Ameliorating the Saliency of Objects in digital Images

Visual saliency is the capability of a revelation system of human or machine to choose a sub part of information for further processing. The mechanism describes here serves as a filter to pick only the interesting information related to given errands and the tasks while ignoring inappropriate information. This work uses Random Forest to know the similarity between the image patches, apply active contour model to get the approximate contour and do dynamic thresholding segmentation .The results we get consists of many small artifacts, so to remove the low level details and to obtain the more smoothness we apply gradient minimization technique

Saliency Driven Vasculature Segmentation with Infinite Perimeter Active Contour Model

Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite active contour model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our model outperforms its competitors

Active Vision for Scene Understanding

Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot\u27s view in order to explore interaction possibilities of the scene