12564805
A multi-Kalman filtering approach for video tracking of human-delineated objects in cluttered environments
In this paper, we propose a new approach that uses a motion-estimation based framework for video tracking of objects in cluttered environments. Our approach is semi-automatic, in the sense that a human is called upon to delineate the boundary of the object to be tracked in the first frame of the image sequence. The approach presented requires no camera calibration; therefore it is not necessary that the camera be stationary. The heart of the approach lies in extracting features and estimating motion through multiple applications of Kalman filtering. The estimated motion is used to place constraints on where to seek feature correspondences; successful correspondences are subsequently used for Kalman-based recursive updating of the motion parameters. Associated with each feature is the frame number in which the feature makes its first appearance in an image sequence. All features that make first-time appearances in the same frame are grouped together for Kalman-based updating of motion parameters. Finally, in order 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. Boundary contour is estimated by projecting the previous-frame contour into the current frame for the purpose of creating neighborhoods in which to search for the true boundary in the current frame. Our approach has been tested on a wide variety of video sequences, some of which are shown in this paper.
11284958
Peptide identification by tandem mass spectra: an efficient parallel searching
De novo peptide sequencing that determines the amino acid sequence of a peptide via tandem mass spectrometry (MS/MS) has been increasingly used nowadays in proteomics for protein identification. Current de novo methods generally employ a graph theory which usually produces a large number of candidate sequences and causes heavy computational cost while trying to determine a sequence with less ambiguity. We present a novel de novo sequencing algorithm which greatly reduces the number of candidate sequences. By utilizing certain properties of b- and y-ion series in MS/MS spectrum, we propose a reliable two-way parallel searching algorithm to filter out the peptide candidates which are further pruned by an intensity evidence based screening criterion. And we find an adjusted value required to determine the position of end node of b- and y-ion series for the charged +2 precursor in our graph. Results of our algorithm are compared with those of PEAKS, a well-known de novo sequencing software. Experimental results demonstrate the six sequences are identical with the correct sequences. And for the further pruning, rankings of our result remain unchanged even though the screening criterion changes. Therefore we can reduce the number of candidate sequences by adopting a proper screening criterion.
39022820
A multi-Kalman filtering approach for video tracking of human-delineated objects in cluttered environments
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.
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.
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.
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.
147952
Locating and bypassing routing holes in sensor networks
Many algorithms for routing in sensor networks exploit greedy forwarding strategies to get packets to their destinations. We study a fundamental difficulty such strategies face: the "local minimum phenomena" that can cause packets to get stuck. We give a definition of stuck nodes where packets may get stuck in greedy multi-hop forwarding, and develop a local rule, the TENT rule, for each node in the network to test whether a packet can get stuck at that node. To help the packets get out of stuck nodes, we describe a distributed algorithm, BOUNDHOLE, to build routes around holes, which are connected regions of the network with boundaries consisting of all the stuck nodes. We show that these hole-surrounding routes can be used in many applications such as geographic routing, path migration, information storage mechanisms and identification of regions of interest.
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.
4242083
Lossless Data Hiding Using Histogram Shifting Method Based on Integer Wavelets
This paper proposes a histogram shifting method for image lossless data hiding in integer wavelet transform domain. This algorithm hides data into wavelet coefficients of high frequency subbands. It shifts a part of the histogram of high frequency wavelet subbands and thus embeds data by using the created histogram zero-point. This shifting process may be sequentially carried out if necessary. Histogram modification technique is applied to prevent overflow and underflow. The performance of this proposed technique in terms of the data embedding payload versus the visual quality of marked images is compared with that of the existing lossless data hiding methods implemented in the spatial domain, integer cosine transform domain, and integer wavelet transform domain. The experimental results have demonstrated the superiority of the proposed method over the existing methods. That is, the proposed method has a larger embedding payload in the same visual quality (measured by PSNR (peak signal noise ratio)) or has a higher PSNR in the same payload.
10392277
Visibility Culling Using Plenoptic Opacity Functions for Large Data Visualization