scholarly journals FARM: Functional Automatic Registration Method for 3D Human Bodies

2019 ◽  
Vol 39 (1) ◽  
pp. 160-173 ◽  
Author(s):  
R. Marin ◽  
S. Melzi ◽  
E. Rodolà ◽  
U. Castellani
Keyword(s):  
Automatic Registration ◽  
Registration Method

An automatic registration method for pre- and post-interventional CT images for assessing treatment success in liver RFA treatment

2015 ◽  
Vol 42 (9) ◽  
pp. 5559-5567 ◽  
Author(s):  
Ha Manh Luu ◽  
Wiro Niessen ◽  
Theo van Walsum ◽  
Camiel Klink ◽  
Adriaan Moelker
Keyword(s):  
Treatment Success ◽  
Ct Images ◽  
Automatic Registration ◽  
Registration Method ◽  
Interventional Ct

High accuracy-automatic registration method research on multi-source remote sensing image

2016 ◽  
Vol 31 (6) ◽  
pp. 604-612
Author(s):  
程国华 CHENG Guo-hua ◽  
王阿川 WANG a-chuan ◽  
陈舒畅 CHEN Shu-chang ◽  
赵 宇 ZHAO Yu ◽  
范晓锐 FAN Xiao-rui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
High Accuracy ◽  
Automatic Registration ◽  
Registration Method

A robust automatic registration method for hand-held structured light 3D scanner

2014 ◽  
Author(s):  
Guomin Zhan ◽  
Mengqi Wu ◽  
Kai Zhong ◽  
Zhongwei Li ◽  
Yusheng Shi
Keyword(s):  
Structured Light ◽  
Automatic Registration ◽  
3D Scanner ◽  
Registration Method

scholarly journals UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features

2021 ◽  
Vol 13 (18) ◽  
pp. 3605
Author(s):  
Xin Luo ◽  
Guangling Lai ◽  
Xiao Wang ◽  
Yuwei Jin ◽  
Xixu He ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Rapid Development ◽  
Image Features ◽  
Automatic Registration ◽  
Remote Sensing Images ◽  
Registration Accuracy ◽  
Registration Method ◽  
Uav Images ◽  
High Level

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.

scholarly journals Exposure Bracketing Techniques for Camera Document Image Enhancement

2019 ◽  
Vol 9 (21) ◽  
pp. 4529
Author(s):  
Tao Liu ◽  
Hao Liu ◽  
Yingying Wu ◽  
Bo Yin ◽  
Zhiqiang Wei
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Image ◽  
Automatic Registration ◽  
Digital Cameras ◽  
Document Images ◽  
Registration Method ◽  
Lighting Conditions ◽  
High Quality Image ◽  
Multiple Document

Capturing document images using digital cameras in uneven lighting conditions is challenging, leading to poorly captured images, which hinders the processing that follows, such as Optical Character Recognition (OCR). In this paper, we propose the use of exposure bracketing techniques to solve this problem. Instead of capturing one image, we used several images that were captured with different exposure settings and used the exposure bracketing technique to generate a high-quality image that incorporates useful information from each image. We found that this technique can enhance image quality and provides an effective way of improving OCR accuracy. Our contributions in this paper are two-fold: (1) a preprocessing chain that uses exposure bracketing techniques for document images is discussed, and an automatic registration method is proposed to find the geometric disparity between multiple document images, which lays the foundation for exposure bracketing; (2) several representative exposure bracketing algorithms are incorporated in the processing chain and their performances are evaluated and compared.

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