24,525 research outputs found
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
We propose a robust approach for performing automatic species-level
recognition of fossil pollen grains in microscopy images that exploits both
global shape and local texture characteristics in a patch-based matching
methodology. We introduce a novel criteria for selecting meaningful and
discriminative exemplar patches. We optimize this function during training
using a greedy submodular function optimization framework that gives a
near-optimal solution with bounded approximation error. We use these selected
exemplars as a dictionary basis and propose a spatially-aware sparse coding
method to match testing images for identification while maintaining global
shape correspondence. To accelerate the coding process for fast matching, we
introduce a relaxed form that uses spatially-aware soft-thresholding during
coding. Finally, we carry out an experimental study that demonstrates the
effectiveness and efficiency of our exemplar selection and classification
mechanisms, achieving accuracy on a difficult fine-grained species
classification task distinguishing three types of fossil spruce pollen.Comment: CVMI 201
Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling
We frame the task of predicting a semantic labeling as a sparse
reconstruction procedure that applies a target-specific learned transfer
function to a generic deep sparse code representation of an image. This
strategy partitions training into two distinct stages. First, in an
unsupervised manner, we learn a set of generic dictionaries optimized for
sparse coding of image patches. We train a multilayer representation via
recursive sparse dictionary learning on pooled codes output by earlier layers.
Second, we encode all training images with the generic dictionaries and learn a
transfer function that optimizes reconstruction of patches extracted from
annotated ground-truth given the sparse codes of their corresponding image
patches. At test time, we encode a novel image using the generic dictionaries
and then reconstruct using the transfer function. The output reconstruction is
a semantic labeling of the test image.
Applying this strategy to the task of contour detection, we demonstrate
performance competitive with state-of-the-art systems. Unlike almost all prior
work, our approach obviates the need for any form of hand-designed features or
filters. To illustrate general applicability, we also show initial results on
semantic part labeling of human faces.
The effectiveness of our approach opens new avenues for research on deep
sparse representations. Our classifiers utilize this representation in a novel
manner. Rather than acting on nodes in the deepest layer, they attach to nodes
along a slice through multiple layers of the network in order to make
predictions about local patches. Our flexible combination of a generatively
learned sparse representation with discriminatively trained transfer
classifiers extends the notion of sparse reconstruction to encompass arbitrary
semantic labeling tasks.Comment: to appear in Asian Conference on Computer Vision (ACCV), 201
Dense 3D Face Correspondence
We present an algorithm that automatically establishes dense correspondences
between a large number of 3D faces. Starting from automatically detected sparse
correspondences on the outer boundary of 3D faces, the algorithm triangulates
existing correspondences and expands them iteratively by matching points of
distinctive surface curvature along the triangle edges. After exhausting
keypoint matches, further correspondences are established by generating evenly
distributed points within triangles by evolving level set geodesic curves from
the centroids of large triangles. A deformable model (K3DM) is constructed from
the dense corresponded faces and an algorithm is proposed for morphing the K3DM
to fit unseen faces. This algorithm iterates between rigid alignment of an
unseen face followed by regularized morphing of the deformable model. We have
extensively evaluated the proposed algorithms on synthetic data and real 3D
faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using
quantitative and qualitative benchmarks. Our algorithm achieved dense
correspondences with a mean localisation error of 1.28mm on synthetic faces and
detected anthropometric landmarks on unseen real faces from the FRGCv2
database with 3mm precision. Furthermore, our deformable model fitting
algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on
Bosphorus database. Our dense model is also able to generalize to unseen
datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
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