In this paper, a novel method for detecting Block Texture Patterns (BTP), based on two measures: smoothness and complexity of neighborhood pixels is proposed. With these two measures, a new classification for texture detection is defined. Texture detection with these measures can be used in many image processing and computer vision applications. As an example, the applicability of BTP on data hiding algorithms is discussed, and the advantages of this classification on these algorithms are shown.

In this paper, a hardware system for Sobel Edge Detection Algorithm is designed and simulated for a 128 pixel, 8-bit monochrome line-scan camera. The system is designed to detect objects as they move along a conveyor belt in a manufacturing environment, the camera will observe dark objects on a light conveyor belt. The edge detector is required to detect horizontal and vertical edges using Sobel edge detection method. The Sobel operator requires 3 lines and takes 3 pixels per line, thus using a 3×3 input block to process each pixel. The centre pixel of the 3×3 pixel block can be classified as an edge point or otherwise by thresholding the value from the operator. The FPGA based Sobel edge detector is designed and simulated using Altera Quartus II 8.1 web edition by targeting Cyclone II development boards.

Current paper proposes a novel robust dual watermarking scheme that entrench a decipherable sample in the spatial domain and an invisible watermark in frequency domain of a color video. Visible watermark facilitate in the protection of publicly accessible data thus providing an imperative contribution for avoiding illegal duplication of data. The adaptive visible watermarking algorithm exploits various histogram features and not only embeds a visible watermark in the spatial domain but also facilitates its extraction and removal thus endowing with the facet of reconstruction of the video. For enhancing the robustness of the scheme and increasing the degree of protection of the watermark, invisible watermark is scrambled in parts and embedded in various frames of the video in DCT domain. These dual watermarks are embedded in host video. The robustness of the scheme is verified with various signal processing attacks, geometric attacks and video watermarking attacks like frame averaging. Reconstruction of the video helps the authorized subscribers to refurbish the original video.

This paper presents a high speed image compression based on the combination of modified self-organizing maps and Back-Propagation neural networks. In the self-organizing model number of the neurons are in a flat topology. These neurons in interaction formed self-organizing neural network. The task this neural network is estimated a distribute function. Finally network disperses cells in the input space until estimated probability density of inputs. Distribute of neurons in input space probability is an information compression. So in the proposed method first by Modified Self-Organizing Feature Maps (MSOFM) we achieved distributed function of the input image by a weight vector then in the next stage these information compressed are applied to back-propagation algorithm until image again compressed. The performance of the proposed method has been evaluated using some standard images. The results demonstrate that the proposed method has High-speed over other existing works.

The subject of the research in this scientific paper are images on the websites, with special emphasis on displaying images chosen by the Web designer, along with its characteristic, on computers of various users. In addition, users can have different operating systems, different browsers, and different preferences in terms of their computers settings. An overall direction for using images and their characteristics when designing web pages, as well as some advice and opinions on the same topic are presented here. After that, several problems which arise from displaying images on the web pages of the computer of users are analyzed, for which a few solutions for the problems, as well as recommendations on which solution when to be chosen are also given in this text. A problem with a speed for loading web pages in correlation with size of images on those pages is studied as well. Then, problems with a speed for loading web pages in correlation with number of images on the page, problems with loading speed of second image on rollover, problems with a speed for loading web pages in correlation with size of background image, problems with texture in vertical bars used for background in web pages, and problems with users monitor size and background image are also analyzed. Finally, the problem with displaying the page without specifying image height and width is also considered.

In this paper, an efficient filter method for salt-and-pepper noise removal is proposed. This method is developed by using cubic B-spline. A noise detector is employed to check whether the selected pixel is noisy or noise free. In this method, noise free pixels are left unaltered. Since not every pixel is filtered, undue distortion can be avoided. Noise pixels are subjected to the filtering operation to reconstruct the intensity values of the noisy pixels. The noise free pixels are only considered in the filter operation. The cubic B-spline is used as a fitting function to generate additional values within the noise free pixels. The noisy pixel is replaced by the mean value of these pixel values. The window size is selected as 3 X 3 in the first step. If all pixels within the window are considered to be noise, then change the selected window size to 5 X 5. If all the pixels within this window are considered to be noise, then the noisy pixel is replaced by the previous resultant pixel. Comparison of the given filter with other existing filters is provided in this paper. The results demonstrate that the proposed technique can obtain better performances than other existing denoising techniques. As a result of this, the proposed method removes the noise effectively even at noise level as high as 97%.

This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

The search for a good representation is a central problem of image processing, this paper explores a new transform type to solve this problem. Color Image compression is now essential for applications such as transmission and storage in data. In the field of medical diagnostics, interested parties have resorted increasingly to color medical imaging. It is well established that the accuracy and completeness of diagnosis are initially connected with the image quality. This paper introduces an algorithm for color medical image compression based on the bandelet transform coupled with SP?HT coding algorithm;bandelet transform is a new method based on capturing the complex geometric content in image. The goal of this paper is to examine the capacity of this transform proposed to offer an optimal representation for image geometric, In order to enhance the compression by our algorithm, we have compared the results obtained with bandelet transform application in satellite image field. For this reason, we evaluated two parameters known for their calculation speed. The first parameter is the PSNR; the second is MSSIM (structural similarity) to measure the quality of compressed image. We concluded that the results obtained are very satisfactory for color medical image domain.