Modeling 3D human body is an advanced technique used in human motion analysis and garment industry. In this paper, we propose a method for forming deformation functions so that we can rebuild the 3D human body given anthropometric measurements. The advanced idea in our approach is that we split the 3D body into small parts. In that way, we can specialize different set of parameters needed to interpolate for each section. With an interpolation approach, we build a 3D human body for 593 female bodies with the corresponding body shape but require fewer input measurements than 3D laser scans.

In object segmentation field, while the non-predefined object segmentation distinguishes arbitrary self-assumed object from background, predefined object segmentation pre-specifies object evidently. This paper presents a new method to segment predefined objects by globally optimizing an orientation-based objective function that measures the fitness of object boundary in a discretized parameter space. A specific object is explicitly described by normalized discrete sets of boundary points and corresponding normal vectors with respect to its plane shapes in a certain aspect. The orientation factor provides robust distinctness for target objects. By considering the order relation of transformation elements, and their dependency on derived oversegmentation outcome, the domain of translations and scales is discretized efficiently. The appropriate transformation parameters of a shape model corresponding to a target object in an image are determined using the global optimization algorithm branch-bound. Discrete boundary points of the consequent transformed model are chained together to produce the final contour of the target object. The results tested on PASCAL dataset show a considerable achievement in solving complex background and unclear boundary images.

Abstract-Automatic segmentation of foreground text from the background in degraded document images is very much essential for the smooth reading of the document content and recognition tasks by machine. In this paper, we present a novel approach to the binarization of degraded document images. The proposed method uses a new local contrast feature extracted based on the stroke width of text. First, a pre-processing method is carried out for noise removal. Text boundary detection is then performed on the image constructed from the contrast feature. Then local estimation follows to extract text from the background. Finally, a refinement procedure is applied to the binarized image as a post-processing step to improve the quality of the final results. Experiments and comparisons of extracting text from degraded handwriting and machine-printed document image against some well-known binarization algorithms demonstrate the effectiveness of the proposed method.