We propose a novel approach for steganography using a reversible texture synthesis. A texture synthesis process resamples a smaller texture image, which synthesizes a new texture image with a similar local appearance and an arbitrary size. We weave the texture synthesis process into steganography to conceal secret messages. In contrast to using an existing cover image to hide messages, our algorithm conceals the source texture image and embeds secret messages through the process of texture synthesis. This allows us to extract the secret messages and source texture from a stego synthetic texture. Our approach offers three distinct advantages. First, our scheme offers the embedding capacity that is proportional to the size of the stego texture image. Second, a steganalytic algorithm is not likely to defeat our Steganography approach. Third, the reversible capability inherited from our scheme provides functionality, which allows recovery of the source texture. Experimental results have verified that our proposed algorithm can provide various numbers of embedding capacities, produce a visually plausible texture images, and recover the source texture.
In this paper, a completely unique human detection approach exploitation ironed 2-D bar graph, for automatic human detection in color image(s). During this approach, an eye fixed detector is employed to refine the skin model for a selected person. The planned approach reduces process prices as no coaching is needed, and it improves the accuracy of human detection despite wide variation in quality and illumination. This can be the primary technique to use fusion strategy for this purpose. First, associate degree approach is adopted to get the face(s) in a very given image. Second, a dynamic technique is used to calculate the skin threshold value(s) on the detected face(s) region. Third, the 2-D bar graph with ironed densities is introduced to represent the skin and Non-skin distributions, severally. Finally, we tend to ready to get the human detection results.Index Terms-Color space, dynamic threshold, Human detection, Microcontroller.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.