10,342 research outputs found
Facial Expression Classification Using Rotation Slepian-based Moment Invariants
Rotation moment invariants have been of great interest in image processing
and pattern recognition. This paper presents a novel kind of rotation moment
invariants based on the Slepian functions, which were originally introduced in
the method of separation of variables for Helmholtz equations. They were first
proposed for time series by Slepian and his coworkers in the 1960s. Recent
studies have shown that these functions have an good performance in local
approximation compared to other approximation basis. Motivated by the good
approximation performance, we construct the Slepian-based moments and derive
the rotation invariant. We not only theoretically prove the invariance, but
also discuss the experiments on real data. The proposed rotation invariants are
robust to noise and yield decent performance in facial expression
classification.Comment: 13 pages, 4 figure
Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine
Content Based Image Retrieval (CBIR) systems based on shape using invariant
image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are
available in the literature. MI and ZM are good at representing the shape
features of an image. However, non-orthogonality of MI and poor reconstruction
of ZM restrict their application in CBIR. Therefore, an efficient and
orthogonal moment based CBIR system is needed. Legendre Moments (LM) are
orthogonal, computationally faster, and can represent image shape features
compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images
is proposed in this work. Superiority of the proposed CBIR system is observed
over other moment based methods, viz., MI and ZM in terms of retrieval
efficiency and retrieval time. Further, the classification efficiency is
improved by employing Support Vector Machine (SVM) classifier. Improved
retrieval results are obtained over existing CBIR algorithm based on Stacked
Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).Comment: 11 Pages, IJM
Rotation invariants of two dimensional curves based on iterated integrals
We introduce a novel class of rotation invariants of two dimensional curves
based on iterated integrals. The invariants we present are in some sense
complete and we describe an algorithm to calculate them, giving explicit
computations up to order six. We present an application to online
(stroke-trajectory based) character recognition. This seems to be the first
time in the literature that the use of iterated integrals of a curve is
proposed for (invariant) feature extraction in machine learning applications
Shape-Color Differential Moment Invariants under Affine Transformations
We propose the general construction formula of shape-color primitives by
using partial differentials of each color channel in this paper. By using all
kinds of shape-color primitives, shape-color differential moment invariants can
be constructed very easily, which are invariant to the shape affine and color
affine transforms. 50 instances of SCDMIs are obtained finally. In experiments,
several commonly used color descriptors and SCDMIs are used in image
classification and retrieval of color images, respectively. By comparing the
experimental results, we find that SCDMIs get better results.Comment: 13 pages, 4 figure
Image Projective Invariants
In this paper, we propose relative projective differential invariants (RPDIs)
which are invariant to general projective transformations. By using RPDIs and
the structural frame of integral invariant, projective weighted moment
invariants (PIs) can be constructed very easily. It is first proved that a kind
of projective invariants exists in terms of weighted integration of images,
with relative differential invariants as the weight functions. Then, some
simple instances of PIs are given. In order to ensure the stability and
discriminability of PIs, we discuss how to calculate partial derivatives of
discrete images more accurately. Since the number of pixels in discrete images
before and after the geometric transformation may be different, we design the
method to normalize the number of pixels. These ways enhance the performance of
PIs. Finally, we carry out some experiments based on synthetic and real image
datasets. We choose commonly used moment invariants for comparison. The results
indicate that PIs have better performance than other moment invariants in image
retrieval and classification. With PIs, one can compare the similarity between
images under the projective transformation without knowing the parameters of
the transformation, which provides a good tool to shape analysis in image
processing, computer vision and pattern recognition
Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition
Shapes and texture image recognition usage is an essential branch of pattern
recognition. It is made up of techniques that aim at extracting information
from images via human knowledge and works. Local Binary Pattern (LBP) ensures
encoding global and local information and scaling invariance by introducing a
look-up table to reflect the uniformity structure of an object. However, edge
direction matrixes (EDMS) only apply global invariant descriptor which employs
first and secondary order relationships. The main idea behind this methodology
is the need of improved recognition capabilities, a goal achieved by the
combinative use of these descriptors. This collaboration aims to make use of
the major advantages each one presents, by simultaneously complementing each
other, in order to elevate their weak points. By using multiple classifier
