Gabor filters for rotation invariant texture classification

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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

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

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS