Symbol spotting in vectorized technical drawings through a lookup table of region strings

In this paper, we address the problem of symbol spotting in technical document images applied to scanned and vectorized line drawings. Like any information spotting architecture, our approach has two components. First, symbols are decomposed in primitives which are compactly represented and second a primitive indexing structure aims to efficiently retrieve similar primitives. Primitives are encoded in terms of attributed strings representing closed regions. Similar strings are clustered in a lookup table so that the set median strings act as indexing keys. A voting scheme formulates hypothesis in certain locations of the line drawing image where there is a high presence of regions similar to the queried ones, and therefore, a high probability to find the queried graphical symbol. The proposed approach is illustrated in a framework consisting in spotting furniture symbols in architectural drawings. It has been proved to work even in the presence of noise and distortion introduced by the scanning and raster-to-vector processes.     

Divide, Compress and Conquer: Querying XML via Partitioned Path-Based Compressed Data Blocks

We propose a novel partition path-based (PPB) grouping strategy to store compressed XML data in a stream of blocks. In addition, we employ a minimal indexing scheme called block statistic signature (BSS) on the compressed data, which is a simple but effective technique to support evaluation of selection and aggregate XPath queries of the compressed data. We present a formal analysis and empirical study of these techniques. The BSS indexing is first extended into effective cluster statistic signature (CSS) and multiple-cluster statistic signature (MSS) indexing by establishing more layers of indexes. We analyze how the response time is affected by various parameters involved in our compression strategy such as the data stream block size, the number of cluster layers, and the query selectivity. We also gain further insight about the compression and querying performance by studying the optimal block size in a stream, which leads to the minimum processing cost for queries. The cost model analysis provides a solid foundation for predicting the querying performance. Finally, we demonstrate that our PPB grouping and indexing strategies are not only efficient enough to support path-based selection and aggregate queries of the compressed XML data, but they also require relatively low computation time and storage space when compared with other state-of-the-art compression strategies.     

NeTra: A toolbox for navigating large image databases

We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time of ingest into the database, and image attributes that represent each of these regions are computed. In addition to image segmentation, other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as “retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper of the image”, where the individual objects could be regions belonging to different images. A Java-based web implementation of NeTra is available at http://vivaldi.ece.ucsb.edu/Netra.     

Minimal representations of 3D models in terms of image parameters under calibrated and uncalibrated perspective

Indexing is a well-known paradigm for object recognition. In indexing, each 3D model is represented as the set of values assumed by a given vector of image parameters in correspondence to all the possible images of the 3D model. An open problem, posed by Jacobs (1992), concerned the minimum dimensionality of such sets under perspective. This paper proves that, under calibrated or uncalibrated perspective, the minimum dimensionality of the set representing any 3D modeled point-set is two. Two-dimensional representations are found also for 3D curved objects.     

Space and time bounds on indexing 3D models from 2D images

Model-based visual recognition systems often match groups of image features to groups of model features to form initial hypotheses, which are then verified. In order to accelerate recognition considerably, the model groups can be arranged in an index space (hashed) offline such that feasible matches are found by indexing into this space. For the case of 2D images and 3D models consisting of point features, bounds on the space required for indexing and on the speedup that such indexing can achieve are demonstrated. It is proved that, even in the absence of image error, each model must be represented by a 2D surface in the index space. This places an unexpected lower bound on the space required to implement indexing and proves that no quantity is invariant for all projections of a model into the image. Theoretical bounds on the speedup achieved by indexing in the presence of image error are also determined, and an implementation of indexing for measuring this speedup empirically is presented. It is found that indexing can produce only a minimal speedup on its own. However, when accompanied by a grouping operation, indexing can provide significant speedups that grow exponentially with the number of features in the groups     

Video retrieval of near-duplicates using κ-nearest neighbor retrieval of spatio-temporal descriptors

