[Show abstract][Hide abstract] ABSTRACT: Image classification plays an important part in the fields of Remote sensing, Image analysis and Pattern recognition. Image classification can be done using conventional methods. But conventional methods lead to misclassification due to strictly convex boundaries. Textural features are included for better classification but are inconvenient for conventional methods. The proposed system uses textural feature based image classification using neural network. Textural features are extracted using Gray level co-occurrence matrix and artificial neural network is developed for the classification of images into different classes. Neural network is trained by supervised learning using standard back propagation algorithm for the classification of images.
[Show abstract][Hide abstract] ABSTRACT: The proposed system showed high hiding rates with reasonable imperceptibility compared to other steganographic systems, DCT and better audio quality. The results shown gives detail comparison between DWT and DCT. In this paper a novel method for digital audio Steganography with security i. e. cryptography is presented where covert data is embedded into the coefficients of host audio (cover signal) in integer wavelet domain using quantization to reduce embedding error. Performance analysis based on quantization and wavelet decomposition is explained in result section. The characteristics of this method are imperceptibility, robustness, large payload, high audio quality and full recovery.
[Show abstract][Hide abstract] ABSTRACT: In this paper a novel method for digital audio Steganography with security i.e cryptography is presented where covert data is embedded into the coefficients of host audio (cover signal) in integer wavelet domain. This is done using embedding algorithm. The coefficients are calculated using Haar transform in transform domain and these coefficients are used to embed secret data. The inverse integer wavelet transform is applied to the modified coefficients to form new audio sequence (stego signal) with the optimum pixel adjustment (OPA) algorithm. The OPA algorithm is applied after embedding secret message to minimize the embedding error. The proposed system showed high hiding rates with reasonable imperceptibility compared to other steganographic systems and better audio quality.
[Show abstract][Hide abstract] ABSTRACT: Image segmentation is the first step in image analysis and pattern recognition, and it is one of the most difficult tasks in image processing, and determines the quality of the final result of analysis. Image segmentation requires the improvement because most image segmentation solution is problem based. Biomedical image segmentation is different than normal image segmentation. Because normal image is taken from camera which is free of noise. While biomedical image is taken from MRI, CT, Microscopic Instrument or X-ray which is it noisy and fuzzy. Medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested. While extracting region of interest in biomedical image, region of interest can avoid the processing of irrelevant image point. Biomedical Image segmentation is necessary when we want the computer to make decision, surgical planning and robotically assisted surgical invention. Biomedical image segmentation is nothing but the classification of similar pixel and their following extraction into separate segment. There are different methods to do the Biomedical image segmentation but they are slow. he method which is described in this paper is fast and efficient. This paper gives comparative research of different method of each of category. The method described in the paper is for fast biomedical image segmentation