2,482 research outputs found
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Partial shape matching using CCP map and weighted graph transformation matching
La dĂ©tection de la similaritĂ© ou de la diffĂ©rence entre les images et leur mise en correspondance sont des problĂšmes fondamentaux dans le traitement de l'image. Pour rĂ©soudre ces problĂšmes, on utilise, dans la littĂ©rature, diffĂ©rents algorithmes d'appariement. MalgrĂ© leur nouveautĂ©, ces algorithmes sont pour la plupart inefficaces et ne peuvent pas fonctionner correctement dans les situations dâimages bruitĂ©es. Dans ce mĂ©moire, nous rĂ©solvons la plupart des problĂšmes de ces mĂ©thodes en utilisant un algorithme fiable pour segmenter la carte des contours image, appelĂ©e carte des CCPs, et une nouvelle mĂ©thode d'appariement. Dans notre algorithme, nous utilisons un descripteur local qui est rapide Ă calculer, est invariant aux transformations affines et est fiable pour des objets non rigides et des situations dâoccultation. AprĂšs avoir trouvĂ© le meilleur appariement pour chaque contour, nous devons vĂ©rifier si ces derniers sont correctement appariĂ©s. Pour ce faire, nous utilisons l'approche « Weighted Graph Transformation Matching » (WGTM), qui est capable d'Ă©liminer les appariements aberrants en fonction de leur proximitĂ© et de leurs relations gĂ©omĂ©triques. WGTM fonctionne correctement pour les objets Ă la fois rigides et non rigides et est robuste aux distorsions importantes. Pour Ă©valuer notre mĂ©thode, le jeu de donnĂ©es ETHZ comportant cinq classes diffĂ©rentes d'objets (bouteilles, cygnes, tasses, girafes, logos Apple) est utilisĂ©. Enfin, notre mĂ©thode est comparĂ©e Ă plusieurs mĂ©thodes cĂ©lĂšbres proposĂ©es par d'autres chercheurs dans la littĂ©rature. Bien que notre mĂ©thode donne un rĂ©sultat comparable Ă celui des mĂ©thodes de rĂ©fĂ©rence en termes du rappel et de la prĂ©cision de localisation des frontiĂšres, elle amĂ©liore significativement la prĂ©cision moyenne pour toutes les catĂ©gories du jeu de donnĂ©es ETHZ.Matching and detecting similarity or dissimilarity between images is a fundamental problem in image processing. Different matching algorithms are used in literature to solve this fundamental problem. Despite their novelty, these algorithms are mostly inefficient and cannot perform properly in noisy situations. In this thesis, we solve most of the problems of previous methods by using a reliable algorithm for segmenting image contour map, called CCP Map, and a new matching method. In our algorithm, we use a local shape descriptor that is very fast, invariant to affine transform, and robust for dealing with non-rigid objects and occlusion. After finding the best match for the contours, we need to verify if they are correctly matched. For this matter, we use the Weighted Graph Transformation Matching (WGTM) approach, which is capable of removing outliers based on their adjacency and geometrical relationships. WGTM works properly for both rigid and non-rigid objects and is robust to high order distortions. For evaluating our method, the ETHZ dataset including five diverse classes of objects (bottles, swans, mugs, giraffes, apple-logos) is used. Finally, our method is compared to several famous methods proposed by other researchers in the literature. While our method shows a comparable result to other benchmarks in terms of recall and the precision of boundary localization, it significantly improves the average precision for all of the categories in the ETHZ dataset
Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects
Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
3D curves reconstruction from multiple images
In this paper, we propose a new approach for reconstructing 3D curves from a sequence of 2D images taken by uncalibrated cameras. A curve in 3D space is represented by a sequence of 3D points sampled along the curve, and the 3D points are reconstructed by minimizing the distances from their projections to the measured 2D curves on different images (i.e., 2D curve reprojection error). The minimization problem is solved by an iterative algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Without requiring calibrated cameras or additional point features, our method can reconstruct multiple 3D curves simultaneously from multiple images and it readily handles images with missing and/or partially occluded curves. © 2010 IEEE.published_or_final_versionThe 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia, 1-3 December 2010. In Proceedings of DICTA, 2010, p. 462-46
Matching Disparate Image Pairs Using Shape-Aware ConvNets
An end-to-end trainable ConvNet architecture, that learns to harness the
power of shape representation for matching disparate image pairs, is proposed.
Disparate image pairs are deemed those that exhibit strong affine variations in
scale, viewpoint and projection parameters accompanied by the presence of
partial or complete occlusion of objects and extreme variations in ambient
illumination. Under these challenging conditions, neither local nor global
feature-based image matching methods, when used in isolation, have been
observed to be effective. The proposed correspondence determination scheme for
matching disparate images exploits high-level shape cues that are derived from
low-level local feature descriptors, thus combining the best of both worlds. A
graph-based representation for the disparate image pair is generated by
constructing an affinity matrix that embeds the distances between feature
points in two images, thus modeling the correspondence determination problem as
one of graph matching. The eigenspectrum of the affinity matrix, i.e., the
learned global shape representation, is then used to further regress the
transformation or homography that defines the correspondence between the source
image and target image. The proposed scheme is shown to yield state-of-the-art
results for both, coarse-level shape matching as well as fine point-wise
correspondence determination.Comment: First two authors contributed equally, to Appear in the IEEE Winter
Conference on Applications of Computer Vision (WACV) 201
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