1,095 research outputs found
An evolutionary computational based approach towards automatic image registration
Image registration is a key component of various image processing operations
which involve the analysis of different image data sets. Automatic image
registration domains have witnessed the application of many intelligent
methodologies over the past decade; however inability to properly model object
shape as well as contextual information had limited the attainable accuracy. In
this paper, we propose a framework for accurate feature shape modeling and
adaptive resampling using advanced techniques such as Vector Machines, Cellular
Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be
effective in improving feature matching as well as resampling stages of
registration and complexity of the approach has been considerably reduced using
corset optimization The salient features of this work are cellular neural
network approach based SIFT feature point optimisation, adaptive resampling and
intelligent object modelling. Developed methodology has been compared with
contemporary methods using different statistical measures. Investigations over
various satellite images revealed that considerable success was achieved with
the approach. System has dynamically used spectral and spatial information for
representing contextual knowledge using CNN-prolog approach. Methodology also
illustrated to be effective in providing intelligent interpretation and
adaptive resampling.Comment: arXiv admin note: substantial text overlap with arXiv:1303.671
An N-dimensional approach towards object based classification of remotely sensed imagery
Remote sensing techniques are widely used for land cover classification and
urban analysis. The availability of high resolution remote sensing imagery
limits the level of classification accuracy attainable from pixel-based
approach. In this paper object-based classification scheme based on a
hierarchical support vector machine is introduced. By combining spatial and
spectral information, the amount of overlap between classes can be decreased;
thereby yielding higher classification accuracy and more accurate land cover
maps. We have adopted certain automatic approaches based on the advanced
techniques as Cellular automata and Genetic Algorithm for kernel and tuning
parameter selection. Performance evaluation of the proposed methodology in
comparison with the existing approaches is performed with reference to the
Bhopal city study area
A review over the applicability of image entropy in analyses of remote sensing datasets
Entropy is the measure of uncertainty in any data and is adopted for
maximisation of mutual information in many remote sensing operations. The
availability of wide entropy variations motivated us for an investigation over
the suitability preference of these versions to specific operations.Comment: arXiv admin note: substantial text overlap with arXiv:1303.692
Cellular Automata based adaptive resampling technique for the processing of remotely sensed imagery
Resampling techniques are being widely used at different stages of satellite
image processing. The existing methodologies cannot perfectly recover features
from a completely under sampled image and hence an intelligent adaptive
resampling methodology is required. We address these issues and adopt an error
metric from the available literature to define interpolation quality. We also
propose a new resampling scheme that adapts itself with regard to the pixel and
texture variation in the image. The proposed CNN based hybrid method has been
found to perform better than the existing methods as it adapts itself with
reference to the image features
An investigation towards wavelet based optimization of automatic image registration techniques
Image registration is the process of transforming different sets of data into
one coordinate system and is required for various remote sensing applications
like change detection, image fusion, and other related areas. The effect of
increased relief displacement, requirement of more control points, and
increased data volume are the challenges associated with the registration of
high resolution image data. The objective of this research work is to study the
most efficient techniques and to investigate the extent of improvement
achievable by enhancing them with Wavelet transform. The SIFT feature based
method uses the Eigen value for extracting thousands of key points based on
scale invariant features and these feature points when further enhanced by the
wavelet transform yields the best results
A Comparative Analysis on the Applicability of Entropy in remote sensing
Entropy is the measure of uncertainty in any data and is adopted for
maximisation of mutual information in many remote sensing operations. The
availability of wide entropy variations motivated us for an investigation over
the suitability preference of these versions to specific operations.
Methodologies were implemented in Matlab and were enhanced with entropy
variations. Evaluation of various implementations was based on different
statistical parameters with reference to the study area The popular available
versions like Tsalli's, Shanon's, and Renyi's entropies were analysed in
context of various remote sensing operations namely thresholding, clustering
and registration
Bipedal Model Based on Human Gait Pattern Parameters for Sagittal Plane Movement
The present research as described in this paper tries to impart how imitation
based learning for behavior-based programming can be used to teach the robot.
This development is a big step in way to prove that push recovery is a software
engineering problem and not hardware engineering problem. The walking algorithm
used here aims to select a subset of push recovery problem i.e. disturbance
from environment. We applied the physics at each joint of Halo with some degree
of freedom. The proposed model, Halo is different from other models as
previously developed model were inconsistent with data for different persons.
This would lead to development of the generalized biped model in future and
will bridge the gap between performance and inconsistency. In this paper the
proposed model is applied to data of different persons. Accuracy of model,
performance and result is measured using the behavior negotiation capability of
model developed. In order to improve the performance, proposed model gives the
freedom to handle each joint independently based on the belongingness value for
each joint. The development can be considered as important development for
future world of robotics. The accuracy of model is 70% in one go
Comparative analysis of common edge detection techniques in context of object extraction
Edges characterize boundaries and are therefore a problem of practical
importance in remote sensing.In this paper a comparative study of various edge
detection techniques and band wise analysis of these algorithms in the context
of object extraction with regard to remote sensing satellite images from the
Indian Remote Sensing Satellite (IRS) sensors LISS 3, LISS 4 and Cartosat1 as
well as Google Earth is presented
An intelligent approach towards automatic shape modeling and object extraction from satellite images using cellular automata based algorithm
Automatic feature extraction domain has witnessed the application of many
intelligent methodologies over past decade; however detection accuracy of these
approaches were limited as object geometry and contextual knowledge were not
given enough consideration. In this paper, we propose a frame work for accurate
detection of features along with automatic interpolation, and interpretation by
modeling feature shape as well as contextual knowledge using advanced
techniques such as SVRF, Cellular Neural Network, Core set, and MACA. Developed
methodology has been compared with contemporary methods using different
statistical measures. Investigations over various satellite images revealed
that considerable success was achieved with the CNN approach. CNN has been
effective in modeling different complex features effectively and complexity of
the approach has been considerably reduced using corset optimization. The
system has dynamically used spectral and spatial information for representing
contextual knowledge using CNN-prolog approach. System has been also proved to
be effective in providing intelligent interpolation and interpretation of
random features
An enhanced neural network based approach towards object extraction
The improvements in spectral and spatial resolution of the satellite images
have facilitated the automatic extraction and identification of the features
from satellite images and aerial photographs. An automatic object extraction
method is presented for extracting and identifying the various objects from
satellite images and the accuracy of the system is verified with regard to IRS
satellite images. The system is based on neural network and simulates the
process of visual interpretation from remote sensing images and hence increases
the efficiency of image analysis. This approach obtains the basic
characteristics of the various features and the performance is enhanced by the
automatic learning approach, intelligent interpretation, and intelligent
interpolation. The major advantage of the method is its simplicity and that the
system identifies the features not only based on pixel value but also based on
the shape, haralick features etc of the objects. Further the system allows
flexibility for identifying the features within the same category based on size
and shape. The successful application of the system verified its effectiveness
and the accuracy of the system were assessed by ground truth verification
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