Showing papers on "Scale-invariant feature transform published in 2001"
TL;DR: An artificial retina camera (ARC) is employed for real-time preprocessing of images and the algorithm of Hough transform is advanced for detecting the biology-images with approximate circle edge-information in the two-dimension space.
Abstract: An artificial retina camera (ARC) is employed for real-time preprocessing of images. And the algorithm of Hough transform is advanced for detecting the biology-images with approximate circle edge-information in the two-dimension space. This method also works in parallel for processing multiple input and partial input patterns.
8 citations
Proceedings Article•
21 Nov 2001
TL;DR: A new approach for the coarse segmentation of tubular structures in 3D image data is presented, based on an extension of the randomized Hough transform (RHT), a robust method for low-dimensional parametric object detection.
7 citations
TL;DR: This paper considers two types of constraints: constraints defined by considering invariant features and constraints defined via gradient direction information, which can significantly improve the gathering strategy, leading to identification of the correct parameters.
6 citations
TL;DR: Generally, the proposed algorithm supports the same level of accuracy as standard Hough transform while featuring acceleration (and computing cost reduction); moreover, further performance boost can be achieved through parallel processing.
Abstract: In this paper, a high-speed line detection method using Hough transform is proposed. This approach is inspired by features of human foveal vision. In particular, line segment candidates are extracted at sufficient level of accuracy by applying Hough transform inside a local window. After that, greater areas are generated by extension of line segment candidates, and segment verification is executed. Such local window is then moved sequentially until the entire image is covered. Such processing makes possible absorption of quantization errors of Hough transform as well as extraction of shorter segments that were hard to detect by means of standard Hough transform. Generally, the proposed algorithm supports the same level of accuracy as standard Hough transform while featuring acceleration (and computing cost reduction); moreover, further performance boost can be achieved through parallel processing. © 2001 Scripta Technica, Syst Comp Jpn, 32(10): 22–30, 2001
5 citations
Journal Article•
TL;DR: In this article, a track reconstruction algorithm based on the Hough transform was proposed for scintillating fiber detectors, which achieved an efficiency of 86% on a 16-node parallel machine.
Abstract: The reconstruction of tracks left by particles in a scintillating fiber detector from a high energy experiment is discussed. The track reconstruction algorithm is based on the Hough transform and achieves an efficiency above 86%. The algorithm is implemented in a 16-node parallel machine using two parallelism approaches in order to speed up the application of the Hough transform, which is known to have large computational cost.
1 citations