5641652
Image orientation detection with integrated human perception cues (or which way is up)
In this paper, we propose a set of human perceptual cues used jointly to automatically detect image orientation. The cues used are: orientation of faces, position of the sky, brighter regions, and textured objects, and symmetry. We combine these cues in a Bayesian framework, and the photo acquiring model has been considered carefully as the prior knowledge of the image orientation. Results on more than a thousand different images provide a compelling argument that our approach is a viable one.
17864128
Traffic Localizing Strategy for Peer-to-Peer Systems
Topology mismatching problem between peer-to-peer overlay network and physical underlying network brings great stress to Internet infrastructure. This paper proposes a traffic localizing strategy (TLS), it keeps scalability property of current DHT algorithms and improves system routing performance by partitioning DHT key space associating with local physical information. Experiments based on CAN model show that the traffic localizing strategy can significantly alleviate the backbone link traffic burden. Meanwhile, the average routing hops are reduced.
8968963
Offense based temporal segmentation for event detection in soccer video
Sports video is regarded as a good testing bed for techniques on content based video analysis and processing. Although partially successful systems have been designed for specific sports domains with limited data, most previous works do not adequately address the problem of temporal segmentation for event detection, nor the event representation problem. In this paper, we present an analysis of soccer video for detecting the semantic notion of <i>offense</i>. It is not only useful as a new semantic concept of sports video analysis, but also provides temporal segmentation for video event detection and representation. We propose a system to detect the offensive unit in soccer video automatically. The offensive unit is then used to calculate new semantics like <i>possession</i>, as well as to detect goal events in video. Experimental results on various sources of soccer video have verified that our approach extracts the new semantic notions successfully and facilitates video event detection and representation
14695418
Generic slow-motion replay detection in sports video
Slow-motion replays highlight important and exciting events in sports videos. Previous works for slow-motion replay detection, however, are usually limited to only some specific production rules such as frame repetition, special video effects and transitions. In this paper, we present a generic method for detecting slow-motion replays in sports video based on the difference of motions between slow-motion replays and normal shots within the same shot class. Experiments on different types of sports videos have verified our approach and achieve reasonable results.
8611890
Relevancy based semantic interoperation of reuse repositories
Software reuse is a promising solution to the software crisis. Reuse repositories are the basic infrastructure for software reuse. During the past decade, various academic, commercial, governmental, and industrial organizations have developed many Internet-enabled reuse repositories to provide access to software components and related resources. It has necessitated semantic interoperation to allow distributed maintenance and management of these repositories while enabling users to efficiently and conveniently access resources from multiple reuse repositories via a single representation view. In this paper, we have proposed an approach to enhancing the semantic interoperability of reuse repositories, called the improved relevancy matching and ranking (IRMR) method, based on analyzing the correlation of terms in representation methods of the repositories. A prototype system, the Virtual Repository supporting Semantic Interoperation (VRSI), is presented to illustrate the application of this approach to support the semantic interoperation of reuse repositories. Experimental results on real world reuse repositories demonstrated significant improvement in terms of searching effectiveness.
5365763
An extended approach to improving the semantic interoperation among reuse epositories
Software reuse is widely believed as a feasible way to improve both the productivity and quality of software development. The developers must be capable to acquire enough desired components before developing with reuse. Many Internet-enabled reuse repositories exist to provide access to reusable resources. It is necessary to eliminate the semantic gaps of the repositories to enable users to access resources from multiple repositories via a single representation view. In some sense, the only usable information to mine the semantics of the terms representing the resources is the mutual components stored in the repositories. In this paper, the extended improved relevancy matching and ranking (EIRMR) method is proposed to enhance the semantic interoperability of reuse repositories, based on our previous work (IRMR) and the mutual components in the third-party repositories. Experimental results on the data sets from the real repositories demonstrated an improvement in terms of searching effectiveness.
171642
Function Call Flow based Fitness Function Design in Evolutionary Testing
Evolutionary Testing has been shown a promising technology for the automatic test data generation. It reformulates test data generation as a metaheuristic search. A well- designed fitness function is essential to the efficiency of evolutionary search. Many efforts have been directed at the design and implementation of fitness function in recent years. However, previous work has just focused on the control dependency of the target on the branches in the same function. When function calls exist in the desired execution trace to the target, the evaluation of the test data on the coverage of these function calls, which should be provided to the evolutionary search, is not captured by the existing fitness function. In this case, the existing fitness function can not fairly evaluate the test data. And the evolutionary search will be hampered or even fail in severe cases. In this paper, a new term is first proposed to incorporate into the existing fitness function. It is applied to evaluate the test data's coverage of function calls along the desired path to the target. The new fitness function can evaluate the test data more fairly, resulting in a better guidance to the evolutionary search. This can be seen by the experiments carried out on two cases.
16020986
Towards achieving reliable and high-performance nanocomputing via dynamic redundancy allocation
Nanoelectronic devices are considered to be the computational fabrics for the emerging nanocomputing systems due to their ultra-high speed and integration density. However, the imperfect bottom-up self-assembly fabrication leads to excessive defects that have become a barrier for achieving reliable computing. In addition, transient errors continue to be a problem. The massive parallelism rendered by nanoscale integration opens up new opportunities but also poses challenges on how to manage such massive resources for reliable and high-performance computing. In this paper, we propose a nanoarchitecture solution to address these emerging challenges. By using dynamic redundancy allocation, the massive parallelism is exploited to jointly achieve fault (defect/error) tolerance and high performance. Simulation results demonstrate the effectiveness of the proposed technique under a range of fault rates and operating conditions.
34620301
Video Local Pattern based Image Matching for Visual Mapping
Image matching plays an important role in visual mapping, a critical task of vision based mobile robot navigation. Based on the observation that the visual content in these video sequences generally changes in a slow and continuous mode, a concept of "video local pattern" is proposed to model each video frame, which is defined as a set of frames that are visually-similar and temporally-adjacent to that frame. Instead of manually labelling, a tracking based method is developed to automatically detect the local pattern for each frame. A model is then estimated from a local pattern, and the matching of images is performed by comparing these models. Experimental results demonstrated its improvement over that comparing individual frames
17154425
Background Subtraction using Incremental Subspace Learning
Background modeling and subtraction is a basic component of many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and activity analysis. In this paper, we propose an effective and adaptive background modeling and subtraction approach that can deal with dynamic scenes. The proposed approach uses a subspace learning method to model the background and the subspace is updated on-line with a sequential Karhunen-Loeve algorithm. A linear prediction model is also used to make the detection more robust. Experimental results show that the proposed approach is able to model the background and detect moving objects under various type of background scenarios with close to real-time performance.
19453960
Integrating Case based Reasoning and Explanation based Learning in an Apprentice Agent
The problem in applications of case based reasoning (CBR) is its utility problem, that is, the cost of retrieving the most appropriate case from the case library for a new given problem and the cost of adapting the retrieved case for solving the new given problem. This paper proposes an approach to solve the utility problem of CBR by integrating CBR and explanation based learning (EBL) from a perspective that emphasizes the function of learning in CBR. In this paper, CBR and EBL are integrated in an apprentice agent, and the application of this apprentice agent in the robotic assembly domain is given as an example.