3,241 research outputs found
Detection, Recognition and Tracking of Moving Objects from Real-time Video via SP Theory of Intelligence and Species Inspired PSO
In this paper, we address the basic problem of recognizing moving objects in
video images using SP Theory of Intelligence. The concept of SP Theory of
Intelligence which is a framework of artificial intelligence, was first
introduced by Gerard J Wolff, where S stands for Simplicity and P stands for
Power. Using the concept of multiple alignment, we detect and recognize object
of our interest in video frames with multilevel hierarchical parts and
subparts, based on polythetic categories. We track the recognized objects using
the species based Particle Swarm Optimization (PSO). First, we extract the
multiple alignment of our object of interest from training images. In order to
recognize accurately and handle occlusion, we use the polythetic concepts on
raw data line to omit the redundant noise via searching for best alignment
representing the features from the extracted alignments. We recognize the
domain of interest from the video scenes in form of wide variety of multiple
alignments to handle scene variability. Unsupervised learning is done in the SP
model following the DONSVIC principle and natural structures are discovered via
information compression and pattern analysis. After successful recognition of
objects, we use species based PSO algorithm as the alignments of our object of
interest is analogues to observation likelihood and fitness ability of species.
Subsequently, we analyze the competition and repulsion among species with
annealed Gaussian based PSO. We have tested our algorithms on David, Walking2,
FaceOcc1, Jogging and Dudek, obtaining very satisfactory and competitive
results
Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization
Classification predicts classes of objects using the knowledge learned during
the training phase. This process requires learning from labeled samples.
However, the labeled samples usually limited. Annotation process is annoying,
tedious, expensive, and requires human experts. Meanwhile, unlabeled data is
available and almost free. Semi-supervised learning approaches make use of both
labeled and unlabeled data. This paper introduces cluster and label approach
using PSO for semi-supervised classification. PSO is competitive to traditional
clustering algorithms. A new local best PSO is presented to cluster the
unlabeled data. The available labeled data guides the learning process. The
experiments are conducted using four state-of-the-art datasets from different
domains. The results compared with Label Propagation a popular semi-supervised
classifier and two state-of-the-art supervised classification models, namely
k-nearest neighbors and decision trees. The experiments show the efficiency of
the proposed model
A Tunable Particle Swarm Size Optimization Algorithm for Feature Selection
Feature selection is the process of identifying statistically most relevant
features to improve the predictive capabilities of the classifiers. To find the
best features subsets, the population based approaches like Particle Swarm
Optimization(PSO) and genetic algorithms are being widely employed. However, it
is a general observation that not having right set of particles in the swarm
may result in sub-optimal solutions, affecting the accuracies of classifiers.
To address this issue, we propose a novel tunable swarm size approach to
reconfigure the particles in a standard PSO, based on the data sets, in real
time. The proposed algorithm is named as Tunable Particle Swarm Size
Optimization Algorithm (TPSO). It is a wrapper based approach wherein an
Alternating Decision Tree (ADT) classifier is used for identifying influential
feature subset, which is further evaluated by a new objective function which
integrates the Classification Accuracy (CA) with a modified F-Score, to ensure
better classification accuracy over varying population sizes. Experimental
studies on bench mark data sets and Wilcoxon statistical test have proved the
fact that the proposed algorithm (TPSO) is efficient in identifying optimal
feature subsets that improve classification accuracies of base classifiers in
comparison to its standalone form.Comment: 7 pages, 1 figure, This paper is accepted for oral presentation at
IEEE Congress on Evolutionary Computation (CEC) - WCCI 2018, Rio de Janerio,
Brazil, #1812
A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systems
We propose a technique to detect and generate patterns in a network of
locally interacting dynamical systems. Central to our approach is a novel
spatial superposition logic, whose semantics is defined over the quad-tree of a
partitioned image. We show that formulas in this logic can be efficiently
learned from positive and negative examples of several types of patterns. We
also demonstrate that pattern detection, which is implemented as a model
checking algorithm, performs very well for test data sets different from the
learning sets. We define a quantitative semantics for the logic and integrate
the model checking algorithm with particle swarm optimization in a
computational framework for synthesis of parameters leading to desired patterns
in reaction-diffusion systems
Automated Simulations of Galaxy Morphology Evolution using Deep Learning and Particle Swarm Optimisation
The formation of Hoag-type galaxies with central spheroidal galaxies and
outer stellar rings has yet to be understood in astronomy. We consider that
these unique objects were formed from the past interaction between elliptical
galaxies and gas-rich dwarf galaxies. We have modelled this potential formation
process through simulation. These numerical simulations are a means of
investigating this formation hypothesis, however the parameter space to be
explored for these simulations is vast. Through the application of machine
learning and computational science, we implement a new two-fold method to find
the best model parameters for stellar rings in the simulations. First, test
particle simulations are run to find a possible range of parameters for which
stellar rings can be formed around elliptical galaxies (i.e. Hoag-type
galaxies). A novel combination of particle swarm optimisation and Siamese
neural networks has been implemented to perform the search over the parameter
space and test the level of consistency between observations and simulations
for numerous models. Upon the success of this initial step, we subsequently run
full chemodynamical simulations for the derived range of model parameters in
order to verify the output of the test particle simulations. We successfully
find parameter sets at which stellar rings can be formed from the interaction
between a gas-rich dwarf galaxy and a central elliptical galaxy. This is
evidence that supports our hypothesis about the formation process of Hoag-type
galaxies. In addition, this suggests that our new two-fold method has been
successfully implemented in this problem search-space and can be investigated
further in future applications. ~Comment: 32 pages: Master thesis at UWA (Computer science
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
Particle Swarm Optimization (PSO) is a metaheuristic global optimization
paradigm that has gained prominence in the last two decades due to its ease of
application in unsupervised, complex multidimensional problems which cannot be
solved using traditional deterministic algorithms. The canonical particle swarm
optimizer is based on the flocking behavior and social co-operation of birds
and fish schools and draws heavily from the evolutionary behavior of these
organisms. This paper serves to provide a thorough survey of the PSO algorithm
with special emphasis on the development, deployment and improvements of its
most basic as well as some of the state-of-the-art implementations. Concepts
and directions on choosing the inertia weight, constriction factor, cognition
and social weights and perspectives on convergence, parallelization, elitism,
niching and discrete optimization as well as neighborhood topologies are
outlined. Hybridization attempts with other evolutionary and swarm paradigms in
selected applications are covered and an up-to-date review is put forward for
the interested reader.Comment: 34 pages, 7 table
A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking
In recent years, artificial intelligence (AI) based on deep learning (DL) has
sparked tremendous global interest. DL is widely used today and has expanded
into various interesting areas. It is becoming more popular in cross-subject
research, such as studies of smart city systems, which combine computer science
with engineering applications. Human action detection is one of these areas.
