Depth Map Upsampler Using Common Edge Detection

2015 ◽  
pp. 492-499
Author(s):  
Soo-Yeon Shin ◽  
Jae-Won Suh
Keyword(s):  
Edge Detection ◽  
Depth Map ◽  
Common Edge

No-Reference Blur Assessment Based on Entropy and PCNN for Restoration Images

2013 ◽  
Vol 303-306 ◽  
pp. 1599-1604
Author(s):  
Jin Ping He ◽  
Jian Sheng Chen ◽  
Guang Da Su
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Objective Measure ◽  
Experimental Results ◽  
Common Edge ◽  
Transition Zones ◽  
Detection Algorithms ◽  
Number Ratio ◽  
Image Blurring ◽  
Blurring Effect

In practical problems, there are usually no clear counterparts as reference to evaluate restoration results. So no-reference blur assessment is very important and necessary. In this paper, we proposed an objective measure named as Edge Factor (EF) to appraise image blurring. The fundamental rationale was that blurring effect was much more perceptible in edge transition zones. The pixel number of edge transition zones would decrease when blurring occured. We defined the pixel number ratio of the edge transition zones to the whole image as EF. Experimental results show the monotonic consistency of EF and RMS. The proposed method is further compared with some common edge detection algorithms to demonstrate the effectiveness of combining point-based entropy with Pulse Coupled Neural Network.

An improved edge detection algorithm for depth map inpainting

2014 ◽  
Vol 55 ◽  
pp. 69-77 ◽  
Author(s):  
Weihai Chen ◽  
Haosong Yue ◽  
Jianhua Wang ◽  
Xingming Wu
Keyword(s):  
Edge Detection ◽  
Depth Map ◽  
Detection Algorithm ◽  
Edge Detection Algorithm

scholarly journals A COMPARATIVE STUDY OF COMMON EDGE DETECTION OPERATORS IN DIGITAL IMAGE PROCESSING

2021 ◽  
Vol 6 (4) ◽  
pp. 147-152
Author(s):  
Benjamin Kommey ◽  
John Kwame Dunyo ◽  
Eric Tutu Tchao ◽  
Andrew Selasi Agbemenu
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Comparative Study ◽  
Digital Image Processing ◽  
Digital Image ◽  
Common Edge

Using Wavelet Transformation and Edge Detection to Generate a Depth Map from a Single Image

Smart Science ◽  
2017 ◽  
Vol 5 (2) ◽  
pp. 75-84 ◽  
Author(s):  
Der-Feng Huang ◽  
Yu-Hsiang Chen ◽  
Ting-Wen Huang
Keyword(s):  
Edge Detection ◽  
Wavelet Transformation ◽  
Depth Map ◽  
Single Image

scholarly journals A Simple Outdoor Environment Obstacle Detection Method Based on Information Fusion of Depth and Infrared

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yaguang Zhu ◽  
Baomin Yi ◽  
Tong Guo
Keyword(s):  
Edge Detection ◽  
Information Fusion ◽  
Recognition Rate ◽  
Infrared Image ◽  
Depth Map ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Intelligent Vehicles ◽  
Outdoor Environment ◽  
Depth Information

In allusion to the existing low recognition rate and robustness problem in obstacle detection; a simple but effective obstacle detection algorithm of information fusion in the depth and infrared is put forward. The scenario is segmented by the mean-shift algorithm and the pixel gradient of foreground is calculated. After pretreatment of edge detection and morphological operation, the depth information and infrared information are fused. The characteristics of depth map and infrared image in edge detection are used for the raised method, the false rate of detection is reduced, and detection precision is improved. Since the depth map and infrared image are not affected by natural sunlight, the influence on obstacle recognition due to the factors such as light intensity and shadow is effectively reduced and the robustness of the algorithm is also improved. Experiments indicate that the detection algorithm of information fusion can accurately identify the small obstacle in the view and the accuracy of obstacle recognition will not be affected by light. Hence, this method has great significance for mobile robot or intelligent vehicles on obstacle detection in outdoor environment.

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