We consider the segmentation of a trajectory into piecewise polynomial parts, or possibly other forms. Segmentation is typically formulated as an optimization problem which trades off model fitting error versus the cost of introducing new segments. Heuristics such as split-and-merge are used to find the best segmentation. We show that for ordered data (eg., single curves or trajectories) the gl...

We develop an object detection method combining top-down recognition with bottom-up image segmentation. There are two main steps in this method: a hypothesis generation step and a verification step. In the top-down hypothesis generation step, we design an improved Shape Context feature, which is more robust to object deformation and background clutter. The improved Shape Context is used to gene...

sar (synthetic aperture radar) image enhancement and segmentation is purpose of this thesis. sar image segmentation is a primary step before steps such as classification and target recognition. the main obstacle in sar image segmentation is inherent speckle noise. speckle noise is a multiplicative and highly destructive noise which results to intensity inhomogeneity. hence common segmentation m...

Recognition of geometric primitives such as line and arc helps in automatic conversion of line drawing document images into electronic form. A large number of raster to vector methods can be found in the literature. A line and arc segmentation contest was held in conjunction with the tenth IAPR International Workshop on Graphics Recognition (GREC 2013) for comparing performance of different met...

We introduce a very general method of achieving stable and fast color segmentation. This method works on diierent hierarchical data structures and combines local bottom-up region growing segmentation with top-down separation techniques. As our method supports exploitation of inherent parallelism, a real-time object detection has been designed and is in the implementation phase.

An automatic multi-organ segmentation pipeline is presented. The segmentation starts with stripping the body of skin and subcutaneous fat using threshold-based level-set methods. After registering the image to be processed against a standard subject picked from the training datasets, a series of model-based level set segmentation operations is carried out guided by hierarchical shape priors. Th...

We review recent biological vision studies that are related to human motion segmentation. Our goal is to develop a practically plausible computational framework that is guided by recent cognitive and psychological studies on the human visual system for the segmentation of human body in a video sequence. Specifically, we discuss the roles and interactions of bottom-up and top-down processes in v...

We describe a new approach for learning to perform classbased segmentation using only unsegmented training examples. As in previous methods, we first use training images to extract fragments that contain common object parts. We then show how these parts can be segmented into their figure and ground regions in an automatic learning process. This is in contrast with previous approaches, which req...

Nowadays, the accumulation of paper in the life of business professional is overwhelming. Digital documents on the other hand being less expensive and more efficient are on the road to a more organized office. Also crucial for many business applications document image analysis is needed before OCR operation. Segmentation of document images into text and non-text regions is essential because OCR...