Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Clutter Noise Removal in Binary Document Images

Abstract: The paper presents a clutter detection and removal algorithm for complex document images. The distance transform based approach is independent of clutter's position, size, shape and connectivity with text. Features are based on a residual image obtained by analysis of the distance transform and clutter elements, if present, are identified with an SVM classifier. Removal is restrictive, so text attached to the clutter is not deleted in the process. The method was tested on a collection of degraded and noisy, machine-printed and handwritten Arabic and English text documents. Results show pixel-level accuracies of 97.5% and 95% for clutter detection and removal respectively. This approach was also extended with a noise detection and removal model for documents having a mix of clutter and salt-n-pepper noise.

Hierarchical control point editing of adaptively sampled distance fields

Abstract: A method edits a graphics object by first representing the graphics object by an adaptively sampled distance field A portion of the adaptively sampled distance field is selected for editing and converted to a triangle model The triangle model is then deformed, the adaptively sampled distance field is regenerated from the deformed triangle model

Real Time Moving Object Tracking by Particle Filter

Abstract: Robust and real time moving object tracking is a tricky job in computer vision problems. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this paper, we first try to develop a color based particle filter. In this approach, the object tracking system relies on the deterministic search of window, whose color content matches a reference histogram model. A simple HSV histogram-based color model is used to develop our observation system. Secondly and finally, we describe a new approach for moving object tracking with particle filter by shape information. The shape similarity between a template and estimated regions in the video scene is measured by their normalized cross-correlation of distance transformed image. Our observation system of particle filter is based on shape from distance transformed edge features. Template is created instantly by selecting any object from the video scene by a rectangle. Experimental results have been presented to show the effectiveness of our proposed system.

Method for recognizing an object in an image and image recognition device

Abstract: A method for recognizing an object in an image and a corresponding image recognition device are provided, in which the object is recognized based on both image data and digital map information that corresponds to an area represented by the image. According to one embodiment, digital map information is evaluated (81) to predict an object position on the image (82), based on which a sub-portion of image data is selected (83). Subsequently, only this sub-portion of the image data has to be analyzed (84) in order to recognize the object (85).

Method for adaptive image region partition and morphologic processing

Abstract: An adaptive dilation method receives an image and performs an adaptive background distance transform to create an adaptive background distance transform image. A threshold is applied to the adaptive background distance transform image to generate adaptive dilation image output. An adaptive erosion method receives an image and performs an adaptive foreground distance transform to create an adaptive foreground distance transform image. A threshold is applied to the adaptive foreground distance transform image to generate adaptive erosion image output.