This paper describes a novel methodology for implementing video search functions such as retrieval of near-duplicate videos and recognition of actions in surveillance video. Videos are divided into half-second clips whose stacked frames produce 3D space-time volumes of pixels. Pixel regions with consistent color and motion properties are extracted from these 3D volumes by a threshold-free hierarchical space-time segmentation technique. Each region is then described by a high-dimensional point whose components represent the position, orientation and, when possible, color of the region. In the indexing phase for a video database, these points are assigned labels that specify their video clip of origin. All the labeled points for all the clips are stored into a single binary tree for efficient -nearest neighbor retrieval. The retrieval phase uses video segments as queries. Half-second clips of these queries are again segmented by space-time segmentation to produce sets of points, and for each point the labels of its nearest neighbors are retrieved. The labels that receive the largest numbers of votes correspond to the database clips that are the most similar to the query video segment. We illustrate this approach for video indexing and retrieval and for action recognition. First, we describe retrieval experiments for dynamic logos, and for video queries that differ from the indexed broadcasts by the addition of large overlays. Then we describe experiments in which office actions (such as pulling and closing drawers, taking and storing items, picking up and putting down a phone) are recognized. Color information is ignored to insure independence of action recognition to people's appearance. One of the distinct advantages of using this approach for action recognition is that there is no need for detection or recognition of body parts.     

A framework for performance characterization of intermediate-levelgrouping modules

We present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. Using these measures, we demonstrate a sound experimental framework, based on statistical ANOVA tests, to compare and contrast three edge based organization modules, namely, those of Etemadi et al. (1991), Jacobs (1996), and Sarkar-Boyer (1993) in the domain of aerial objects using 50 images. With adapted parameters, the Jacobs module performs overall the best for constraint based recognition. For fixed parameters, the Sarkar-Boyer module is the best in terms of recognition accuracy and indexing speedup. Etemadi et al.'s module performs equally well with fixed and adapted parameters while the Jacobs module is most sensitive to fixed and adapted parameter choices. The overall performance ranking of the modules is Jacobs, Sarkar-Boyer, and Etemadi et al     

An effective 3D target recognition model imitating robust methods of the human visual system

This paper presents a model of 3D object recognition motivated from the robust properties of human vision system (HVS). The HVS shows the best efficiency and robustness for an object identification task. The robust properties of the HVS are visual attention, contrast mechanism, feature binding, multi-resolution, size tuning, and part-based representation. In addition, bottom-up and top-down information are combined cooperatively. Based on these facts, a plausible computational model integrating these facts under the Monte Carlo optimization technique was proposed. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process is used to initialize parameters in a discriminative way; the top-down process is used to optimize them in a generative way. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation in visible and infrared band images.     

Data resource selection in distributed visual information systems

With the advances in multimedia databases and the popularization of the Internet, it is now possible to access large image and video repositories distributed throughout the world. One of the challenging problems in such access is how the information in the respective databases can be summarized to enable an intelligent selection of relevant database sites based on visual queries. This paper presents an approach to solve this problem based on image content-based indexing of a metadatabase at a query distribution server. The metadatabase records a summary of the visual content of the images in each database through image templates and statistical features characterizing the similarity distributions of the images. The selection of the databases is done by searching the metadatabase using a ranking algorithm that uses the query's similarity to a template and the features of the databases associated with the template. Two selection approaches, termed mean-based and histogram-based approaches, are presented. The database selection mechanisms have been implemented in a metaserver, and extensive experiments have been performed to demonstrate the effectiveness of the database selection approaches     

