Results 291 to 300 of about 178,695 (309)

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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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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Efficient heterogeneous face recognition using Scale Invariant Feature Transform

2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), 2014
Face recognition includes analysis of an image and extracting its facial features which will help to discriminate it from others. Scale invariant feature transform (SIFT) to extract distinctive invariant features from images can be used to perform reliable matching.
Vrushali Purandare, K T Talele
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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.
null Youliang Yang, null Weili Liu, null Lan Zhang   +2 more
openaire   +1 more source

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.
Suresh K. Lodha, Yongqin Xiao
openaire   +1 more source

3D face representation using scale and transform invariant features

2008 IEEE 16th Signal Processing, Communication and Applications Conference, 2008
In this study a representation using scale and invariant generic 3D features, for 3D facial models is proposed. These generic feature vectors obtained from descriptive parts of the face like eyes, nose, or nose saddle, are then convolved into a graphical model where a characteristic topology for a 3D facial model representation is achieved. These scale
Erdem Akagunduz, Ilkay Ulusoy
openaire   +1 more source

Scale Adaptive Kernelized Correlation Filter with Scale-Invariant Feature Transform

2017
In order to solve the problem of scale variation in Kernelized Correlation Filter (KCF) tracker, a scale adaptive tracking method based on Scale-Invariant Feature Transform (SIFT) is proposed. Firstly, it uses SIFT to extract and match keypoints between two successive frames to estimate the new scale of the target.
Xueting Qiao, Yingmin Jia
openaire   +1 more source

Scale Invariant Feature Transform Based Image Matching and Registration

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, Vishvjit K. Thakar
openaire   +1 more source

Scale Invariant Feature Transform with Irregular Orientation Histogram Binning

2009
The SIFT (Scale Invariant Feature Transform) descriptor is a widely used method for matching image features. However, perfect scale invariance can not be achieved in practice because of sampling artefacts, noise in the image data, and the fact that the computational effort limits the number of analyzed scale space images.
Cui, Y., Hasler, N., Thormählen, T., Seidel, H. ; https://orcid.org/0000-0002-1343-8613   +3 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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