6,529 research outputs found
Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds
In this paper, we consider the problem of fast and efficient indexing
techniques for sequences evolving in non-Euclidean spaces. This problem has
several applications in the areas of human activity analysis, where there is a
need to perform fast search, and recognition in very high dimensional spaces.
The problem is made more challenging when representations such as landmarks,
contours, and human skeletons etc. are naturally studied in a non-Euclidean
setting where even simple operations are much more computationally intensive
than their Euclidean counterparts. We propose a geometry and data adaptive
symbolic framework that is shown to enable the deployment of fast and accurate
algorithms for activity recognition, dynamic texture recognition, motif
discovery. Toward this end, we present generalizations of key concepts of
piece-wise aggregation and symbolic approximation for the case of non-Euclidean
manifolds. We show that one can replace expensive geodesic computations with
much faster symbolic computations with little loss of accuracy in activity
recognition and discovery applications. The framework is general enough to work
across both Euclidean and non-Euclidean spaces, depending on appropriate
feature representations without compromising on the ultra-low bandwidth, high
speed and high accuracy. The proposed methods are ideally suited for real-time
systems and low complexity scenarios.Comment: Under major revision at IJC
Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition
The rigid registration of two 3D point sets is a fundamental problem in
computer vision. The current trend is to solve this problem globally using the
BnB optimization framework. However, the existing global methods are slow for
two main reasons: the computational complexity of BnB is exponential to the
problem dimensionality (which is six for 3D rigid registration), and the bound
evaluation used in BnB is inefficient. In this paper, we propose two techniques
to address these problems. First, we introduce the idea of translation
invariant vectors, which allows us to decompose the search of a 6D rigid
transformation into a search of 3D rotation followed by a search of 3D
translation, each of which is solved by a separate BnB algorithm. This
transformation decomposition reduces the problem dimensionality of BnB
algorithms and substantially improves its efficiency. Then, we propose a new
data structure, named 3D Integral Volume, to accelerate the bound evaluation in
both BnB algorithms. By combining these two techniques, we implement an
efficient algorithm for rigid registration of 3D point sets. Extensive
experiments on both synthetic and real data show that the proposed algorithm is
three orders of magnitude faster than the existing state-of-the-art global
methods.Comment: 17pages, 16 figures and 6 table
OATM: Occlusion Aware Template Matching by Consensus Set Maximization
We present a novel approach to template matching that is efficient, can
handle partial occlusions, and comes with provable performance guarantees. A
key component of the method is a reduction that transforms the problem of
searching a nearest neighbor among high-dimensional vectors, to searching
neighbors among two sets of order vectors, which can be found
efficiently using range search techniques. This allows for a quadratic
improvement in search complexity, and makes the method scalable in handling
large search spaces. The second contribution is a hashing scheme based on
consensus set maximization, which allows us to handle occlusions. The resulting
scheme can be seen as a randomized hypothesize-and-test algorithm, which is
equipped with guarantees regarding the number of iterations required for
obtaining an optimal solution with high probability. The predicted matching
rates are validated empirically and the algorithm shows a significant
improvement over the state-of-the-art in both speed and robustness to
occlusions.Comment: to appear at cvpr 201
A Survey on Non-rigid 3D Shape Analysis
Shape is an important physical property of natural and manmade 3D objects
that characterizes their external appearances. Understanding differences
between shapes and modeling the variability within and across shape classes,
hereinafter referred to as \emph{shape analysis}, are fundamental problems to
many applications, ranging from computer vision and computer graphics to
biology and medicine. This chapter provides an overview of some of the recent
techniques that studied the shape of 3D objects that undergo non-rigid
deformations including bending and stretching. Recent surveys that covered some
aspects such classification, retrieval, recognition, and rigid or nonrigid
registration, focused on methods that use shape descriptors. Descriptors,
however, provide abstract representations that do not enable the exploration of
shape variability. In this chapter, we focus on recent techniques that treated
the shape of 3D objects as points in some high dimensional space where paths
describe deformations. Equipping the space with a suitable metric enables the
quantification of the range of deformations of a given shape, which in turn
enables (1) comparing and classifying 3D objects based on their shape, (2)
computing smooth deformations, i.e. geodesics, between pairs of objects, and
(3) modeling and exploring continuous shape variability in a collection of 3D
models. This article surveys and classifies recent developments in this field,
outlines fundamental issues, discusses their potential applications in computer
vision and graphics, and highlights opportunities for future research. Our
primary goal is to bridge the gap between various techniques that have been
often independently proposed by different communities including mathematics and
statistics, computer vision and graphics, and medical image analysis
Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects
Robotic manipulation of deformable objects is a difficult problem especially
because of the complexity of the many different ways an object can deform.
