39,241 research outputs found
Learning Correspondence Structures for Person Re-identification
This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera pair. The learned correspondence structure can not only capture
the spatial correspondence pattern between cameras but also handle the
viewpoint or human-pose variation in individual images. We further introduce a
global constraint-based matching process. It integrates a global matching
constraint over the learned correspondence structure to exclude cross-view
misalignments during the image patch matching process, hence achieving a more
reliable matching score between images. Finally, we also extend our approach by
introducing a multi-structure scheme, which learns a set of local
correspondence structures to capture the spatial correspondence sub-patterns
between a camera pair, so as to handle the spatial misalignments between
individual images in a more precise way. Experimental results on various
datasets demonstrate the effectiveness of our approach.Comment: IEEE Trans. Image Processing, vol. 26, no. 5, pp. 2438-2453, 2017.
The project page for this paper is available at
http://min.sjtu.edu.cn/lwydemo/personReID.htm arXiv admin note: text overlap
with arXiv:1504.0624
Statistical evaluation of research performance of young university scholars: A case study
The research performance of a small group of 49 young scholars, such as doctoral students, postdoctoral and junior researchers, working in different technical and scientific fields, was evaluated based on 11 types of research outputs. The scholars worked at a technical university in the fields of Civil Engineering, Ecology, Economics, Informatics, Materials Engineering, Mechanical Engineering, and Safety Engineering. Principal Component Analysis was used to statistically analyze the research outputs and its results were compared with factor and cluster analysis. The metrics of research productivity describing the types of research outputs included the number of papers, books and chapters published in books, the number of patents, utility models and function samples, and the number of research projects conducted. The metrics of citation impact included the number of citations and h-index. From these metrics -the variables -the principal component analysis extracted 4 main principal components. The 1st principal component characterized the cited publications in high-impact journals indexed by the Web of Science. The 2nd principal component represented the outputs of applied research and the 3rd and 4th principal components represented other kinds of publications. The results of the principal component analysis were compared with the hierarchical clustering using Ward's method. The scatter plots of the principal component analysis and the Mahalanobis distances were calculated from the 4 main principal component scores, which allowed us to statistically evaluate the research performance of individual scholars. Using variance analysis, no influence of the field of research on the overall research performance was found. Unlike the statistical analysis of individual research metrics, the approach based on the principal component analysis can provide a complex view of the research systems.Web of Science30217716
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
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