Human action detection is an interesting challenge due to its stringent
requirements in terms of computing speed and accuracy. High-accuracy real-time
object tracking is also considered a significant challenge. This paper
integrates the YOLO detection network, which is considered a state-of-the-art
tool for real-time object detection, with motion vectors and the Coyote
Optimization Algorithm (COA) to construct a real-time human action localization
and tracking system. The proposed system starts with the extraction of motion
information from a compressed video stream and the extraction of appearance
information from RGB frames using an object detector. Then, a fusion step
between the two streams is performed, and the results are fed into the proposed
action tracking model. The COA is used in object tracking due to its accuracy
and fast convergence. The basic foundation of the proposed model is the
utilization of motion vectors, which already exist in a compressed video bit
stream and provide sufficient information to improve the localization of the
target action without requiring high consumption of computational resources
compared with other popular methods of extracting motion information, such as
optical flows. This advantage allows the proposed approach to be implemented in
challenging environments where the computational resources are limited, such as
Internet of Things (IoT) systems.Comment: SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING
SYSTEM
LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques
Optimization techniques play an important role in several scientific and
real-world applications, thus becoming of great interest for the community. As
a consequence, a number of open-source libraries are available in the
literature, which ends up fostering the research and development of new
techniques and applications. In this work, we present a new library for the
implementation and fast prototyping of nature-inspired techniques called
LibOPT. Currently, the library implements 15 techniques and 112 benchmarking
functions, as well as it also supports 11 hypercomplex-based optimization
approaches, which makes it one of the first of its kind. We showed how one can
easily use and also implement new techniques in LibOPT under the C paradigm.
Examples are provided with samples of source-code using benchmarking functions
Motion correction of PET/CT images
Indiana University-Purdue University Indianapolis (IUPUI)The advances in health care technology help physicians make more accurate diagnoses about the health conditions of their patients. Positron Emission Tomography/Computed Tomography (PET/CT) is one of the many tools currently used to diagnose health and disease in patients. PET/CT explorations are typically used to detect: cancer, heart diseases, disorders in the central nervous system. Since PET/CT studies can take up to 60 minutes or more, it is impossible for patients to remain motionless throughout the scanning process. This movements create motion-related artifacts which alter the quantitative and qualitative results produced by the scanning process. The patient's motion results in image blurring, reduction in the image signal to noise ratio, and reduced image contrast, which could lead to misdiagnoses.
In the literature, software and hardware-based techniques have been studied to implement motion correction over medical files. Techniques based on the use of an external motion tracking system are preferred by researchers because they present a better accuracy. This thesis proposes a motion correction system that uses 3D affine registrations using particle swarm optimization and an off-the-shelf Microsoft Kinect camera to eliminate or reduce errors caused by the patient's motion during a medical imaging study
Intelligent Nanophotonics: Merging Photonics and Artificial Intelligence at the Nanoscale
Nanophotonics has been an active research field over the past two decades,
triggered by the rising interests in exploring new physics and technologies
with light at the nanoscale. As the demands of performance and integration
level keep increasing, the design and optimization of nanophotonic devices
become computationally expensive and time-inefficient. Advanced computational
methods and artificial intelligence, especially its subfield of machine
learning, have led to revolutionary development in many applications, such as
web searches, computer vision, and speech/image recognition. The complex models
and algorithms help to exploit the enormous parameter space in a highly
efficient way. In this review, we summarize the recent advances on the emerging
field where nanophotonics and machine learning blend. We provide an overview of
different computational methods, with the focus on deep learning, for the
nanophotonic inverse design. The implementation of deep neural networks with
photonic platforms is also discussed. This review aims at sketching an
illustration of the nanophotonic design with machine learning and giving a
perspective on the future tasks.Comment: 46 pages, 14 figures. To appear in Nanophotonic
- …