18339723
Automatic video object shape extraction and its classification with camera in motion
In this paper, we present an automatic moving object extraction and classification system. For automatic extraction of object taken by a moving camera, a novel technique is proposed, in which optical flow handles the background modeling and camera motion estimation, and frame difference information yields the exact object shape. We also use forward region boundary based change detection approach for frame difference. This approach assures change detection for uniform intensity regions. For classification, weighted likelihood discriminant based shape classifier is designed. Unlike maximum likelihood (ML) methods, our proposed method utilizes information from all classes to design the classifier. In the description phase of the classifier, curvature features are extracted from the shape and are utilized to build a hidden Markov model (HMM). The HMM provides a robust ML description of the shape. In the discrimination phase, a weighted likelihood discriminant function is introduced, which weights the likelihoods of curvature at individual points of the shape to minimize the classification error. The weights are estimated by generalized probabilistic descent (GPD) method. To demonstrate the performance of the proposed method, we present results achieved for car shapes extraction and classification.
39022820
A multi-Kalman filtering approach for video tracking of human-delineated objects in cluttered environments
14630821
A New Semi-Supervised Subspace Clustering Algorithm on Fitting Mixture Models
We propose a new subspace clustering algorithm (EPSCMIX), which is based on the feature saliency measure that is obtained by using both the Emerging Patterns algorithm and the EM algorithm, for the analysis of microarray data. For the model selection, it employs a novel agglomerative step together with MDL criterion. And, we present the result of comparative experiments between AIC, MDL and minimum message length (MML) used to determine a criterion for our algorithm. The robustness of using emerging patterns based on mixture models, as well as using the Gaussian mixture model for subspace clustering, was demonstrated on both synthetic and real data sets. In experiments, it also certified that a new agglomerative method that merges mostly correlated components with MDL consistently worked better than the one that removes weak weight components.
13951367
Interactive color image segmentation editor driven by active contour model
A new general-purpose color image segmentation editor (CISE) for the purpose of extracting a semantic object is designed, implemented and tested on a number of various natural scene images. Our editor integrates a deformable model and image statistics including intensity, color, gradient and texture. The editor starts with a coarse region segmentation which applies the Canny's operator followed by a low-complexity edge linking algorithm. This segmentation basically builds regions of smooth intensity by closing all dangling edges. Next, a topologically-based region labeling method makes full use of relationship among pixels and produces useful labeled image. Finally, to refine the extracted object of interest (O/sup 2/I), a deformable model based on energy minimization is applied by incorporating both the gradient and region criteria to the external constraint force. These processes are demonstrated through examples on natural scene color images. Experimental results suggest the efficiency and accuracy of the algorithm in its segmentation operations.
9243283
Self-occlusion immune video tracking of objects in cluttered environments
We propose a new approach that uses a motion-estimation based framework for video tracking of objects in the presence of self-occlusion in cluttered environments. What makes our work different from others is that, instead of carrying out the motion estimation between two adjacent frames, we tackle the self-occlusion problem from the view of multiple frames. The heart of our approach lies in extracting features appearing in different time frames, genesis frames, and setting up a motion estimation scheme through multiple applications of Kalman filtering based on the different genesis indices. To make the tracked object look visually familiar to the human observer, the system also makes its best attempt at extracting the boundary contour of the object - a difficult problem in its own right, since self-occlusion created by any rotational motion of the tracked object would cause large sections of the boundary contour in the previous frame to disappear in the current frame. Our approach has been tested on a wide variety of video sequences, some of which are presented.
8377397
Boundary recognition in sensor networks by topological methods
Wireless sensor networks are tightly associated with the underlying environment in which the sensors are deployed. The global topology of the network is of great importance to both sensor network applications and the implementation of networking functionalities. In this paper we study the problem of topology discovery, in particular, identifying boundaries in a sensor network. Suppose a large number of sensor nodes are scattered in a geometric region, with nearby nodes communicating with each other directly. Our goal is to find the boundary nodes by using only connectivity information. We do not assume any knowledge of the node locations or inter-distances, nor do we enforce that the communication graph follows the unit disk graph model. We propose a simple, distributed algorithm that correctly detects nodes on the boundaries and connects them into meaningful boundary cycles. We obtain as a byproduct the medial axis of the sensor field, which has applications in creating virtual coordinates for routing. We show by extensive simulation that the algorithm gives good results even for networks with low density. We also prove rigorously the correctness of the algorithm for continuous geometric domains.
461312
Dependence language model for information retrieval
This paper presents a new dependence language modeling approach to information retrieval. The approach extends the basic language modeling approach based on unigram by relaxing the independence assumption. We integrate the linkage of a query as a hidden variable, which expresses the term dependencies within the query as an acyclic, planar, undirected graph. We then assume that a query is generated from a document in two stages: the linkage is generated first, and then each term is generated in turn depending on other related terms according to the linkage. We also present a smoothing method for model parameter estimation and an approach to learning the linkage of a sentence in an unsupervised manner. The new approach is compared to the classical probabilistic retrieval model and the previously proposed language models with and without taking into account term dependencies. Results show that our model achieves substantial and significant improvements on TREC collections.
3016958
Multiple frame motion inference using belief propagation
We present an algorithm for automatic inference of human upper body motion. A graph model is proposed for inferring human motion, and motion inference is posed as a mapping problem between state nodes in the graph model and features in image patches. Belief propagation is utilized for Bayesian inference in this graph. A multiple-frame inference model/algorithm is proposed to combine both structural and temporal constraints in human motion. We also present a method for capturing constraints of human body configuration under different view angles. The algorithm is applied in a prototype system that can automatically label upper body motion from videos, without manual initialization of body parts.
16214442
An adaptive algorithm for text detection from natural scenes
We present a new adaptive algorithm for automatic detection of text from a natural scene. The initial cues of text regions are first detected from the captured image/video. An adaptive color modeling and searching algorithm is then utilized near the initial text cues, to discriminate text/non-text regions. EM optimization algorithm is used for color modeling, under the constraint of text layout relations for a specific language. The proposed algorithm combines the advantages of several previous approaches for text detection, and utilizes a focus-of-attention approach for text finding. The whole algorithm is applied in a prototype system that can automatically detect and recognize sign input from a video camera, and translate the signs into English text or voice streams. We present evaluation results of our algorithm on this system.
6888787
Distributed data management for large volume visualization
We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users with a variety of local computing capabilities, we introduce an adaptive data selection method based on an "enhanced time-space partitioning" (ETSP) tree that assists with effective visibility culling, as well as multiresolution data selection. By traversing the tree, our data management algorithm can quickly identify the visible regions of data, and, for each region, adaptively choose the lowest resolution satisfying user-specified error tolerances. Only necessary data elements are accessed and sent to the visualization pipeline. To further address the issue of sharing large-scale data among geographically distributed collaborative teams, we have designed an infrastructure for integrating our data management technique with a distributed data storage system provided by logistical networking (LoN). Data sets at different resolutions are generated and uploaded to LoN for wide-area access. We describe a parallel volume rendering system that verifies the effectiveness of our data storage, selection and access scheme.