Image segmentation plays a crucial role in effective understanding of digital images. Past few decades saw hundreds of research contributions in this field. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. This paper critically reviews existing important graph based segmentation methods. The review is done based on the classification of various segmentation algorithms within the framework of graph based approaches. The major four categorizations we have employed for the purpose of review are: graph cut based methods, interactive methods, minimum spanning tree based methods and pyramid based methods. This review not only reveals the pros in each method and category but also explores its limitations. In addition, the review highlights the need for creating a database for benchmarking intensity based algorithms, and the need for further research in graph based segmentation for automated real time applications.

In this Paper, We worked and documented the implementation and performance analysis of digital video watermarking that uses the hybrid features of two of the most powerful transform domain processing of the video and fundamentals of the linear algebra. We have taken into the account fundamentals of Discrete Cosine Transform and Singular Value Decomposition for the development of the proposed algorithm. We first used the Singular Value Decomposition and then used the singular values for the insertion of the message behind the video. Finally we used two of the visual quality matrices for the analysis purpose. We also applied various attacks on the video and found the proposed scheme more robust.

This paper presents an analysis of stabilogram using the modified Principal Component Analysis (mPCA) decomposition which will be employed to highlight the effects of different aspects on the human postural stability.
The aim of this study is to analyze stabilogram center of pressure time series using the mPCA decomposition method. The mPCA is a decomposition method applied to a complex signal. It decomposes the stabilogram, considered as an additive model, into three components: trend, rambling and trembling. The study of the trace of analytic trembling (respectively of rambling) in the complex plan highlights a unique rotation center. So the phase is defined and two parameters are extracted: the area of the circle in which 95% of the trace's data points are located and the angular frequency. In this study 25 healthy volunteers (average age 31± 11 years) are required to stand upright on an electromagnetic platform either with eyes closed or open and with feet outspread or tighten.
Experimental results show the efficiency of the parameter area to identify the effect of visual, proprioceptive and directional entries on the postural stability. These results are able to discriminate between control and young groups and indicate a less well-controlled posture for control subjects (34.5± 7.5y) relatively to young subjects (22.5 ±2. 5y). Results serve also to display that female subjects are more stable than males, that fat subjects are more stable than thin and that tall subjects are more stable than small.

Image segmentation is a vital part of many applications because it makes possible for the information extraction and analysis of image contents. For image segmentation process, many approaches have been proposed earlier. The image segmentation using normal standard methods is well only for simple image contents. In existing approaches image segmentation is done through multi-resolution stochastic level set method (MSLSM), but the topology changes are undesirable and it presented nonparametric topology-constrained segmentation model. To improve the image segmentation more effective, in this work, we plan to do image segmentation using level set method with geodesic active contour to analyze medical image disease diagnosis. With level set segmentation based on geodesic active contour, active cluster objects are segregated. Cluster object formation is done with fuzzification of growing active contours with the previously known contours of diseased image portions. With segment portions new similar regions can be traced out with automatic seeded growing method. Experimentation is conducted with bench mark data sets obtained from UCI repository and proved that the proposed GAC work will be 80% efficiency for image segmentation compared to an existing MSLSM. The parametric evaluations are carried over in terms of segment size and contour growth, Contour objects in the cluster, Similarity ratio of known and unknown contours, Seed size of the detected contours.

In this paper, we present an efficient content based image retrieval system that uses texture and color as visual features to describe the image and its segmented regions. Our contribution is of three directions. First, we use Gabor filters to extract texture features from the whole image or arbitrary shaped regions extracted from it after segmentation. Second, to speed up retrieval, the database images are segmented and the extracted regions are clustered according to their feature vectors using Self Organizing Map (SOM). This process is performed offline before query processing; therefore to answer a query, our system does not need to search the entire database images. Third, to further increase the retrieval accuracy of our system, we combine the region features with global features to obtain a more efficient system.
The experimental evaluation of the system is based on a 1000 COREL color image database. From experimentation, it is evident that our system performs significantly better and faster compared with other existing systems. We provide a comparison between retrieval results based on features extracted from the whole image, and features extracted from image regions. The results demonstrate that a combination of global and region based approaches gives better retrieval results for almost all semantic classes.

Image processing is a comprehensively research topic with an elongated history. Segmenting an image is the most challenging and difficult task in image processing and analysis. The principal intricacy met in image segmentation is the ability of techniques to discover semantic objects efficiently from an image without any prior knowledge. One recent work presented connected coherence tree algorithm (CCTA) for image segmentation (with no prior knowledge) which discovered regions of semantic coherence based on neighbor coherence segmentation criteria. It deployed an adaptive spatial scale and a suitable intensity-difference scale to extract several sets of coherent neighboring pixels and maximize the probability of single image content and minimize complex backgrounds. However CCTA segmented images either consists of small, lengthy and slender objects or rigorously ruined by noise, irregular lighting, occlusion, poor illumination, and shadow.
In this paper, we present a Cluster based Semantic Coherent Tree (CBSCT) scheme for image segmentation. CBSCT's initial work is on the semantic connected coherence criteria for the image segregation. Semantic coherent regions are clustered based on Bayesian nearest neighbor search of neighborhood pixels. The segmentation regions are extracted from the images based on the cluster object purity obtained through semantic coherent regions. The clustered image regions are post processed with non linear noise filters. Performance metrics used in the evaluation of CBSCT are semantic coherent pixel size, number of cluster objects, and purity levels of the cluster, segmented coherent region intensity threshold, and quality of segmented images in terms of image clarity with PSNR.

From the first use of the technics of intravascular ultrasound (IVUS) as an imaging technique for the coronary artery system at the 70th century until now , the segmentation of the arterial wall boundaries still an important problem . Much research has been done to give better segmentation result for better diagnostics , evaluation and therapy planning. In this paper we present a new segmentation technics based on Morphological Snakes which developed by Luis Álvarez used for the first time for IVUS segmentation. It is a simple , fast and stable approach of snakes evolution algorithm. Results are presented and discussed in order to demonstrate the effectiveness of this approach in IVUS segmentation.

The dynamics of rational maps and their properties are interesting because of the presence of poles and zeros. In this paper we have computed Julia sets of rational maps having Zhukovskii Function for which the double of the first derivative has no Herman rings. The data points out of the Julia set in Matlab workspace were imported to Matlab Signal Processing Tool for their analysis. We have sampled the data points with the sampling frequency of 8192 Hz and obtained complex signals. We have then applied the band pass filter to these complex signals. The effect of the band pass filter has generated complex analogue modulated signals.