THE IMAGE TORQUE OPERATOR FOR MID-LEVEL VISION: THEORY AND EXPERIMENT

A problem central to visual scene understanding and computer vision is to extract semantically meaningful parts of images. A visual scene consists of objects, and the objects and parts of objects are delineated from their surrounding by closed contours. In this thesis a new bottom-up visual operator, called the Torque operator, which captures the concept of closed contours is introduced. Its computation is inspired by the mechanical definition of torque or moment of force, and applied to image edges. It takes as input edges and computes over regions of different size a measure of how well the edges are aligned to form a closed, convex contour. The torque operator is by definition scale independent, and can be seen as an operator of mid-level vision that captures the organizational concept of 'closure' and grouping mechanism of edges. In this thesis, fundamental properties of the torque measure are studied, and experiments are performed to demonstrate and verify that it can be made a useful tool for a variety of applications, including visual attention, segmentation, and boundary edge detection

Ego-Motion Estimation using Fewer Image Feature Points

Environmental recognition using images is a worthwhile research subject. 3D information is valuable information to recognize surrounding, because currently it is difficult for machines to understand 3D structure in the scene with only one image, even though a human can understand the scene structure from a picture. 3D information can be obtained from stereo and motion disparities. Since the stereo camera is assumed to be calibrated, the 3D shape can be calculated from disparities. On the other ahnd, since the camera motion, so called ego-motion, is not known, even though motion disparities is obtained,3D shape cannot be calculated until ego-motion is estimated. In addition, the 3D shape recovered from motion disparities is determied up to a scale. Generally, the accuracy of 3D reconstruction by stereo camera depends on the baseline. A stereo camera usually cannot take long baseline, while camera motion can produce long baseline. Since each stereo and motion has information on 3D shape, the combination of stereo and motion disparities could complementally produce better 3D reconstruction than only one of them. Researches in this report were taken placed toward the 3D reconstruction using stereo and motion information

The image torque operator: A new tool for mid-level vision

Contours are a powerful cue for semantic image understanding. Objects and parts of objects in the image are delineated from their surrounding by closed contours which make up their boundary. In this paper we introduce a new bottom-up visual operator to capture the concept of closed contours, which we call the ’Torque ’ operator. Its computation is inspired by the mechanical definition of torque or moment of force, and applied to image edges. The torque operator takes as input edges and computes over regions of different size a measure of how well the edges are aligned to form a closed, convex contour. We explore fundamental properties of this measure and demonstrate that it can be made a useful tool for visual attention, segmentation, and boundary edge detection by verifying its benefits on these applications. 1

Visualizing Archival Collections

The proposed project will build on the research and prototype development work done in the creation of ArchivesZ. This project has two goals. The first is to design and evaluate interfaces for visualizing aggregated data harvested from EAD encoded archival finding aids. The second is to analyze and develop recommendations for handling issues related to the lack of subject term standardization in the description of archival collections. This will lay the foundation for future work to develop a tool for use in visualizing archival collections from institutions using EAD to encode their finding aids. A tool for visualizing this broad range of archival collections would support both experienced and amateur researchers in their efforts to locate new materials. Any set of archival collections could be evaluated an an aggregated manner. Visualization tools can support discovery of relationships among time periods and subjects that otherwise may never be detected

Contour-based Recognition ∗

Contour is an important cue for object recognition. In this paper, built upon the concept of torque in image space, we propose a new contour-related feature to detect and describe local contour information in images. There are two components for our proposed feature: One is a contour patch detector for detecting image patches with interesting information of object contour, which we call the Maximal/Minimal Torque Patch (MTP) detector. The other is a contour patch descriptor for characterizing a contour patch, which we call the Multi-scale Torque (MST) descriptor. It samples the torque values in the neighborhood of the patch in a multi-scale manner. Experiments for object recognition on the Caltech-101 dataset showed that the proposed contour feature outperforms other contour-related features and is on a par with many other types of features. When combing our descriptor with the complementary SIFT descriptor, impressive recognition results are observed. 1