49,362 research outputs found
High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision
Most of the current boundary detection systems rely exclusively on low-level
features, such as color and texture. However, perception studies suggest that
humans employ object-level reasoning when judging if a particular pixel is a
boundary. Inspired by this observation, in this work we show how to predict
boundaries by exploiting object-level features from a pretrained
object-classification network. Our method can be viewed as a "High-for-Low"
approach where high-level object features inform the low-level boundary
detection process. Our model achieves state-of-the-art performance on an
established boundary detection benchmark and it is efficient to run.
Additionally, we show that due to the semantic nature of our boundaries we
can use them to aid a number of high-level vision tasks. We demonstrate that
using our boundaries we improve the performance of state-of-the-art methods on
the problems of semantic boundary labeling, semantic segmentation and object
proposal generation. We can view this process as a "Low-for-High" scheme, where
low-level boundaries aid high-level vision tasks.
Thus, our contributions include a boundary detection system that is accurate,
efficient, generalizes well to multiple datasets, and is also shown to improve
existing state-of-the-art high-level vision methods on three distinct tasks
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
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
Using polyhedral models to automatically sketch idealized geometry for structural analysis
Simplification of polyhedral models, which may incorporate large numbers of faces and nodes, is often required to reduce their amount of data, to allow their efficient manipulation, and to speed up computation. Such a simplification process must be adapted to the use of the resulting polyhedral model. Several applications require simplified shapes which have the same topology as the original model (e.g. reverse engineering, medical applications, etc.). Nevertheless, in the fields of structural analysis and computer visualization, for example, several adaptations and idealizations of the initial geometry are often necessary. To this end, within this paper a new approach is proposed to simplify an initial manifold or non-manifold polyhedral model with respect to bounded errors specified by the user, or set up, for example, from a preliminary F.E. analysis. The topological changes which may occur during a simplification because of the bounded error (or tolerance) values specified are performed using specific curvature and topological criteria and operators. Moreover, topological changes, whether or not they kept the manifold of the object, are managed simultaneously with the geometric operations of the simplification process
Vision-based toddler tracking at home
This paper presents a vision-based toddler tracking system for detecting risk factors of a toddler's fall within the home environment. The risk factors have environmental and behavioral aspects and the research in this paper focuses on the behavioral aspects. Apart from common image processing tasks such as background subtraction, the vision-based toddler tracking involves human classification, acquisition of motion and position information, and handling of regional merges and splits. The human classification is based on dynamic motion vectors of the human body. The center of mass of each contour is detected and connected with the closest center of mass in the next frame to obtain position, speed, and directional information. This tracking system is further enhanced by dealing with regional merges and splits due to multiple object occlusions. In order to identify the merges and splits, two directional detections of closest region centers are conducted between every two successive frames. Merges and splits of a single object due to errors in the background subtraction are also handled. The tracking algorithms have been developed, implemented and tested
New Descriptor for Glomerulus Detection in Kidney Microscopy Image
Glomerulus detection is a key step in histopathological evaluation of
microscopy images of kidneys. However, the task of automatic detection of
glomeruli poses challenges due to the disparity in sizes and shapes of
glomeruli in renal sections. Moreover, extensive variations of their
intensities due to heterogeneity in immunohistochemistry staining are also
encountered. Despite being widely recognized as a powerful descriptor for
general object detection, the rectangular histogram of oriented gradients
(Rectangular HOG) suffers from many false positives due to the aforementioned
difficulties in the context of glomerulus detection.
A new descriptor referred to as Segmental HOG is developed to perform a
comprehensive detection of hundreds of glomeruli in images of whole kidney
sections. The new descriptor possesses flexible blocks that can be adaptively
fitted to input images to acquire robustness to deformations of glomeruli.
Moreover, the novel segmentation technique employed herewith generates high
quality segmentation outputs and the algorithm is assured to converge to an
optimal solution. Consequently, experiments using real world image data reveal
that Segmental HOG achieves significant improvements in detection performance
compared to Rectangular HOG.
The proposed descriptor and method for glomeruli detection present promising
results and is expected to be useful in pathological evaluation
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