grey-level segmentation is an essential concept related with image processing and machine vision. Thresholding is a conversion between a grey-level image and a bilevel image. Bilevel image is a monochrome image only composed by black and white pixels. It should contain the most essential information of the image (i.e.‚ number‚ position and shape
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image interpolation depends on an image model that can adapt to nonstationary statistics of natural images when estimating the missing pixels. However‚ the construction of such an adaptive model needs the knowledge of every pixels that are absent. We resolve this dilemma by a new piecewise 2D autoregressive technique that builds the model and estimates the missing pixels jointly. This task is formulated as a non-linear optimization problem. Although computationally demanding‚ the new non-linear approach
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International Journal of Theoretical and Applied Computer Sciences Volume 1 Number 1 (2006) pp. 9–34 (c) GBS Publishers and Distributors (India) http://www.gbspublisher.com/ijtacs.htm Implementation and Evaluation of Image Processing Algorithms on Reconfigurable Architecture using C-based Hardware Descriptive Languages Daggu Venkateshwar Rao*‚ Shruti Patil‚ Naveen Anne Babu and V Muthukumar Department of Electrical and Computer Engineering University of Nevada Las Vegas. Las Vegas‚ NV 89154 *E-mail:
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known as pixels. Several types of resolutions are important when working with digitized images. They are: Image resolution refers to the spacing of pixels in the images and is measured in pixels per inch (ppi). If an image has a resolution of 72 ppi‚ this will have 5184 pixels in a square inch. Pixel depth (bit resolution) is a measurement of the number of bits of stored information per pixel. Bit resolution determines how much color information is available for each pixel. Greater pixel depth means
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A. Identification of similar pixels. B. Grouping of thematic information. C. Interpretation using prior knowledge. D. Making sense of geographic patterns. E. All of the above. 2. Principal components analysis is associated with which of the following? A. Data reduction. B. Training samples. C. Components and Eigenvectors. D. A and C. E. A‚ B and C. 3. What are spectral classes? A. Groups of pixels with similar spectral values. B. Groups of pixels with similar thematic information
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before the filtering operation. Most of these filtering techniques assume the presence of salt and pepper type of impulse noise. The detection of salt and pepper type of noise is relatively easy as there are only two intensity levels in the noisy pixels. However‚ the study reveals that in case of uniformly distributed impulse noise‚ these techniques do not perform well. In this paper a new algorithm is presented which improves the performance of switching median filter as a result of efficient detection
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Finally‚ Edge extraction using edge histogram is taken into account. The edge extraction method proposed in this paper is feasible. Index terms: digital image‚ edge detection‚ operators‚ edge histogram. Introduction: The edge is a set of those pixels whose grey have stepchange and rooftop change‚ and it exists between object andbackground‚ object and object‚ region and region‚ and betweenelement and element. Edge always indwells in twoneighboring areas having different grey level. It is the result
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else will know that the message exists (Bateman‚ P. and Schaathun‚ H. 2008). Images are considered as the most popular file formats used in steganography. They are known for constituting a non-causal medium‚ due to the possibility to access any pixel of the image at random. In addition‚ the hidden information could remain invisible to the eye. However‚ the image steganography techniques will exploit "holes" in the Human Visual System (Hamid‚ N. et al. 2012) Scientific Review As the computer
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finding its boundary precisely. An important factor in detecting tumor from healthy tissues is the difference in intensity level. However‚ relying only on the intensity level is usually not enough. The spatial information available in clusters of pixels that form a tumor should also be used in the detection process. Accurate measurements in brain diagnosis are quite difficult because of diverse shapes‚ sizes and appearances of tumors. Tumors can grow abruptly causing defects in neighbouring
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