Distance transform

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

Moving object detection device, moving object detection method, and moving object detection program

Abstract: A moving object detection device comprising: an object distance setting part, determining the distance to a moving object that moves the most based on a distance image, in which information on distances to image-taken objects are embedded, and a difference image, in which the movements of moving objects are embedded as movement information; an object distance image generating part, generating an object distance image corresponding to the abovementioned distance; and a contour extraction part, extracting a contour inside the object distance image to detect a moving object.

3D fingertip and palm tracking in depth image sequences

Abstract: We present a vision-based approach for robust 3D fingertip and palm tracking on depth images using a single Kinect sensor. First the hand is segmented in the depth images by applying depth and morphological constraints. The palm is located by performing distance transform to the hand contour and tracked with a Kalman filter. The fingertips are detected by combining three depth-based features and tracked with a particle filter over successive frames. Quantitative results on synthetic depth sequences show the proposed scheme can track the fingertips quite accurately. Besides, its capabilities are further demonstrated through a real-life human-computer interaction application.

Metamorphs: Deformable Shape and Appearance Models

Abstract: This paper presents a new deformable modeling strategy that is aimed at integrating shape and appearance in a unified space. If we think of traditional deformable models as "active contours" or "evolving curve fronts," the new deformable shape and appearance models that we propose are "deforming disks or volumes." Each model not only has boundary shape but also interior appearance. The model shape is implicitly embedded in a higher dimensional space of distance transforms and is thus represented by a distance map "image." This way, both the shape and the appearance of the model are defined in the pixel space. A common deformation scheme, that is, the free-form deformations (FFDs), parameterizes warping deformations of the volumetric space in which the model is embedded, hence simultaneously deforming both model boundary and interior. When applied to segmentation, a metamorphs model can be initialized by covering a seed region far from the object boundary, and then the model efficiently evolves and converges to an optimal solution. The model dynamics are derived in a unified variational framework that consists of edge-based and region-based energy terms, both of which are differentiable with respect to the common set of FFD parameters. As the model deforms, its interior appearance statistics are adaptively learned and, then, toward the next-step deformation, the model examines not only edge information but also its exterior region statistics to ensure that it only expands to new territory with consistent appearance statistics. The Metamorphs formulation also allows natural merging and competition of multiple models. We demonstrate the robustness of metamorphs by using both natural and medical images that have high noise levels, intensity inhomogeneity, and complex texture.

Method for point-of-interest attraction in digital images

Abstract: A method for point-of-interest attraction towards an object pixel in a digital image by first performing object segmentation resulting in a contour-based or a region-based representation of object pixels and background pixels of the image. Secondly a vector distance transform image is computed comprising a vector displacement of each background pixel towards the nearest of said object pixels and the nearest object pixel for a given background pixel is determined by adding the vector displacement to said background pixel. Finally the point-of-interest is attracted towards the determined nearest object pixel.