Distance transform

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

A Transportation $$L^p$$ Distance for Signal Analysis

Abstract: Transport-based distances, such as the Wasserstein distance and earth mover’s distance, have been shown to be an effective tool in signal and image analysis. The success of transport-based distances is in part due to their Lagrangian nature which allows it to capture the important variations in many signal classes. However, these distances require the signal to be non-negative and normalised. Furthermore, the signals are considered as measures and compared by redistributing (transporting) them, which does not directly take into account the signal intensity. Here, we study a transport-based distance, called the $$TL^p$$ distance, that combines Lagrangian and intensity modelling and is directly applicable to general, non-positive and multichannelled signals. The distance can be computed by existing numerical methods. We give an overview of the basic properties of this distance and applications to classification, with multichannelled non-positive one-dimensional signals and two-dimensional images, and colour transfer.

Image recongition system

Abstract: Methods, devices and systems for recognizing an object in an image are provided, in which the object is recognized by evaluation of both image data and digital map information that corresponds to an area represented by the image. Evaluation of the image data and the digital map information may involve various methods of evaluation including cross-checking, in which the digital map information is utilized to verify correct object recognition in the image data; prediction, in which digital map information is utilized to predict a feature of an object to facilitate object recognition in the image data; or modeling in which a generic model of an object is compared with the image data.

A distance-field based automatic neuron tracing method

Abstract: Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex. We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based “force” fields are used: one for “pressure”, which is the distance transform field of foreground pixels (voxels) to the background, and another for “thrust”, which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can “push” a “rolling ball” quickly along the skeleton of a neuron, reconstructing the 3D cell morphology. We have used DF-Tracing to reconstruct the intricate neuron structures found in noisy image stacks, obtained with 3D laser microscopy, of dragonfly thoracic ganglia. Compared to several previous methods, DF-Tracing produces better reconstructions.

Method and system for detecting a geometrically transformed copy of an image

Abstract: A method for detecting a geometrically transformed copy of content in at least a portion of an image, comprises the steps of: (a) providing first and second digital images; (b) searching for objects of interest within each digital image; (c) identifying pairs of corresponding objects of interest in the digital images, wherein each pair of corresponding objects of interest comprises a located object of interest in the first digital image and a corresponding located object of interest in the second digital image that corresponds to the located object of interest in the first image; (d) locating feature points on each located object of interest in each digital image; (e) matching feature points on the located object of interest in the first digital image to the feature points on the corresponding object of interest in the second digital image, thereby generating a set of correspondence points for each image; (f) determining parameters of a geometric transformation that maps the set of correspondence points in the first digital image into the set of correspondence points in the second digital image; (g) transforming the first digital image according to the parameters of the geometric transformation determined in step (f); and (h) detecting regions of similarity between the content of the transformed first digital image and the second digital image, thereby determining if the second image contains a region that is a geometrically transformed copy of a region in the first image.

Precise Hausdorff distance computation for planar freeform curves using biarcs and depth buffer

Abstract: We present a real-time algorithm for computing the precise Hausdorff Distance (HD) between two planar freeform curves. The algorithm is based on an effective technique that approximates each curve with a sequence of G 1 biarcs within an arbitrary error bound. The distance map for the union of arcs is then given as the lower envelope of trimmed truncated circular cones, which can be rendered efficiently to the graphics hardware depth buffer. By sampling the distance map along the other curve, we can estimate a lower bound for the HD and eliminate many redundant curve segments using the lower bound. For the remaining curve segments, we read the distance map and detect the pixel(s) with the maximum distance. Checking a small neighborhood of the maximum-distance pixel, we can reduce the computation to considerably smaller subproblems, where we employ a multivariate equation solver for an accurate solution to the original problem. We demonstrate the effectiveness of the proposed approach using several experimental results.