model solid objects; and (2) a method for detecting interference. Body representation of a solid in this system is based upon enveloping each solid with surfaces (called positive entities). Most computer aided design (CAD) systems use solid modeling techniques to represent solid objects. Since most solid models use Boolean operations to model complex objects, a method is presented to envelop complex objects with parametric surfaces. A method for tracing intersection curves between two surfaces is also presented. Discontinuities on surfaces are defined as negative entitics in order to extend the method to complex solids. Determining interference is based upon a numerical algorithm for computing points of intersection between boundary curves and parametrized entities. The existence of segments of these curves inside the boundary of positive and negative entities is established by computing the circulation of a function around the boundary curve. Interference between two solids is then detected. No limitations are imposed on the convexity or simplicity of the boundary curves treated.     

基于三视图的三维形体重建技术

基于工程图纸的三维形体的重建技术是根据形体的二维视图中的几何信息和拓扑信息,生成相应的三维形体,这是一个从爸维到高维的构造过程,文中提出了一个三维形体重建算法,该算法首先根据三视图生成形体的线框图,然后应用左邻边搜索策略求出线框图内的所有面及相应的极小环,最后利用Ｍｏｅｂｉｕｓ规则及工程图的性质,删除重建过程中生成的非法元素,该方法利用二几何基元和三维几何基元的几何性质及生成关系,减少了重建过程中     

Familiarity based unified visual attention model for fast and robust object recognition

Even though visual attention models using bottom-up saliency can speed up object recognition by predicting object locations, in the presence of multiple salient objects, saliency alone cannot discern target objects from the clutter in a scene. Using a metric named familiarity, we propose a top-down method for guiding attention towards target objects, in addition to bottom-up saliency. To demonstrate the effectiveness of familiarity, the unified visual attention model (UVAM) which combines top-down familiarity and bottom-up saliency is applied to SIFT based object recognition. The UVAM is tested on 3600 artificially generated images containing COIL-100 objects with varying amounts of clutter, and on 126 images of real scenes. The recognition times are reduced by 2.7× and 2×, respectively, with no reduction in recognition accuracy, demonstrating the effectiveness and robustness of the familiarity based UVAM.     

Geometric Image Parsing in Man-Made Environments

We present a new optimization based parsing framework for the geometric analysis of a single image coming from a man-made environment. This framework models the scene as a composition of geometric primitives spanning different layers from low level (edges) through mid-level (lines segments, lines and vanishing points) to high level (the zenith and the horizon). The inference in such a model thus jointly and simultaneously estimates (a) the grouping of edges into the line segments, (b) the grouping of line segments into the straight lines, (c) the grouping of lines into parallel families, and (d) the positioning of the horizon and the zenith in the image. Such a unified treatment means that the uncertainty information propagates between the layers of the model. This is in contrast to most previous approaches to the same problem, which either ignore the middle levels (line segments or lines) all together, or use the bottom-up step-by-step pipeline.