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