Indexing animated objects using spatiotemporal access methods

We present an approach for indexing animated objects and efficiently answering queries about their position in time and space. In particular, we consider an animated movie as a spatiotemporal evolution. A movie is viewed as an ordered sequence of frames, where each frame is a 2D space occupied by the objects that appear in that frame. The queries of interest are range queries of the form, "find the objects that appear in area S between frames f_i and f_j^" as well as nearest neighbor queries such as, "find the q nearest objects to a given position A between frames f_i and f_j^". The straightforward approach to index such objects considers the frame sequence as another dimension and uses a 3D access method (such as an R-Tree or its variants). This, however, assigns long "lifetime" intervals to objects that appear through many consecutive frames. Long intervals are difficult to cluster efficiently in a 3D index. Instead, we propose to reduce the problem to a partial-persistence problem. Namely, we use a 2D access method that is made partially persistent. We show that this approach leads to faster query performance while still using storage proportional to the total number of changes in the frame evolution, What differentiates this problem from traditional temporal indexing approaches is that objects are allowed to move and/or change their extent continuously between frames. We present novel methods to approximate such object evolutions, We formulate an optimization problem for which we provide an optimal solution for the case where objects move linearly. Finally, we present an extensive experimental study of the proposed methods. While we concentrate on animated movies, our approach is general and can be applied to other spatiotemporal applications as well     

基于处理-分裂-合并的彩色图像自动分割系统

叙述一个基于处理-分裂-合并的彩色图像自动分割处理系统。其主要功能为：根据图像的区域特征将彩色图像自动分割成若干块并存储区域的边界特征，实现了地图的面查询，并可实现原图和分割出来的区域图的放大和缩小显示。采用适当的去噪、平滑方法使区域图像分割更精确。本系统采用面向对象的分析设计方法，在Windows平台上，进行编程开发实现。系统对GIS、图像数据库及其检索和图像及文字模式识别的研究有较重要的意义。     

三维物体的形态图表达方法

三维物体的表达方法是计算机视觉中的关键问题之一，现有的各种三维物体表达方法虽然在各自的识别中得到应用，但都存在各自的局限性，用形态图表达三维物体是一种以视点为中心的表达方法，由于它列举了一个物体所有可能的“定性”形象，即它可使用最少的二维投影线图（特征视图）来表达一个完整的三维物体，因此使三维物体识别转化为2D与2D的匹配，该文首先定义了二维线图拓扑结构等价的判别准则，然后给出了构造透明物体形态图的方法，最后根据拓扑结构等价准则来得到不透明物体的形态图和特征图，并用圆锥与圆柱相交的实例进行了验证。     

Integration of Multiple Feature Groups and Multiple Views into a 3D Object Recognition System

This paper proposes two approaches for utilizing the information in multiple entity groups and multiple views to reduce the number of hypotheses passed to the verification stage in a model-based object recognition system employing invariant feature indexing (P. J. Flynn and A. K. Jain, CVGIP: Image Understand. 55(2), 1992, 119-129). The first approach is based on a majority voting scheme that keeps track of the number of consistent votes cast by prototype hypotheses for particular object models. The second approach examines the consistency of estimated object pose from multiple groups of entities (surfaces) in one or more views. A salient feature of our system and experiment design compared to most existing 3D object recognition systems is our use of a large object database and a large number of test images. Monte Carlo experiments employing 585 single-view synthetic range images and 117 pairs of synthetic range images with a large CAD-based 3D object database (P. J. Flynn and A. K. Jain, IEEE Trans. Pattern Anal. Mach. Intell. 13(2), 1991, 114-132) show that a large number of hypotheses (about 60% for single views and 90% for multiple views on average) can be eliminated through use of these approaches. The techniques have also been tested on several real 3D objects sensed by a Technical Arts 100X range scanner to demonstrate a substantial improvement in recognition time.     

图像中物体空间关系的表示及在检索中的应用

基于图像中物体之间的空间关系的图像检索往往受困于待处理的图像中物体种类和空间位置难以自动准确地获取。文中基于物体识别算法的输出,提出一种对物体空间关系的三元组表示法,给出基于这种表示方法对图像索引、相似度计算和检索排序的方法及允许用户使用查询词和空间关系表达查询需求的二维输入界面,并实现原型系统。这种表示法具有良好的鲁棒性,可容忍物体识别算法一定程度的误差,将物体识别得到的置信度加入三元组表示法置信度计算和排序算法中,减少物体识别结果误差对检索性能的影响。在原型系统上的实验表明,该系统在实验中对包含物体位置关系的检索给出更准确的结果,在NDCG@m、MAP、F@m上均优于现有系统。