7,403 research outputs found
Performance improvement of edge detection based on edge likelihood index
One of the problems of conventional edge detectors is the difficulty in distinguishing noise and true edges correctly using a simple measurement, such as gradient, local energy, or phase congruency. This paper proposes a performance improvement algorithm for edge detection based on a composite measurement called Edge Likelihood Index (ELI). In principle, given a raw edge map obtained from any edge detectors, edge contours can be extracted where gradient, continuity and smoothness of each contour are measured. The ELI of an edge contour is defined as directly proportional to its gradient and length, and inversely proportional to its smoothness, which offers a more flexible representation of true edges, such as those with low gradient, but continuous and smooth. The proposed method was tested on the South Florida data sets, using the Canny edge operator for edge detection, and evaluated using the Receiver Operator Characteristic curves. It can be shown that the proposed method reduces Bayes risk of ROC curves by over 10% in the aggregate test results.published_or_final_versio
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
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
Evaluating Rapid Application Development with Python for Heterogeneous Processor-based FPGAs
As modern FPGAs evolve to include more het- erogeneous processing elements,
such as ARM cores, it makes sense to consider these devices as processors first
and FPGA accelerators second. As such, the conventional FPGA develop- ment
environment must also adapt to support more software- like programming
functionality. While high-level synthesis tools can help reduce FPGA
development time, there still remains a large expertise gap in order to realize
highly performing implementations. At a system-level the skill set necessary to
integrate multiple custom IP hardware cores, interconnects, memory interfaces,
and now heterogeneous processing elements is complex. Rather than drive FPGA
development from the hardware up, we consider the impact of leveraging Python
to ac- celerate application development. Python offers highly optimized
libraries from an incredibly large developer community, yet is limited to the
performance of the hardware system. In this work we evaluate the impact of
using PYNQ, a Python development environment for application development on the
Xilinx Zynq devices, the performance implications, and bottlenecks associated
with it. We compare our results against existing C-based and hand-coded
implementations to better understand if Python can be the glue that binds
together software and hardware developers.Comment: To appear in 2017 IEEE 25th Annual International Symposium on
Field-Programmable Custom Computing Machines (FCCM'17
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
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