5,271 research outputs found
Binary Adaptive Semi-Global Matching Based on Image Edges
Image-based modeling and rendering is currently one of the most challenging topics in Computer Vision and Photogrammetry. The key issue here is building a set of dense correspondence points between two images, namely dense matching or stereo matching. Among all dense matching algorithms, Semi-Global Matching (SGM) is arguably one of the most promising algorithms for real-time stereo vision. Compared with global matching algorithms, SGM aggregates matching cost from several (eight or sixteen) directions rather than only the epipolar line using Dynamic Programming (DP). Thus, SGM eliminates the classical “streaking problem” and greatly improves its accuracy and efficiency. In this paper, we aim at further improvement of SGM accuracy without increasing the computational cost. We propose setting the penalty parameters adaptively according to image edges extracted by edge detectors. We have carried out experiments on the standard Middlebury stereo dataset and evaluated the performance of our modified method with the ground truth. The results have shown a noticeable accuracy improvement compared with the results using fixed penalty parameters while the runtime computational cost was not increased
GPU-based Image Analysis on Mobile Devices
With the rapid advances in mobile technology many mobile devices are capable
of capturing high quality images and video with their embedded camera. This
paper investigates techniques for real-time processing of the resulting images,
particularly on-device utilizing a graphical processing unit. Issues and
limitations of image processing on mobile devices are discussed, and the
performance of graphical processing units on a range of devices measured
through a programmable shader implementation of Canny edge detection.Comment: Proceedings of Image and Vision Computing New Zealand 201
Using the discrete hadamard transform to detect moving objects in surveillance video
In this paper we present an approach to object detection in surveillance video based on detecting moving edges
using the Hadamard transform. The proposed method is characterized by robustness to illumination changes
and ghosting effects and provides high speed detection, making it particularly suitable for surveillance applications.
In addition to presenting an approach to moving edge detection using the Hadamard transform, we
introduce two measures to track edge history, Pixel Bit Mask Difference (PBMD) and History Update Value
(H UV ) that help reduce the false detections commonly experienced by approaches based on moving edges.
Experimental results show that the proposed algorithm overcomes the traditional drawbacks of frame differencing
and outperforms existing edge-based approaches in terms of both detection results and computational
complexity
Searching for Signatures of Cosmic Superstrings in the CMB
Because cosmic superstrings generically form junctions and gauge theoretic
strings typically do not, junctions may provide a signature to distinguish
between cosmic superstrings and gauge theoretic cosmic strings. In cosmic
microwave background anisotropy maps, cosmic strings lead to distinctive line
discontinuities. String junctions lead to junctions in these line
discontinuities. In turn, edge detection algorithms such as the Canny algorithm
can be used to search for signatures of strings in anisotropy maps. We apply
the Canny algorithm to simulated maps which contain the effects of cosmic
strings with and without string junctions. The Canny algorithm produces edge
maps. To distinguish between edge maps from string simulations with and without
junctions, we examine the density distribution of edges and pixels crossed by
edges. We find that in string simulations without Gaussian noise (such as
produced by the dominant inflationary fluctuations) our analysis of the output
data from the Canny algorithm can clearly distinguish between simulations with
and without string junctions. In the presence of Gaussian noise at the level
expected from the current bounds on the contribution of cosmic strings to the
total power spectrum of density fluctuations, the distinction between models
with and without junctions is more difficult. However, by carefully analyzing
the data the models can still be differentiated.Comment: 15 page
Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform
Low back pain is a very common problem in the industrialised countries and its associated cost is enormous. Diagnosis of the underlying causes can be extremely difficult. Many studies have focused on mechanical disorders of the spine. Digital videofluoroscopy (DVF) was widely used to obtain images for motion studies. This can provide motion sequences of the lumbar spine, but the images obtained often suffer due to noise, exacerbated by the very low radiation dosage. Thus determining vertebrae position within the image sequence presents a considerable challenge. In this paper, we show how our new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm as without it the extraction of moving multiple vertebrae is computationally daunting. Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders
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