9,083 research outputs found
Object matching using boundary descriptors
The problem of object recognition is of immense practical importance and potential, and the last decade has witnessed a number of breakthroughs in the state of the art. Most of the past object recognition work focuses on textured objects and local appearance descriptors extracted around salient points in an image. These methods fail in the matching of smooth, untextured objects for which salient point detection does not produce robust results. The recently proposed bag of boundaries (BoB) method is the first to directly address this problem. Since the texture of smooth objects is largely uninformative, BoB focuses on describing and matching objects based on their post-segmentation boundaries. Herein we address three major weaknesses of this work. The first of these is the uniform treatment of all boundary segments. Instead, we describe a method for detecting the locations and scales of salient boundary segments. Secondly, while the BoB method uses an image based elementary descriptor (HoGs + occupancy matrix), we propose a more compact descriptor based on the local profile of boundary normals’ directions. Lastly, we conduct a far more systematic evaluation, both of the bag of boundaries method and the method proposed here. Using a large public database, we demonstrate that our method exhibits greater robustness while at the same time achieving a major computational saving – object representation is extracted from an image in only 6% of the time needed to extract a bag of boundaries, and the storage requirement is similarly reduced to less than 8%
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
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Recent results indicate that the generic descriptors extracted from the
convolutional neural networks are very powerful. This paper adds to the
mounting evidence that this is indeed the case. We report on a series of
experiments conducted for different recognition tasks using the publicly
available code and model of the \overfeat network which was trained to perform
object classification on ILSVRC13. We use features extracted from the \overfeat
network as a generic image representation to tackle the diverse range of
recognition tasks of object image classification, scene recognition, fine
grained recognition, attribute detection and image retrieval applied to a
diverse set of datasets. We selected these tasks and datasets as they gradually
move further away from the original task and data the \overfeat network was
trained to solve. Astonishingly, we report consistent superior results compared
to the highly tuned state-of-the-art systems in all the visual classification
tasks on various datasets. For instance retrieval it consistently outperforms
low memory footprint methods except for sculptures dataset. The results are
achieved using a linear SVM classifier (or distance in case of retrieval)
applied to a feature representation of size 4096 extracted from a layer in the
net. The representations are further modified using simple augmentation
techniques e.g. jittering. The results strongly suggest that features obtained
from deep learning with convolutional nets should be the primary candidate in
most visual recognition tasks.Comment: version 3 revisions: 1)Added results using feature processing and
data augmentation 2)Referring to most recent efforts of using CNN for
different visual recognition tasks 3) updated text/captio
Matching objects across the textured-smooth continuum
The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of an object is represented by a histogram of discretized features capturing local shape. On a large public database of a diverse set of objects, the proposed method is shown to outperform significantly both purely textural and purely shape based approaches for matching across viewpoint variation
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