43527681
Learning-based pulmonary nodule detection from multislice CT data
14450433
Integrating Faces, Fingerprints, and Soft Biometric Traits for User Recognition
Soft biometric traits like gender, age, height, weight, ethnicity, and eye color cannot provide reliable user recognition because they are not distinctive and permanent. However, such ancillary information can complement the identity information provided by the primary biometric traits (face, fingerprint, hand-geometry, iris, etc.). This paper describes a hybrid biometric system that uses face and fingerprint as the primary characteristics and gender, ethnicity, and height as the soft characteristics. We have studied the effect of the soft biometric traits on the recognition performance of unimodal face and fingerprint recognition systems and a multimodal system that uses both the primary traits. Experiments conducted on a database of 263 users show that the recognition performance of the primary biometric system can be improved significantly by making use of soft biometric information. The results also indicate that such a performance improvement can be achieved only if the soft biometric traits are complementary to the primary biometric traits.
18993736
Matching 2.5D Scans for Face Recognition
The performance of face recognition systems that use two-dimensional images is dependent on consistent conditions such as lighting, pose, and facial appearance. We are developing a face recognition system that uses three-dimensional depth information to make the system more robust to these arbitrary conditions. We have developed a face matching system that automatically correlates points in three dimensions between two 2.5D range images of different views. A hybrid Iterative Closest Point (ICP) scheme is proposed to integrate two classical ICP algorithms for fine registration of the two scans. A robust similarity metric is defined for matching purpose. Results are provided on a preliminary database of 10 subjects (one training image per subject) containing frontal face images of neutral expression with a testing database of 63 scans that varied in pose, expression and lighting.
19393995
Skilled Forgery Detection in On-Line Signatures: A Multimodal Approach
Signature recognition has a long history of usage in authentication of transactions and legal contracts and hence is easily accepted by users in a variety of applications. However, the problem of skilled forgeries is an important challenge that needs to be overcome before signature recognition systems will become viable in unsupervised authentication systems. In this paper, we present a multimodal approach to forgery detection, where a physiological trait, the face of the signing person, is used to validate the signature. Methods of normalizing and combining the matching scores from the individual modalities are investigated. Test results of the system on a database of 100 users is presented. The system achieves an equal error rate of 2.2% in the presence of high quality skilled forgeries and could detect all the skilled forgeries at a genuine acceptance rate of 75%.
16545185
Hybrid Methods for POS Guessing of Chinese Unknown Words
This paper describes a hybrid model that combines a rule-based model with two statistical models for the task of POS guessing of Chinese unknown words. The rule-based model is sensitive to the type, length, and internal structure of unknown words, and the two statistical models utilize contextual information and the likelihood for a character to appear in a particular position of words of a particular length and POS category. By combining models that use different sources of information, the hybrid model achieves a precision of 89%, a significant improvement over the best result reported in previous studies, which was 69%.
14843520
Deriving knowledge from figures for digital libraries
Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We present our system on automatic categorization of figures and extraction of data from 2-D plots. A machine-learning based method is used to categorize figures into a set of predefined types based on image features. An automated algorithm is designed to extract data values from solid line curves in 2-D plots. The semantic type of figures and extracted data values from 2-D plots can be integrated with textual information within documents to provide more effective document retrieval services for digital library users. Experimental evaluation has demonstrated that our system can produce results suitable for real-world use.
12662382
Autonomous rating oriented agent allocation to achieve high response in demand-oriented information service system
The efficient and reliable information service provision and utilization is an important infrastructure in the high-assurance information service system. In this paper, we introduce a rating oriented distributed information system sustained by push/pull mobile agents to cope with providers' and users' heterogeneous requirements. Based on this environment, autonomous information provision technology is proposed. However, when users' demand changes, congestion might arise in the system. In order to achieve load balancing for users' timeliness access and guarantee that users can always get the information from a certain pathway, autonomous information reallocation technology is proposed.
23366254
Autonomous Information Services Integration for High-Assurance in Demand-Oriented Information Systems
With interdependent requests of multi-service increasing, the need for adaptability, timeliness and reliability to information service systems has become more and more important. In this paper, the relationship between the timeliness and the reliability of correlated services allocation and access is clarified. Based on these factors, the autonomous network-based information services integration technology to provide one-stop service for users’ multi-service requests is proposed. We proved the effectiveness of the proposed technology through the simulation and the results show that the integrated service can reduce the total users access time compared with the conventional systems.
4916415
Power efficient H.263 video transmission over wireless channels
We introduce an approach for adaptive minimization of the total power consumption of wireless video communications subject to a given level of quality of service. Our approach exploits tradeoffs between the power consumption of the H.263 encoder, the Reed-Solomon channel encoder and the transmitter. Simulation results show that source and channel coding parameters and transmit energy per bit should vary based on channel conditions. Optimized settings can reduce the total power consumption by a significant factor compared to fixed parameter settings which do not match with the channel conditions.