approaches such as random forest and multi-layer perceptron neural network, the
proposed combinative descriptor are compared with the state of the art
combinative methods based on Gray-Level Co-occurrence matrix (GLCM with EDMS),
LBP and moment invariant on four benchmark dataset MPEG-7 CE-Shape-1, KTH-TIPS
image, Enghlishfnt and Arabic calligraphy . The experiments have shown the
superiority of the introduced descriptor over the GLCM with EDMS, LBP and
moment invariants and other well-known descriptor such as Scale Invariant
Feature Transform from the literature
Using 3D Hahn Moments as A Computational Representation of ATS Drugs Molecular Structure
The campaign against drug abuse is fought by all countries, most notably on
ATS drugs. The technical limitations of the current test kits to detect new
brand of ATS drugs present a challenge to law enforcement authorities and
forensic laboratories. Meanwhile, new molecular imaging devices which allowed
mankind to characterize the physical 3D molecular structure have been recently
introduced, and it can be used to remedy the limitations of existing drug test
kits. Thus, a new type of 3D molecular structure representation technique
should be developed to cater the 3D molecular structure acquired physically
using these molecular imaging devices. One of the applications of image
processing methods to represent a 3D image is 3D moments, and this study
formulates a new 3D moments technique, namely 3D Hahn moments, to represent the
3D molecular structure of ATS drugs. The performance of the proposed technique
was analysed using drug chemical structures obtained from UNODC for the ATS
drugs, while non-ATS drugs are obtained randomly from ChemSpider database. The
evaluation shows the technique is qualified to be further explored in the
future works to be fully compatible with ATS drug identification domain
Rigid-Motion Scattering for Texture Classification
A rigid-motion scattering computes adaptive invariants along translations and
rotations, with a deep convolutional network. Convolutions are calculated on
the rigid-motion group, with wavelets defined on the translation and rotation
variables. It preserves joint rotation and translation information, while
providing global invariants at any desired scale. Texture classification is
studied, through the characterization of stationary processes from a single
realization. State-of-the-art results are obtained on multiple texture data
bases, with important rotation and scaling variabilities.Comment: 19 pages, submitted to International Journal of Computer Visio
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference
This paper presents a shape-theoretic framework for dynamical analysis of
nonlinear dynamical systems which appear frequently in several video-based
inference tasks. Traditional approaches to dynamical modeling have included
linear and nonlinear methods with their respective drawbacks. A novel approach
we propose is the use of descriptors of the shape of the dynamical attractor as
a feature representation of nature of dynamics. The proposed framework has two
main advantages over traditional approaches: a) representation of the dynamical
system is derived directly from the observational data, without any inherent
assumptions, and b) the proposed features show stability under different
time-series lengths where traditional dynamical invariants fail. We illustrate
our idea using nonlinear dynamical models such as Lorenz and Rossler systems,
where our feature representations (shape distribution) support our hypothesis
that the local shape of the reconstructed phase space can be used as a
discriminative feature. Our experimental analyses on these models also indicate
that the proposed framework show stability for different time-series lengths,
which is useful when the available number of samples are small/variable. The
specific applications of interest in this paper are: 1) activity recognition
using motion capture and RGBD sensors, 2) activity quality assessment for
applications in stroke rehabilitation, and 3) dynamical scene classification.
We provide experimental validation through action and gesture recognition
experiments on motion capture and Kinect datasets. In all these scenarios, we
show experimental evidence of the favorable properties of the proposed
representation.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligenc
Naturally Combined Shape-Color Moment Invariants under Affine Transformations
We proposed a kind of naturally combined shape-color affine moment invariants
(SCAMI), which consider both shape and color affine transformations
simultaneously in one single system. In the real scene, color and shape
deformations always exist in images simultaneously. Simple shape invariants or
color invariants can not be qualified for this situation. The conventional
method is just to make a simple linear combination of the two factors.
Meanwhile, the manual selection of weights is a complex issue. Our construction
method is based on the multiple integration framework. The integral kernel is
assigned as the continued product of the shape and color invariant cores. It is
the first time to directly derive an invariant to dual affine transformations
of shape and color. The manual selection of weights is no longer necessary, and
both the shape and color transformations are extended to affine transformation
group. With the various of invariant cores, a set of lower-order invariants are
constructed and the completeness and independence are discussed detailedly. A
set of SCAMIs, which called SCAMI24, are recommended, and the effectiveness and
robustness have been evaluated on both synthetic and real datasets
- …