Development of Parallel Algorithms for Computer Vision

Computer vision is an important research area where computationally-intensive and time critical problems need to be solved routinely. This paper described some parallel algorithms for image processing and computational geometry applicable to the field of 'robot vision' which was developed using PACE parallel computer, a closely coupled message passing MIMD machine, designed and developed at the Advanced Numerical Reserach & Analysis Group, Hyderabad

Implementation of Image Registration Algorithms for Real-time Target Tracking Through Video Sequences

"Automatic detection and tracking of interesting targets from a sequence of images obtained from a reconnaissance platform is an interesting area of research for defence-related applications. Image registration is the basic step used in target tracking application. The paper briefly reviews some of the image registration algorithms, analyse their performance using a suitable image processing hardware, and selects the most suitable algorithm for a real-time target tracking application using cubic-spline model and spline model Kalman filter for the prediction of an occluded target. The algorithms developed are implemented in a ground-based image exploitation system (GIES) developed at the Aeronautical Development Establishment for unmanned aerial vehicle application, and the results presented for the images obtained during actual flight trial

Terrain Classification using Multiple Image Features

A wide variety of image processing applications require segmentation and classification ofimages. The problem becomes complex when the images are obtained in an uncontrolledenvironment with a non-uniform illumination. The selection of suitable features is a critical partof an image segmentation and classification process, where the basic objective is to identify theimage regions that are homogeneous but dissimilar to all spatially adjacent regions. This paperproposes an automatic method for the classification of a terrain using image features such asintensity, texture, and edge. The textural features are calculated using statistics of geometricalattributes of connected regions in a sequence of binary images obtained from a texture image.A pixel-wise image segmentation scheme using a multi-resolution pyramid is used to correct thesegmentation process so as to get homogeneous image regions. Localisation of texture boundariesis done using a refined-edge map obtained by convolution, thinning, thresholding, and linking.The individual regions are classified using a database generated from the features extracted fromknown samples of the actual terrain. The algorithm is used to classify airborne images of a terrainobtained from the sensor mounted on an aerial reconnaissance platform and the results arepresented

Automatic Classification of Aerial Imagery

The aerial imagery obtained from reconnaissance platform is voluminous and the defenceforces rely on image information to perform intelligent tasks. The application of a welldesigned automatic image classifier would enhance the end results of different high levelapplications thereby abridging the effort of a human analyst. Automatic image classifierscould be designed using a training data set for supervised learning or using an unsupervisedlearning. In this paper, a method, which combines both unsupervised and supervised methodsof learning is proposed

Warehouse Review Classification Using Naïve Bayes Classifier

A customer review is a review of a product or service made by a customer who has purchased the product or service. Customer reviews are a form of customer feedback on electronic commerce and online shopping sites. In this study, a review of a warehouse by a customer who has booked a warehouse for storing their goods will be classified. By this classification a customer will come to know whether the warehouse is suitable for storing their goods or not as the proposed model will classify the reviews into positive and negative reviews.. This paper proposes an approach to perform subjectivity classification on feedback text based on a supervised machine learning algorithm, Naive Bayes. Experiment studies have been conducted on warehouse reviews. The results show that the performances of the proposed approach are comparable to those of the existing english subjectivity classification studies

Image Exploitation-A Forefront Area for UAV Application

Image exploitation, an innovative image utilisation program uses high revisit multisensor, multiresolution imagery from unmanned air vehicle or other reconnaissance platform for intelligent information gathering. This paper describes the imagc exploitation system developed at the Aeronautical Dcvclopment Establishment, Bangalore, for the remotely piloted vehicle (RPV) Nishonr and highlights two major areas (i) In-flight imagc exploitation, and (ii) post-flight imagc cxploitatlon. In-flight imagc study includes real-timeenhancement of images frames during RPV flight. target acquisition. calculation of geo-location of targets, distance and area computation, and image-to-map correspondence. Post-flight image exploitation study includes image restoration, classtfication of terrain, 3-D depth computation using stereo vision and shape from shading techniques. The paper shows results obtained in each of these areas from actual flight trials

Image Enhancement by Histogram Specification Using Multiple Target Images

Shape and characteristics of the histogram plays a major role in finding the quality of an image. Histogram Specification is an image enhancement technique, where the histogram of the input image is transformed to a pre-specified histogram derived from a high resolution image, called target image. In this paper, the classical histogram specification technique is extended by using a target image which is obtained by fusing multiple high resolution images. A set of Quality Metrics were identified to assess the quality of the output enhanced image. The paper addresses the following issues: a) Effect of varying the number of target images on the quality of the output enhanced image b) Role of using different methods of fusion on the quality of the output enhanced image c) Category of the target image on the quality of the output enhanced image. If the input image is from a forest, whether in order to obtain an enhanced image, all target images has to be selected from the forest category d) Effect of preprocessing of target image on the quality of the output enhanced image

Enhancement of Aerial and Medical Image using Multi resolution pyramid

Image enhancement has been an area of active research for decades. Most of the studies are aimed at improving the quality of image for better visualization. An approach for contrast enhancement utilizing multi-scale analysis is introduced. To show the effects of image enhancement, quantitative measures should be introduced. In this paper, we examine the effect of global and local enhancement using multi resolution pyramids. We identify a set of quality metric parameters for comparative performance analysis and use it to assess the enhanced output image for a number of image enhancement algorithms using pyramid