Steganography is an art of concealing the fact with the purpose of communication, by hiding one information in other information. In steganography, many different carrier file formats are used now days, but the digital images are most popular for hiding information because of their frequency on Internet. There is a large variety of techniques of steganography are available for hiding secret information into images. Each of them has its strong & weak points. Choice of which steganography technique is used it depends on the different requirements of the application. For example, some applications may have need of a bigger secret message to be concealed and some need absolute invisibility of the secret message. This paper gives an overview of different techniques used for image steganography. Among these following techniques DCT & DWT techniques are widely used because of their efficiency.

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

Video Shot boundary detection is the process of automatically detecting the boundaries between shots in video. Shot Boundary detection is an important fundamental process in video data access, indexing, search and retrieval. The increased availability and usage of on-line digital video has created a need for automated video content analysis techniques. Detection of gradual transition and elimination of disturbances caused by illumination change is a major challenge in shot boundary detection technique. These disturbances are often mistaken as shot boundaries. It is a crucial task to develop a method that is not only insensitive to illumination change but also sensitive to detect shot change. An algorithm is proposed for shot boundary detection in presence of illumination change. This is very important for accurate detection of shot boundaries and very useful in content based analysis of video. First the algorithm removes illumination change using discrete cosine transform and discrete wavelet transform. Then shot boundaries are detected using normal difference & wavelet difference. A shot boundary is detected when the feature difference shows sharp change greater than threshold. Experimental study is performed on number of videos that include significant illumination change. The performance of proposed algorithm is better as compared to existing techniques General TermsVideo shot boundary detection, Image processing, Pattern recognition.

CBIR system focuses on retrieving images from the database; the system depends on the way the indexing is being implemented. The way or method in which an image is stored will affect how it will be retrieved later and which can save more storage space and improve the retrieval process. Building effective content-based image retrieval (CBIR) systems involves the combination of image creation, storage, security, transmission, analysis, evaluation feature extraction, and feature combination in order to store and retrieve images effectively. The goal of CBIR systems is to support image retrieval based on content i.e. shape, color, texture. In this paper we have implemented CBIR techniques using conventional Histogram and Gabor filter. We have shown results of query image and retrieved image also 2D frequency response of Gabor filter with various angles as it is direction dependent filter. We have used Euclidean distance as a measure to calculate distance between two images.

Abstract. Video shot transition identification constitutes an important computer vision research field, being applied, as an essential step, in many digital video analysis domains: video scene detection, video compression, video indexing, video content retrieval and video object tracking. In this paper we propose a novel technique for shot boundary detection using finite ridgelet transform aiming to obtain fast and accurate boundary detection. We devise new two step algorithm for automatic shot boundary detection. Firstly effect of illumination change is removed using DCT and DWT. Then shot boundary is detected using finite ridgelet transform. The ridgelet transform was introduced as a sparse expansion for functions on continuous spaces that are smooth away from discontinuities along lines. This transform is a new directional resolution transform and it is more suitable for describing the signals with line or superplane singularities. Finite ridgelet transform is a discrete orthonormal version of ridgelet transform. Experimental result indicates that finite ridgelet transform offers an efficient representation for frames that are smooth away from line discontinuities or straight edges.