Distance transform

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

Method and apparatus for image registration

Abstract: An apparatus and method for image registration involves computing (350) a first transform based on landmark manifold points (102, 104, 114), using a distance measure, computing (370) a second transform from the distance measure and the first transform. Registering the images is accomplished by applying the second transform.

Moving object detection apparatus and method

Abstract: Disclosed is directed to a moving object detection apparatus and method. The apparatus comprises an image capture module, an image alignment module, a temporal differencing module, a distance transform module, and a background subtraction module. The image capture module derives a plurality of images in a time series. The image alignment module aligns the images if the image capture module is situated on a movable platform. The temporal differencing module performs temporal differencing on the captured images or the aligned images, and generates a difference image. The distance transform module transforms the difference image into a distance map. The background subtraction module applies the distance map to background subtraction technology and compares the results with the current captured image, so as to obtain the information for moving objects.

Comparing images using the Hausdorff distance under translation

Abstract: Efficient algorithms are provided for computing the Hausdorff distance between a binary image and all possible relative positions (translations) of a model, or a portion of that model. The computation is in many ways similar to binary correlation. However, it is more tolerant of perturbations in the locations of points because it measures proximity rather than exact superposition. >

Distance measuring method and a distance measuring apparatus

Abstract: Method and apparatus for obtaining distance data from an object to a lens by obtaining an image of the object by an image input unit for forming an image of the object through the lens on an image receiving plane. Two observed images Is and Is' with different blurs from the image input unit are obtained by making a position of the lens and/or a position of the image receiving plane with regard to the object different by a minute distance Δz. A filtering process is then performed by applying a filter to the two input observed images Is and Is', thereby providing two output images Fs and Fs'. The distance a up to the object is calculated by using the two output images Fs and Fs' obtained by the filtering process based on the relation between the two output images Fs and Fs' and radius s of the blur and the distance a from the lens to the object.

MetaMorphs: Deformable shape and texture models

Abstract: We present a new class of deformable models, MetaMorphs, that consist of both shape and interior texture. The model deformations are derived from both boundary and region information in a common variational framework. This framework represents a generalization of previous model-based segmentation approaches. The shapes of the new models are represented implicitly as "images" in the higher dimensional space of distance transforms. The interior textures are captured using a nonparametric kernel-based approximation of the intensity probability density functions (p.d.f.s) inside the models. The deformations that MetaMorph models can undergo are defined using a space warping technique - the cubic B-spline based Free Form Deformations (FFD). When using the models for boundary finding in images, we derive the model dynamics from an energy functional consisting of both edge energy terms and intensity/texture energy terms. This way, the models deform wider the influence of forces derived from both boundary and regional information. The proposed MetaMorph deformable models are efficient in convergence, have large attraction range, and are robust to image noise and inhomogeities. Various examples on finding object boundaries in noisy images with complex textures demonstrate the potential of the proposed technique.