3,013 research outputs found
Saliency maps on image hierarchies
© 2015 Elsevier B.V. All rights reserved.
In this paper we propose two saliency models for salient object segmentation based on a hierarchical image segmentation, a tree-like structure that represents regions at different scales from the details to the whole image (e.g. gPb-UCM, BPT). The first model is based on a hierarchy of image partitions. The saliency at each level is computed on a region basis, taking into account the contrast between regions. The maps obtained for the different partitions are then integrated into a final saliency map. The second model directly works on the structure created by the segmentation algorithm, computing saliency at each node and integrating these cues in a straightforward manner into a single saliency map. We show that the proposed models produce high quality saliency maps. Objective evaluation demonstrates that the two methods achieve state-of-the-art performance in several benchmark datasets.Peer ReviewedPostprint (author's final draft
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
STNet: Selective Tuning of Convolutional Networks for Object Localization
Visual attention modeling has recently gained momentum in developing visual
hierarchies provided by Convolutional Neural Networks. Despite recent successes
of feedforward processing on the abstraction of concepts form raw images, the
inherent nature of feedback processing has remained computationally
controversial. Inspired by the computational models of covert visual attention,
we propose the Selective Tuning of Convolutional Networks (STNet). It is
composed of both streams of Bottom-Up and Top-Down information processing to
selectively tune the visual representation of Convolutional networks. We
experimentally evaluate the performance of STNet for the weakly-supervised
localization task on the ImageNet benchmark dataset. We demonstrate that STNet
not only successfully surpasses the state-of-the-art results but also generates
attention-driven class hypothesis maps
Hierarchical improvement of foreground segmentation masks in background subtraction
A plethora of algorithms have been defined for foreground
segmentation, a fundamental stage for many computer
vision applications. In this work, we propose a post-processing
framework to improve foreground segmentation performance of
background subtraction algorithms. We define a hierarchical
framework for extending segmented foreground pixels to undetected
foreground object areas and for removing erroneously
segmented foreground. Firstly, we create a motion-aware hierarchical
image segmentation of each frame that prevents merging
foreground and background image regions. Then, we estimate
the quality of the foreground mask through the fitness of the
binary regions in the mask and the hierarchy of segmented
regions. Finally, the improved foreground mask is obtained as
an optimal labeling by jointly exploiting foreground quality and
spatial color relations in a pixel-wise fully-connected Conditional
Random Field. Experiments are conducted over four large and
heterogeneous datasets with varied challenges (CDNET2014,
LASIESTA, SABS and BMC) demonstrating the capability of the
proposed framework to improve background subtraction resultsThis work was partially supported by the Spanish Government
(HAVideo, TEC2014-53176-R
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