157172
Computing rectifying homographies for stereo vision
Image rectification is the process of applying a pair of 2D projective transforms, or homographies, to a pair of images whose epipolar geometry is known so that epipolar lines in the original images map to horizontally aligned lines in the transformed images. We propose a novel technique for image rectification based on geometrically well defined criteria such that image distortion due to rectification is minimized. This is achieved by decomposing each homography into a specialized projective transform, a similarity transform, followed by a shearing transform. The effect of image distortion at each stage is carefully considered.
8951856
Model-based head pose tracking with stereovision
We present a robust model-based stereo head tracking algorithm that operates in real time on a commodity PC. The use of an individualized three-dimensional head model, coupled with the epipolar constraint from the stereo image pair greatly improves the robustness of the tracking. Experimental results have shown that our method is able to track all the six degrees of freedom of the rigid part of head motions, over an extended period of time, in the presence of large angular and translational head motions, partial occlusions, and/or dramatic facial expression changes. Applications include human-computer interaction and eye-gaze correction for videoconferencing.
28801152
Remote Collaboration on Physical Whiteboards
A whiteboard provides a large shared space for the participants to focus their attention and express their ideas, and is therefore a great collaboration tool for information workers. However, it has several limitations; notably, the contents on the whiteboard are hard to archive or share with people who are not present in the discussions. This paper presents our work in developing tools to facilitate collaboration on physical whiteboards by using a camera and a microphone. In particular, our system allows a user to take notes of a whiteboard meeting when he/she wants or in an automatic way, to transmit the whiteboard content to remote participants in real time and in an efficient way, and to archive the whole whiteboard session for efficient post-viewing. Our system can be retrofit to any existing whiteboard. With the help of a camera and a microphone, we are effectively bridging the physical and digital worlds.
36184486
Image-Based Surface Detail Transfer
We present a novel technique, called Image-Based Surface Detail Transfer, to transfer geometric details from one surface to another with simple 2D image operations. The basic observation is that, without knowing its 3D geometry, geometric details (local deformations) can be extracted from a single image of an object in a way independent of its surface reflectance, and furthermore, these geometric details can be transferred to modify the appearance of other objects directly in images. We show examples including surface detail transfer between real objects, as well as between real and synthesized objects.
2276524
Automatic Business Card Scanning with a Camera
In this paper, we present a system to automatically extract, rectify and enhance business card images. First the business card image patch is automatically segmented by minimizing a novel local-global variational energy. Second a quadrangle is fitted to the segmented image patch. With the four corner points of the quadrangle, we then estimate the physical aspect ratio of the business card and obtain a homography to rectify the quadrangle back to rectangular shape. We finally enhance the contrast of the rectified business card image using a S-shaped curve. Extensive experiments demonstrated the efficacy and robustness of our system.
18016173
Corner guided curve matching and its application to scene reconstruction
Corners and curves are important image features in many vision-based applications. Corners are usually more stable and easier to match than curves, while curves contain richer information of scene structure. In previously work, corners are often used to recover the epipolar geometry between two views, which is then used in curve matching to reduce the search space. However, information of the scene structure contained in this set of matched corners is ignored. In this paper we present a curve matching algorithm that is guided by a set of matched corners. Within a probabilistic framework, the role of the corner guidance is explicitly defined by a set of similarity-invariant unary measurements and by a similarity function. The similarity function provides stronger capability of resolving matching ambiguity than the epipolar constraint, and is integrated into a relaxation scheme to reduce computational complexity and improve accuracy of curve matching. Experimental results clearly demonstrate the benefit of integrating corner matches into the curve matching procedure.
10911475
Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval
This paper is about the work on user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to stretching Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of BAyesian Learning in Asymmetric and Small sample collections, thus called BALAS. Different learning strategies are used for positive and negative sample collections in BALAS, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS which complementarily combines the subjective relevancy confidence and the objective feature-based distance measure to capture the overall retrieval semantics. The experimental evaluations have confirmed the rationale of the proposed ranking scheme, and have also demonstrated that BALAS is superior to an existing relevance feedback method in the current literature in capturing the overall retrieval semantics.
8796177
LIPS: Lightweight Internet Permit System for Stopping Unwanted Packets
In this paper, we propose a Lightweight Internet Permit System (LIPS) that provides a lightweight, scalable packet authentication mechanism for ensuring traffic-origin accountability. LIPS is a simple extension of IP, in which each packet carries an access permit issued by its destination host or gateway, and the destination verifies the access permit to determine if a packet is accepted or dropped. We will first present the design and the prototype implementation of LIPS on Linux 2.4 kernel. We then use analysis, simulations, and experiments to show how LIPS can effectively prevent protected critical servers and links from being flooded by unwanted packets with negligible overheads. We propose LIPS as an domain-to-domain approach to stop unwanted attacks, without requiring broad changes in backbone networks as other approaches. Therefore, LIPS is incrementally deployable in a large scale on common platforms with minor software patches.
1498226
Approaches to implementing decision aggregation in multi-agent systems
62548744
Hidden semantic concept discovery in region based image retrieval
This work addresses content based image retrieval (CBIR), focusing on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented to region; associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which expectation-maximization (EM) technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation and the experimental evaluations on a database of 10,000 general-purposed images demonstrate its promise of the effectiveness.
5416143
A clustering based approach to efficient image retrieval
This paper addresses the issue of effective and efficient content based image retrieval by presenting a novel indexing and retrieval methodology that integrates color, texture, and shape information for the indexing and retrieval, and applies these features in regions obtained through unsupervised segmentation, as opposed to applying them to the whole image domain. In order to address the typical color feature "inaccuracy" problem in the literature, fuzzy logic is applied to the traditional color histogram to solve for the problem to a certain degree. The similarity is defined through a balanced combination between global and regional similarity measures incorporating all the features. In order to further improve the retrieval efficiency, a secondary clustering technique is developed and employed to significantly save query processing time without compromising the retrieval precision. An implemented prototype system has demonstrated a promising retrieval performance for a test database containing 2000 general-purpose color images, as compared with its peer systems in the literature.