39,065 research outputs found
Matching of complex patterns by energy minimization
Two patterns are matched by putting one on top of the other and iteratively moving their individual parts until most of their corresponding parts are aligned. An energy function and a neighborhood of influence are defined for each iteration. Initially, a large neighborhood is used such that the movements result in global features being coarsely aligned. The neighborhood size is gradually reduced in successive iterations so that finer and finer details are aligned. Encouraging results have been obtained when applied to match complex Chinese characters. It has been observed that computation increases with the square of the number of moving parts which is quite favorable compared with other algorithms. The method was applied to the recognition of handwritten Chinese characters. After performing the iterative matching, a set of similarity measures are used to measure the similarity in topological features between the input and template characters. An overall recognition rate of 96.1% is achieved. © 1998 IEEE.published_or_final_versio
Simultaneously Sparse Solutions to Linear Inverse Problems with Multiple System Matrices and a Single Observation Vector
A linear inverse problem is proposed that requires the determination of
multiple unknown signal vectors. Each unknown vector passes through a different
system matrix and the results are added to yield a single observation vector.
Given the matrices and lone observation, the objective is to find a
simultaneously sparse set of unknown vectors that solves the system. We will
refer to this as the multiple-system single-output (MSSO) simultaneous sparsity
problem. This manuscript contrasts the MSSO problem with other simultaneous
sparsity problems and conducts a thorough initial exploration of algorithms
with which to solve it. Seven algorithms are formulated that approximately
solve this NP-Hard problem. Three greedy techniques are developed (matching
pursuit, orthogonal matching pursuit, and least squares matching pursuit) along
with four methods based on a convex relaxation (iteratively reweighted least
squares, two forms of iterative shrinkage, and formulation as a second-order
cone program). The algorithms are evaluated across three experiments: the first
and second involve sparsity profile recovery in noiseless and noisy scenarios,
respectively, while the third deals with magnetic resonance imaging
radio-frequency excitation pulse design.Comment: 36 pages; manuscript unchanged from July 21, 2008, except for updated
references; content appears in September 2008 PhD thesi
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at large
displacements with significant oc-clusions. It consists of two steps: i) dense
matching by edge-preserving interpolation from a sparse set of matches; ii)
variational energy minimization initialized with the dense matches. The
sparse-to-dense interpolation relies on an appropriate choice of the distance,
namely an edge-aware geodesic distance. This distance is tailored to handle
occlusions and motion boundaries -- two common and difficult issues for optical
flow computation. We also propose an approximation scheme for the geodesic
distance to allow fast computation without loss of performance. Subsequent to
the dense interpolation step, standard one-level variational energy
minimization is carried out on the dense matches to obtain the final flow
estimation. The proposed approach, called Edge-Preserving Interpolation of
Correspondences (EpicFlow) is fast and robust to large displacements. It
significantly outperforms the state of the art on MPI-Sintel and performs on
par on Kitti and Middlebury
Optimal Planar Electric Dipole Antenna
Considerable time is often spent optimizing antennas to meet specific design
metrics. Rarely, however, are the resulting antenna designs compared to
rigorous physical bounds on those metrics. Here we study the performance of
optimized planar meander line antennas with respect to such bounds. Results
show that these simple structures meet the lower bound on radiation Q-factor
(maximizing single resonance fractional bandwidth), but are far from reaching
the associated physical bounds on efficiency. The relative performance of other
canonical antenna designs is compared in similar ways, and the quantitative
results are connected to intuitions from small antenna design, physical bounds,
and matching network design.Comment: 10 pages, 15 figures, 2 tables, 4 boxe
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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