基于投影直方图匹配的双目视觉跟踪算法

为了在机器人机械手双目视觉伺服系统中跟踪并精确定位目标的空间位置,提出了一种利用投影直方图匹配和极线几何约束的目标跟踪方法。分别在2个视觉中人工标定目标,并提取目标在多颜色空间的水平、垂直投影直方图作为匹配模板;在一个视觉中利用目标的运动一致性原则和投影直方图匹配搜索并跟踪目标;在另一个视觉中依据双目视觉系统的极线几何原理限定目标搜索范围,搜索并定位目标。该方法利用水平、垂直投影直方图描述目标的结构信息,同时完成了双目视觉系统中的目标跟踪与配准功能,有利于目标的精确定位和视觉测量。实验结果表明,该方法可在双目视觉系统中有效跟踪目标,运算效率高,鲁棒性强。     

Image indexing and retrieval based on color histograms

While general object recognition is difficult, it is relatively easy to capture various primitive properties such as color distributions, prominent regions and their topological features from an image and use them to narrow down the search space when attempts to retrieving images by contents from an image database are made.In this paper, we present an image database in which images are indexed and retrieved based on color histograms. We first address the problems inherent in color histograms created by the conventional method, and then propose a new method to create histograms which are compact in size and insensitive to minor illumination variations such as highlight, shape, and etc. A powerful indexing scheme where each histogram of an image is encoded into a numerical key, and stored in a two-layered tree structure is introduced. This approach turns the problem of histogram matching, which is computation intensive, into index key search, so as to realize quick data access in a large scale image database. Two types of user interfaces, Query by user provided sample images, and Query by combination of the system provided templates, are provided to meet various user requests. Various experimental evaluations exhibit the effectiveness of the image database system.     

Polyhedral object recognition by indexing

In computer vision, the indexing problem is the problem of recognizing a few objects in a large database of objects while avoiding the help of the classical image-feature-to-object-feature matching paradigm. In this paper we address the problem of recognizing three-dimensional (3-D) polyhedral objects from 2-D images by indexing. Both the objects to be recognized and the images are represented by weighted graphs. The indexing problem is therefore the problem of determining whether a graph extracted from the image is present or absent in a database of model graphs. We introduce a novel method for performing this graph indexing process which is based both on polynomial characterization of binary and weighted graphs and on hashing. We describe in detail this polynomial characterization and then we show how it can be used in the context of polyhedral object recognition. Next we describe a practical recognition-by-indexing system that includes the organization of the database, the representation of polyhedral objects in terms of 2-D characteristic views, the representation of this views in terms of weighted graphs and the associated image processing. Finally, some experimental results allow the evaluation of the system performance.     

电视图象中目标检索方法

本文给出一种有效的图象目标检索方法。该方法由颜色分割和定位算法组成。颜色分割中利用样本学习和非样本交互操作，提取目标的特征颜色，并以此建立颜色索引表，以提高颜色分割速度。这位算法中利用映射技术与区域相结合的操作确定目标的大小和位置。实验结果表明，本检索算法具有正确检测和定位目标的能力。     

Efficient object identification and localization for image retrieval using query-by-region

Localizing an object within an image is a common task in the field of computer vision, and represents the first step towards the solution of the recognition problem. This paper presents an efficient approach to object localization for image retrieval using query-by-region. The new algorithm utilizes correlogram back-projection in the YCbCr chromaticity components to handle the problem of subregion querying. Utilizing similar spatial color information enables users to detect and locate primary location and candidate regions accurately without the need for further information about the number of objects. Comparing this new approach to existing methods, an improvement of 21% was observed in experimental trials. These results reveal that color correlograms are markedly more effective than color histograms for this task.     

Concurrent behavior: A construct to specify the external behavior of objects in object databases

We present a linguistic construct to define concurrency control for the objects of an object database. This construct, calledconcurrent behavior, allows to define a concurrency control specification for each object type in the database; in a sense, it can be seen as a type extension. The concurrent behavior is composed by two parts: the first one, calledcommutativity specification, is a set of conditional rules, by which the programmer specifies when two methods do not conflict each other. The second part, the constraint specification, is a set of guarded regular expressions, calledconstraints, by which the programmer defines the allowed sequences of method calls. At each time during an actual execution, a subset of constraints may be active so limiting the external behavior of the object. A constraint becomesactive when its guard is verified, where a guard is composed of the occurrence of some method callm along with the verification of a boolean expression on the object state and the actual parameters ofm. A constraint dies when a string of the language corresponding to the regular expression has been recognized. While the commutativity specification is devoted to specify the way in which the external behavior of an object is influenced by the existence of concurrent transactions in the system, the constraint specification defines the behavior of the object, independently from the transactions. Since the two parts of the concurrent behavior are syntactically distinct and, moreover, each of them consists of a set of independent rules, modularity in specifying the objects is enhanced, with respect to a unique specification. We outline an implementation of the construct, which is based on a look-ahead policy: at each method execution, we foresee the admissible successive behaviors of the object, instead of checking the admission of each request at the time it is actually made.     

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.     

Dynamic learning from multiple examples for semantic object segmentation and search

We present a novel “dynamic learning” approach for an intelligent image database system to automatically improve object segmentation and labeling without user intervention, as new examples become available, for object-based indexing. The proposed approach is an extension of our earlier work on “learning by example,” which addressed labeling of similar objects in a set of database images based on a single example. The proposed dynamic learning procedure utilizes multiple example object templates to improve the accuracy of existing object segmentations and labels. Multiple example templates may be images of the same object from different viewing angles, or images of related objects. This paper also introduces a new shape similarity metric called normalized area of symmetric differences (NASD), which has desired properties for use in the proposed “dynamic learning” scheme, and is more robust against boundary noise that results from automatic image segmentation. Performance of the dynamic learning procedures has been demonstrated by experimental results.