Results 281 to 290 of about 178,695 (309)

FPGA implementation of scale invariant feature transform

2016 International Conference on Microelectronics, Computing and Communications (MicroCom), 2016
Scale Invariant Feature Transform is a competent algorithm for extracting unique features from images. The fact that features extracted are invariant to image scaling, translation, rotation and partially invariant to illumination changes makes it attractive in many computer vision applications involving mobile robots such as obstacle recognition ...
S S Rekha, Y J Pavitra, Prabhakar Mishra
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Scale-Invariant Feature Transform (SIFT)

2016
Many real applications require the localization of reference positions in one or more images, for example, for image alignment, removing distortions, object tracking, 3D reconstruction, etc. We have seen that corner points1 can be located quite reliably and independent of orientation.
Wilhelm Burger, Mark J. Burge
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Modified Scale Invariant Feature Transform in omnidirectional images

2009 International Conference on Mechatronics and Automation, 2009
The Scale Invariant Feature Transform, SIFT, is invariant to image translation, scaling, rotation, and is partially invariant to illumination changes. But, the time of features extraction and matching is huge, and the number of features is much larger then that is needed.
null Yuquan Wang, null Guihua Xia, null Qidan Zhu, null Tong Wang   +3 more
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Visual orientation inhomogeneity based scale-invariant feature transform

Expert Systems with Applications, 2015
Provide the evidence of existence of the least important visual orientation.Novel algorithm with high efficiency is proposed to detect and describe local feature.Better performance for detection and matching, comparable performance for recognition. Scale-invariant feature transform (SIFT) is an algorithm to detect and describe local features in images.
Sheng-hua Zhong, Yan Liu, Qing-cai Chen
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Object Recognition by Modified Scale Invariant Feature Transform

2008 Third International Workshop on Semantic Media Adaptation and Personalization, 2008
This paper presents a methodology for object recognition. It relies on the extraction of distinctive invariant image features that can be used to find the correspondence between different views of an object or a scene. These features are invariant to image rotation and scaling, they have substantial robustness to changes in viewpoint and illumination ...
null Gul-e-Saman, S. Asif M. Gilani
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Automated Urine Microscopy Using Scale Invariant Feature Transform

Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology, 2019
Urine microscopy is a tedious task that requires utmost care from the technician doing the job. In order to provide clearer images for accurate interpretation of urine samples, microscopic images must be properly focused. Likewise, it is essential for the technician to avoid contamination with the urine sample when doing the task, especially in the ...
Jennifer C. Dela Cruz, Ramon G. Garcia, Mikko Ivan D. Avilledo, John Christopher M. Buera, Rom Vincent S. Chan, Paul Gian T. Espana   +5 more
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Thumb Biometric Using Scale Invariant Feature Transform

2017
Recently, biometrics technology has been receiving attention as means of personal authentication in smartphone environment. Fingerprint recognition is generally contained in newest smartphones and other biometric methods such as iris recognition are receiving attention.
Naeun Lim, Daejune Ko, Kun Ha Suh, Eui Chul Lee   +3 more
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Object Detection Using Scale Invariant Feature Transform

2014
An object detection scheme using the Scale Invariant Feature Transform (SIFT) is proposed in this paper. The SIFT extracts distinctive invariant features from images and it is a useful tool for matching between different views of an object. This paper proposes how the SIFT can be used for an object detection problem, especially human detection problem.
Thao Nguyen, Eun-Ae Park, Jiho Han, Dong-Chul Park, Soo-Young Min   +4 more
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Orthogonal design for scale invariant feature transform optimization

Journal of Electronic Imaging, 2016
To improve object recognition capabilities in applications, we used orthogonal design (OD) to choose a group of optimal parameters in the parameter space of scale invariant feature transform (SIFT). In the case of global optimization (GOP) and local optimization (LOP) objectives, our aim is to show the operation of OD on the SIFT method.
Xintao Ding, Yonglong Luo, Yunyun Yi, Biao Jie, Taochun Wang, Weixin Bian   +5 more
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Super resolution based on scale invariant feature transform

2008 International Conference on Audio, Language and Image Processing, 2008
In this paper, SIFT (scale invariant feature transform) algorithm is used for the image registration of super resolution to ensure a more stable and accurate registration result, and thus improve the result of super-resolution which will be realized by least squares minimization.
null Zhi Yuan, null Peimin Yan, null Sheng Li   +2 more
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