Results 201 to 210 of about 155,901 (241)

Scale invariant feature transform evaluation in small dataset

2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2015
This paper investigates how we can achieve object recognition in an image by looking at some examples of training images. Scale Invariant Feature Transform (SIFT) is one proposal method to detect features in an image and then can use those features to distinguish between different objects.
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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.
Kun Ha Suh, Naeun Lim, Daejune Ko, Eui Chul Lee   +3 more
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Make Scale Invariant Feature Transform "Fly" with CUDA

This paper introduces an implementation of scale invariant feature transform (SIFT) algorithm with CUDA. Primary  steps  including  building the  Gaussian  pyramid  and the difference of Gaussian pyramid, identification, localization [1], and orientation  generation of key-points  are  realized  on GPU with CUDA.
Yuhong Mo, Chaoyi Tan, Chenghao Wang, Hao Qin, Yushan Dong   +4 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, Soo-Young Min, Eun-Ae Park, Ji-Ho Han, Dong-Chul Park   +4 more
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Enhancing Gadgets for Blinds Through Scale Invariant Feature Transform

2019
ICT can help blind people in movement and direction-finding tasks. This paper proposes a new methodology for safe mobility based on scale invariant feature transform (SIFT) that is expected to lead to higher precision and accuracy. Various existing gadgets for visually impaired are examined, and the conclusion is that the proposed methodology can ...
Kumar, Raman, Wiil, Uffe Kock
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Scale Invariant Feature Transform Based Image Matching and Registration [PDF]

2014 Fifth International Conference on Signal and Image Processing, 2014
This paper presents Image matching and registration method that is invariant to scale, rotation, translation and illumination changes. The method is named as Scale Invariant Feature Transform (SIFT). This algorithm will detect and describe image features such as contours, points, corners etc.
Heena R. Kher, V. K. Thakar
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Framework for texture classification and retrieval using scale invariant feature transform

2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE), 2012
Texture images can be characterized with key features extracted from images. In this paper, the scale invariant feature transform (hereinafter SIFT) algorithm is utilized to generate local features for texture image classification. The local features are selected as inputs for texture classification framework.
Do, Tuan, Aikala, Antti, Saarela, Olli
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Scale Invariant Feature Transform as feature tracking method in 4D imaging: A feasibility study

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
We propose the use of Scale Invariant Feature Transform (SIFT) as a method able to extract stable landmarks from 4D images and to quantify internal motion. We present a preliminary validation of the SIFT method relying on expert user identification of landmarks and then apply it to 4D lung CT and liver MRI data. Results demonstrate SIFT capabilities as
PAGANELLI, CHIARA, PERONI, MARTA, PENNATI, FRANCESCA, BARONI, GUIDO, P. Summers, M. Bellomi, RIBOLDI, MARCO   +6 more
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Study on improved scale Invariant Feature Transform matching algorithm

2010 Second Pacific-Asia Conference on Circuits, Communications and System, 2010
False matching feature points are caused by Scale Invariant Feature Transform (SIFT) which just considers the local feature information. In order to improve the precision of matching feature points, this paper proposes a method that combines the SIFT matching, epipolar restrict and regional matching.
Weili Liu, Youliang Yang, Lan Zhang
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GSIFT: geometric scale invariant feature transform for terrain data

SPIE Proceedings, 2006
In this work, we introduce GSIFT (Geometric Scale Invariant Terrain Feature Transform), geometric descriptors that are invariant to translation, rotation, and scaling. SIFT (Scale Invariant Feature Transform) descriptors have been found to be very successful in a variety of computer vision tasks.
Yongqin Xiao, Suresh K. Lodha
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