Distance transform

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

Scalable Parallel Distance Field Construction for Large-Scale Applications

Abstract: Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. A new distributed spatial data structure, named parallel distance tree , is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.

A Fast CUDA-Based Implementation for the Euclidean Distance Transform

Abstract: In Image Processing efficient algorithms are always pursued for applications that use the most advanced hardware architectures. Distance Transform is a classic operation for blurring effects, skeletonizing, segmentation and various other purposes. This article presents two implementations of the Euclidean Distance Transform using CUDA (Compute Unified Device Architecture) in GPU (Graphics Process Unit): of the Meijster's Sequential Algorithm and another is a very efficient algorithm of simple structure. Both using only shared memory. The results presented herein used images of various types and sizes to show a faster run time compared with the best-known implementations in CPU.

Computer recognition of hand-written characters using the distance transform

Abstract: A new statistical classifier for hand-written character recognition is presented. The system features a preprocessing phase for image normalisation and a distance transform applied to the normalised image, which converts a B/W picture into a grey scale image. A k-nearest-neighbour classifier follows, based on the distance transform and a suitable metric. The system has an accuracy of 98.96% when applied to the US Post Office zip code database, at 0.98% error rate.

Watershed-based Real-time Image Processing for Multi-Potholes Detection on Asphalt Road

Abstract: This paper presents a real-time watershed-based algorithm for detecting multiple potholes on asphalt road surface. The algorithm uses (i) inverted binary in combination with Otsu thresholding techniques to find the optimal threshold value of the image in an inverted color space; then (ii) morphological technique with open, then close kernels to filter small noises and bold pothole edges on the image; and (iii) distance transform for finding markers on the pre-watershed-phase image before applying the watershed algorithm. As a result, the algorithm achieves real-time processing speed of approximately 33.1 frame-per-second (fps). Based on the tested images, it is evident that the algorithm can be used for detecting effectively potholes with different sizes and structures on three types of road surfaces namely smooth, aged, and degraded ones.

CUDA Acceleration of 3D Dynamic Scene Reconstruction and 3D Motion Estimation for Motion Capture

Abstract: Tracking of 3D human body movement from multiple camera video streams is an important problem in the domain of computer vision. In this paper we perform body pose tracking in 3D space using 3D data reconstructed at every frame. We present an efficient GPU-based method for 3D reconstruction of the real world dynamic scenes. Besides volumetric reconstruction, we propose to compute view-independent 3D optical flow (i.e., scene flow) in combination with volumetric reconstruction, and have attained efficient scene flow estimation using GPU acceleration. Body pose estimation starts from a deterministic prediction based on scene flow, and then uses a multi-layer search algorithm involving stochastic search and local optimization. We design and parallelize the PSO-based (particle swarm optimization) stochastic search algorithm and 3D DT (distance transform) computation of the pose estimation method on GPU. To the end, our system can reach efficient and robust body pose tracking.