Automatic video surveillance systems typically involve tasks of moving object detection, object tracking, event analysis and detection of abnormality. One of the key requirements of scene understanding is detection an...
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Automatic video surveillance systems typically involve tasks of moving object detection, object tracking, event analysis and detection of abnormality. One of the key requirements of scene understanding is detection and removal of moving shadows. In the context of real time automatic surveillance systems, considering the various tasks to be performed, detection of moving shadows needs to be performed with minimal computational complexity. Many algorithms have been proposed in the literature that deals with moving shadow detection. The existing algorithms treat tracking of objects and shadow detection tasks in an isolated manner. In this paper we present a technique for shadow detection, which uses motion information from the tracking module and hence greatly reduces the computational complexity.
Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures o...
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Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines, the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.
Perceptual image hashing maps an image to a fixed length binary string based on the image's appearance to the human eye, and has applications in image indexing, authentication, and watermarking. We present a gener...
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Perceptual image hashing maps an image to a fixed length binary string based on the image's appearance to the human eye, and has applications in image indexing, authentication, and watermarking. We present a general framework for perceptual image hashing using feature points. The feature points should be largely invariant under perceptually insignificant distortions. To satisfy this, we propose an iterative feature detector to extract significant geometry preserving feature points. We apply probabilistic quantization on the derived features to enhance perceptual robustness further. The proposed hash algorithm withstands standard benchmark (e.g. Stirmark) attacks including compression, geometric distortions of scaling and small angle rotation, and common signal processing operations. Content changing (malicious) manipulations of image data are also accurately detected.
In this paper, we define the intrinsic shape of an object in three-dimensional (3D) space as the shape invariant, to affine-permutation geometric shape distortions. We present an algorithm that blindly recovers from a...
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In this paper, we define the intrinsic shape of an object in three-dimensional (3D) space as the shape invariant, to affine-permutation geometric shape distortions. We present an algorithm that blindly recovers from an arbitrarily affine-permutation distorted shape its intrinsic shape. This algorithm referred to us 3D BLAlSER (blind algorithm for intrinsic shape recovery) executes as one of its steps 3D PRA (point-based reorientation algorithm). 3D PRA reorients (blindly) rotated versions of the same object so that they become exactly aligned. 3D PRA extends 2D PRA to 3D space, but is much more complicated due to the multiple axes of rotation and the associated fold numbers. We describe the algorithms of 3D BLAISER and 3D PRA in detail.
We intend to fill the methodological-level gaps which exist in the theory of imaging radar (IR) for remote sensing (RS) systems by addressing a novel look at RS imaging as an ill-conditioned inverse problem with model...
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We intend to fill the methodological-level gaps which exist in the theory of imaging radar (IR) for remote sensing (RS) systems by addressing a novel look at RS imaging as an ill-conditioned inverse problem with model uncertainties. We extend the theory presented in previous studies by developing the fused Bayesian-regularization method for RS image formation subject to the projection-type regularization constraints imposed on the solution. Next, we propose to employ neural network-based-processing for efficient implementation of the developed radar image enhancing algorithms and include some simulation examples to illustrate the overall performances of the proposed approach. Our study is intended to establish the foundation to assist in understanding the basic theoretical aspects of the multi-level (Bayesian-regularization-neural-network-computing) optimization of signal processing techniques for enhancing RS imagery.
The paper provides a review into the steps involved in acquiring and processing synthetic aperture radar (SAR) data transmitted by the European remote sensing (ERS) satellites. The paper reports on a simple MATLAB-bas...
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The paper provides a review into the steps involved in acquiring and processing synthetic aperture radar (SAR) data transmitted by the European remote sensing (ERS) satellites. The paper reports on a simple MATLAB-based SAR processing system, that reads the image out of the complex SAR data files and that is suitable for use in the classroom to demonstrate one of the procedures used in SAR data processing. The paper can also help a beginner in the field of SAR signal processing to get information and understand the basics that are necessary to acquire and process a SAR image. The data, provided by the Alaskan Satellite Facility (ASF), is categorized into different levels and the paper describes the process of obtaining the level-1 basic image from the level-0 raw data file provided by ASF.
This paper presents the behavioral simulation results of an optical flow estimation (OFE) algorithm, the Lukas-Kanade (LK) method, for a cooperative analog/digital signal processing (CADSP) system design. First, perfo...
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This paper presents the behavioral simulation results of an optical flow estimation (OFE) algorithm, the Lukas-Kanade (LK) method, for a cooperative analog/digital signal processing (CADSP) system design. First, performance tests are executed considering the limited-bit resolution of analog circuitry. To incorporate general analog circuit noise, OFE is evaluated with additive Gaussian noise. Finally, OFE that depends on noise from the CMOS image sensor is presented. Bit-level simulations show that different reduced-bit resolutions for each dimension in smoothing can make OFE very little affected. The experimental results can provide a guideline for system specification of CADSP system design or the efficient implementation of an OFE algorithm using reduced precision arithmetic.
Procedures of various feature extractions in synchronous modes, segmentation for the subsequent coding and reconstruction of the initial gray-tone image are realized. The model of a two-dimensional layer describing a ...
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Procedures of various feature extractions in synchronous modes, segmentation for the subsequent coding and reconstruction of the initial gray-tone image are realized. The model of a two-dimensional layer describing a one-layer (one-component) distributed neuron-like system is used for the computer simulation. The system consists of active neuron-like elements with a nonlocal coupling function between them. Analysis of possible collective activity patterns in such a system is carried out. The developed algorithm of image segmentation and reconstruction belongs to the class of synchronous data-processingalgorithms.
The architecture of a data dividing based airborne SAR real-time imaging system is discussed, and its merit and const merit are analyzed. Aimed at RD imaging algorithms, the analysis of real-time based on granularity ...
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The architecture of a data dividing based airborne SAR real-time imaging system is discussed, and its merit and const merit are analyzed. Aimed at RD imaging algorithms, the analysis of real-time based on granularity is proposed, and the implementation of a real-time imaging system is given. The imaging results with raw data are in agreement with the theoretical analysis.
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