9,030 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
Feature fusion for facial landmark detection: A feature descriptors combination approach
Facial landmark detection is a crucial first step in facial analysis for biometrics and numerous other applications. However, it has proved to be a very challenging task due to the numerous sources of variation in 2D and 3D facial data. Although landmark detection based on descriptors of the 2D and 3D appearance of the face has been extensively studied, the fusion of such feature descriptors is a relatively under-studied issue. In this report, a novel generalized framework for combining facial feature descriptors is presented, and several feature fusion schemes are proposed and evaluated. The proposed framework maps each feature into a similarity score, combines the individual similarity scores into a resultant score, used to select the optimal solution for a queried landmark. The evaluation of the proposed fusion schemes for facial landmark detection clearly indicates that a quadratic distance to similarity mapping in conjunction with a root mean square rule for similarity fusion achieves the best performance in accuracy, efficiency, robustness and monotonicity
Adding Cues to Binary Feature Descriptors for Visual Place Recognition
In this paper we propose an approach to embed continuous and selector cues in
binary feature descriptors used for visual place recognition. The embedding is
achieved by extending each feature descriptor with a binary string that encodes
a cue and supports the Hamming distance metric. Augmenting the descriptors in
such a way has the advantage of being transparent to the procedure used to
compare them. We present two concrete applications of our methodology,
demonstrating the two considered types of cues. In addition to that, we
conducted on these applications a broad quantitative and comparative evaluation
covering five benchmark datasets and several state-of-the-art image retrieval
approaches in combination with various binary descriptor types.Comment: 8 pages, 8 figures, source: www.gitlab.com/srrg-software/srrg_bench,
submitted to ICRA 201
Achieving Corresponding Effects on Multiple Robotic Platforms: Imitating in Context Using Different Effect Metrics
Original paper can be found at: www.aisb.org.uk/publications/proceedings/aisb05/3_Imitation_Final.pdfOne of the fundamental problems in imitation is the correspondence problem, how to map between the actions, states and effects of the model and imitator agents, when the embodiment of the agents is dissimilar. In our approach, the matching is according to different metrics and granularity. This paper presents JABBERWOCKY, a system that uses captured data from a human demonstrator to generate appropriate action commands, addressing the correspondence problem in imitation. Towards a characterization of the space of effect metrics, we are exploring absolute/relative angle and displacement aspects and focus on the overall arrangement and trajectory of manipulated objects. Using as an example a captured demonstration from a human, the system produces a correspondence solution given a selection of effect metrics and starting from dissimilar initial object positions, producing action commands that are then executed by two imitator target platforms (in simulation) to successfully imitate
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