Abstract:Brain tumor segmentation is quite popular area of research but detection of its surface texture is challenging for researchers. Normally, MRI datasets have very low resolution. This paper utilizes image enhancement technique based on wavelet. It is used to scale the low resolution image to a suitable resolution without loss. Secondly the proposed method is focused on implementation of a trained classifier using features: fractal dimension, fractal area, and wavelet average to classify type of texture present in brain tumor.

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Abstract.We have explored a new dimension in image steganography and propose a deft method for image-secret data -keyword (steg key) based sampling, encryption and embedding the former with a variable bit retrieval function. The keen association of the image, secret data and steg key, varied with a pixel dependant embedding results in a highly secure, reliable L.S.B. substitution. Meticulous statistical analyses have been provided to emphasize the strong immunity of the algorithm to the various steganalysis methods in the later sections of the paper.

Human Face and facial parts are the most significant parts as it reveals a person's true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person's identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

Wavelet analysis being a relatively new subject of study is being explored, all around the globe, using various mathematical tools, currently available. This paper is a humble attempt to provide a comprehensive study of the same, by means of exhaustive mathematical analysis. Since frame theory has been established as a standard notion in applied mathematics, so it was used as the analytical tool to explain the formation, purpose and use of wavelets. The theoretical explanation follows the mathematical analysis, which is an attempt to give picture the ‘theorems’, ‘definitions’ and the ‘lemmas’.