Searching such a high dimensional state space makes it difficult to recognize,
track, and manipulate deformable objects. In this paper, we introduce a
predictive, model-driven approach to address this challenge, using a
pre-computed, simulated database of deformable object models. Mesh models of
common deformable garments are simulated with the garments picked up in
multiple different poses under gravity, and stored in a database for fast and
efficient retrieval. To validate this approach, we developed a comprehensive
pipeline for manipulating clothing as in a typical laundry task. First, the
database is used for category and pose estimation for a garment in an arbitrary
position. A fully featured 3D model of the garment is constructed in real-time
and volumetric features are then used to obtain the most similar model in the
database to predict the object category and pose. Second, the database can
significantly benefit the manipulation of deformable objects via non-rigid
registration, providing accurate correspondences between the reconstructed
object model and the database models. Third, the accurate model simulation can
also be used to optimize the trajectories for manipulation of deformable
objects, such as the folding of garments. Extensive experimental results are
shown for the tasks above using a variety of different clothing.Comment: 21 pages, 27 figure
Globally Optimal Joint Image Segmentation and Shape Matching Based on Wasserstein Modes
A functional for joint variational object segmentation and shape matching is
developed. The formulation is based on optimal transport w.r.t. geometric
distance and local feature similarity. Geometric invariance and modelling of
object-typical statistical variations is achieved by introducing degrees of
freedom that describe transformations and deformations of the shape template.
The shape model is mathematically equivalent to contour-based approaches but
inference can be performed without conversion between the contour and region
representations, allowing combination with other convex segmentation approaches
and simplifying optimization. While the overall functional is non-convex,
non-convexity is confined to a low-dimensional variable. We propose a locally
optimal alternating optimization scheme and a globally optimal branch and bound
scheme, based on adaptive convex relaxation. Combining both methods allows to
eliminate the delicate initialization problem inherent to many contour based
approaches while remaining computationally practical. The properties of the
functional, its ability to adapt to a wide range of input data structures and
the different optimization schemes are illustrated and compared by numerical
experiments.Comment: 31 pages, 16 figures. Accepted by Journal of Mathematical Imaging and
Vision, published online. Printed publication pendin
Recent Advance in Content-based Image Retrieval: A Literature Survey
The explosive increase and ubiquitous accessibility of visual data on the Web
have led to the prosperity of research activity in image search or retrieval.
With the ignorance of visual content as a ranking clue, methods with text
search techniques for visual retrieval may suffer inconsistency between the
text words and visual content. Content-based image retrieval (CBIR), which
makes use of the representation of visual content to identify relevant images,
has attracted sustained attention in recent two decades. Such a problem is
challenging due to the intention gap and the semantic gap problems. Numerous
techniques have been developed for content-based image retrieval in the last
decade. The purpose of this paper is to categorize and evaluate those
algorithms proposed during the period of 2003 to 2016. We conclude with several
promising directions for future research.Comment: 22 page
Squiggle - A Glyph Recognizer for Gesture Input
Squiggle is a template-based glyph recognizer in the lineage of `$1
Recognizer' and `Protractor'. It seeks a good fit linear affine mapping between
the input and template glyphs which are represented as a list of milestone
points along the glyph path. The algorithm can recognize input glyphs invariant
of rotation, scaling, skew, and reflection symmetries. In practice the
algorithm is fast and robust enough to recognize user-generated glyphs as they
are being drawn in real time, and to project `shadows' of the matching
templates as feedback.Comment: 10 page
A Performance Evaluation of Local Features for Image Based 3D Reconstruction
This paper performs a comprehensive and comparative evaluation of the state
of the art local features for the task of image based 3D reconstruction. The
evaluated local features cover the recently developed ones by using powerful
machine learning techniques and the elaborately designed handcrafted features.
To obtain a comprehensive evaluation, we choose to include both float type
features and binary ones. Meanwhile, two kinds of datasets have been used in
this evaluation. One is a dataset of many different scene types with
groundtruth 3D points, containing images of different scenes captured at fixed
positions, for quantitative performance evaluation of different local features
in the controlled image capturing situations. The other dataset contains
Internet scale image sets of several landmarks with a lot of unrelated images,
which is used for qualitative performance evaluation of different local
features in the free image collection situations. Our experimental results show
that binary features are competent to reconstruct scenes from controlled image
sequences with only a fraction of processing time compared to use float type
features. However, for the case of large scale image set with many distracting
images, float type features show a clear advantage over binary ones
Profile Based Sub-Image Search in Image Databases
Sub-image search with high accuracy in natural images still remains a
challenging problem. This paper proposes a new feature vector called profile
for a keypoint in a bag of visual words model of an image. The profile of a
keypoint captures the spatial geometry of all the other keypoints in an image
with respect to itself, and is very effective in discriminating true matches
from false matches. Sub-image search using profiles is a single-phase process
requiring no geometric validation, yields high precision on natural images, and
works well on small visual codebook. The proposed search technique differs from
traditional methods that first generate a set of candidates disregarding
spatial information and then verify them geometrically. Conventional methods
also use large codebooks. We achieve a precision of 81% on a combined data set
of synthetic and real natural images using a codebook size of 500 for top-10
queries; that is 31% higher than the conventional candidate generation
approach.Comment: Sub-Image Retrieval, New Feature Vector, Similarit
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