Depth Recovery from Motion and Defocus Blur

2006 ◽  
pp. 122-133 ◽  
Author(s):  
Huei-Yung Lin ◽  
Chia-Hong Chang
Keyword(s):  
Depth Recovery ◽  
Defocus Blur

scholarly journals Blur-readable two-dimensional barcode based on blur-invariant shape and geometric features

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199958
Author(s):  
Shundao Xie ◽  
Hong-Zhou Tan
Keyword(s):  
Circular Disk ◽  
Geometric Features ◽  
Two Dimensional ◽  
Common Solution ◽  
Ringing Artifacts ◽  
2D Barcode ◽  
Defocus Blur ◽  
Blurred Image ◽  
Decoding Accuracy ◽  
Invariant Shape

In recent years, the application of two-dimensional (2D) barcode is more and more extensive and has been used as landmarks for robots to detect and peruse the information. However, it is hard to obtain a sharp 2D barcode image because of the moving robot, and the common solution is to deblur the blurry image before decoding the barcode. Image deblurring is an ill-posed problem, where ringing artifacts are commonly presented in the deblurred image, which causes the increase of decoding time and the limited improvement of decoding accuracy. In this article, a novel approach is proposed using blur-invariant shape and geometric features to make a blur-readable (BR) 2D barcode, which can be directly decoded even when seriously blurred. The finder patterns of BR code consist of two concentric rings and five disjoint disks, whose centroids form two triangles. The outer edges of the concentric rings can be regarded as blur-invariant shapes, which enable BR code to be quickly located even in a blurred image. The inner angles of the triangle are of blur-invariant geometric features, which can be used to store the format information of BR code. When suffering from severe defocus blur, the BR code can not only reduce the decoding time by skipping the deblurring process but also improve the decoding accuracy. With the defocus blur described by circular disk point-spread function, simulation results verify the performance of blur-invariant shape and the performance of BR code under blurred image situation.

Defocus Blur Detection via Boosting Diversity of Deep Ensemble Networks

2021 ◽  
pp. 1-1
Author(s):  
Wenda Zhao ◽  
Xueqing Hou ◽  
You He ◽  
Huchuan Lu
Keyword(s):  
Blur Detection ◽  
Defocus Blur

Telemetry-Based Depth Recovery for Virtual Factory Construction and Extension to Remote Facility Management

1999 ◽  
Author(s):  
Dan Zetu ◽  
Pat Banerjee ◽  
Ali Akgunduz
Keyword(s):  
3D Models ◽  
Human Motion ◽  
Facility Management ◽  
Virtual Manufacturing ◽  
Shop Floor ◽  
Virtual Factory ◽  
Depth Recovery ◽  
Camera Location ◽  
Virtual Manufacturing Environment ◽  
2D Images

Abstract The fast construction of a Virtual Factory model without using a CAD package can be made possible by using computer vision techniques. In order to create a realistic Virtual Manufacturing environment, especially when such a model has to be created in correlation to an existing facility, a reliable algorithm that extracts 3D models from camera images is needed, and this requires exact knowledge of the camera location when capturing images. In this paper, we describe an approach for depth recovery from 2D images based on tracking a camera within the environment. We also explore the extension of our telemetry-based algorithm to remote facility management, by tracking and synchronizing human motion on the shop floor with motion of an avatar in a Virtual Environment representing the same shop floor.

Defocus blur detection using novel local directional mean patterns (LDMP) and segmentation via KNN matting

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Awais Khan ◽  
Aun Irtaza ◽  
Ali Javed ◽  
Tahira Nazir ◽  
Hafiz Malik ◽  
...  
Keyword(s):  
Blur Detection ◽  
Defocus Blur
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