Recently, according to the advanced hardware technology, the price of sensors in HMD (Head Mounted Display) is getting cheaper and calculation speed in smartphone is fast enough to express a plausible virtual reality. Users wear the HMD device on their head and it shows the virtual scenes in front of users. Users can enjoy a wide display without any restrictions in space. Based on this device, ...

Models for visualization are important, helping the developer and user to understand the visualization process; to follow the connections and the data paths through the system; and to reference and compare the functionality and the limitations of different systems or techniques. Display models specifically classify the data by what type of output can be created. Jacques Bertin [2] described a s...

Deformable shape models require correspondence across the training population in order to generate a statistical model for use as a future geometric prior. Traditional methods use fixed sampling and assume correspondence, or attempt to induce correspondence by minimizing variance. In this paper, we define a training methodology for sampled medial deformable shape models (m-reps) which generates...

We present a novel evolutionary computing based approach to medical image segmentation. Our method complements the image-pixel integration power of deformable shape models with the high-level control mechanisms of genetic algorithms (GA). Specifically, GA alleviate typical deformable model weaknesses pertaining to model initialization, deformation parameter selection, and energy functional loca...

We present a novel evolutionary computing based approach to medical image segmentation. Our method complements the image-pixel integration power of deformable shape models with the high-level control mechanisms of genetic algorithms (GA). Specifically, the GAs alleviate typical deformable model weaknesses pertaining to model initialization, deformation parameter selection, and energy functional...

Segmentation of medical images is an important step in various applications such as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require add...

Statistical models of deformations (SMD) capture the variability of deformations from the template image onto a group of sample images and can be used to constrain the traditional deformable registration algorithms to improve their robustness and accuracy. This paper employs a wavelet-PCA-based SMD to constrain the traditional deformable registration based on the Bayesian framework. The templat...

Deformable models like snakes are a classical tool for image segmentation. Highly deformable models extend them with the ability to handle dynamic topological changes, and therefore to extract arbitrary complex shapes. However, the resolution of these models largely depends on the resolution of the image. As a consequence, their time and memory complexity increases at least as fast as the size ...

Deformable Models, which includes the popular snake models (Kass et al., 1988) and deformable surfaces (McInerney & Terzopoulos, 1996; Suri & Editors, 2006), are well known techniques for boundary extraction and tracking in 2D/3D images. Basically, these models can be classified into three categories: parametric, geodesic snakes and implicit models. The relationships between these models have b...

Recently, some deformable models have been proposed for character recognition, due to their ability to capture variations in handwriting. These proposed systems use deformable models to represent characters and to extract features, and subsequently feed the extracted information into a classiier. They often treat the three components { modeling, feature extraction, and clas-siication { as three...