30255791
Adaptive Model-Based Multi-person Tracking
This paper proposes a method for tracking and identifying persons from video image frames taken by a fixed camera. The majority of conventional video tracking surveillance systems assumes a likeness to a person's appearance for some time, and existing human tracking systems usually consider short-term situations. To address this situation, we use an adaptive background and human body model updated statistically frame-by-frame to correctly construct a person with body parts. The formed person is labeled and recorded in a person's list, which stores the individual's human body model details. Such recorded information can be used to identify tracked persons. The results of this experiment are demonstrated in several indoor situations.
24310355
Component-Based Online Learning for Face Detection and Verification
Component detectors can accurately locate facial components, and component-based approaches can be used to build detectors that can handle partial occlusions. This paper proposes a face detection and verification method using component-based online learning. The main difference from previously reported component-based approaches is the use of online learning, which is ideal for highly repetitive tasks. This results in faster and more accurate face detection, because system performance improves with continued use. Further, uncertainty is added by calculating the standard deviation of face components and their relations.
34164098
A New EM Algorithm for Resource Allocation Network
Clustering usually assumes that the number of clusters is known or given. No knowledge of such a priori information is needed to find an appropriate number of clusters. This paper introduces an elliptical clustering algorithm with incremental growth of clusters, which is derived from the batch EM algorithm with a decay factor and a novelty criterion. The proposed algorithm can start with no or a small number of clusters. Whenever unusual data is presented, the algorithm adds a new cluster and finally the number of clusters in the data is obtained after clustering. The usefulness of the proposed algorithm is demonstrated for texture image segmentation and skin image segmentation.
42709388
Precomputing avatar behavior from human motion data
15404238
Process resurrection: a fast recovery mechanism for real-time embedded systems
This paper describes a fast recovery mechanism that meets the requirements of embedded real-time systems. In general purpose computing, restart is an established technology for achieving high availability. Restart has also been used in soft real-time systems where the temporary interruption of service is undesirable but acceptable. However, it has not been widely used in small embedded real-time systems with hard deadlines, mainly because the traditional approaches do not meet their requirements of low memory and processor overhead, and fast response times with little variations. We have developed process resurrection, a novel restart mechanism for recovering from crash failures to meet these requirements. The experiments on an inverted pendulum control system shows that it can recover the control process in time after a crash (eg. segmentation fault). Another experiment conducted on an MP3 audio player shows that this technique is also applicable to some multimedia applications.
5225944
Adaptive filtering using constrained subband updates
This paper describes and analyzes a class of subband adaptive filters (SAF) that stems from various approaches of applying subband and multirate techniques in adaptive filtering. This class of SAF has a unique structure of weight-control mechanism where subband error signals are used to actuate the adaptation of a fullband weight vector. In this paper, we show that the recursive weight adaptation can be expressed as an underdetermined least-squares solution. Within this deterministic framework, we show its underlying affinity with the normalized least-mean-square (NLMS) and affine projection (AP) algorithms. We then compare the efficacy of these algorithms in terms of convergence rate and computational complexity.
206593703
A Method for Deciding Quantization Steps in QIM Watermarking Schemes
In this paper, we propose a method for enlarging quantization steps of a QIM watermarking scheme which determines the perceptual quality and robustness of the watermarked images. In general, increasing the quantization steps leads to good robustness but poor perceptual quality of watermarked images and vice versa. However, if we choose the quantization steps considering the expected quantization results as well as the original images, we can increase both robustness and perceptual quality of the watermarked images.
6174992
Efficient memoryless protocol for tag identification (extended abstract)
This paper presents an efficient collision resolution protocol and its variations for the tag identification problem, where an electromagnetic reader attempts to obtain within is read range the unique ID number of each tag. The novelty of our main protocol is that each tag is memoryless, i.e., the current response of each tag only depends on the current query of the reader but not on the past history of the reader's queries. Moreover, the only computation required for each tag is to match its ID against the binary string in the query. Theoretical resulst in both time and communication complexities are derived to demonstrate the efficiency of our protocols.