Results 191 to 200 of about 155,901 (241)

3D Nano‐architected Polymer Shell Enables Reconfigurable Stabilized Blue Phase Soft Crystals

Advanced Functional Materials, EarlyView.
Spherical polymer shells featuring nanostructure of blue phase (BP) disclinations form via in situ photo‐polymerization within BP liquid crystals, providing controlled anchoring for BP nucleation and growth at room temperature and beyond. This architected confinement enhances BP thermal stability over a broad temperature range while enabling dynamic ...
Sepideh Norouzi, Yazael R. Morales‐Flores, Tadej Emersic, Jeremy Money, Otilio E. Rodriguez‐Lopez, Andrew Casale, Juan J. de Pablo, Jose A. Martinez‐Gonzalez, Monirosadat Sadati   +8 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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 ...
Y J Pavitra, S S Rekha, Prabhakar Mishra
openaire   +2 more sources

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.
Guihua Xia, Qidan Zhu, Yuquan Wang, Tong Wang   +3 more
openaire   +2 more sources

Keyboard recognition from scale-invariant feature transform

2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2017
Based on the scale-invariant feature transform, this paper presents an approach to keyboard recognition. Not only the skewed keyboard can be corrected, but also the keys in the keyboard can be located. Experimental results confirm the feasibility of the proposed method.
Ming-Te Chao, Yung-Sheng Chen
openaire   +2 more sources

Scale Invariant Feature Transform using oriented pattern

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), 2014
Image matching plays an important role in many aspects of computer vision. Our proposed method is based on Scale Invariant Feature Transform (SIFT) which is one of the popular image matching methods. The main ideas behind our method are removing the excess keypoints, adding oriented patterns to descriptor, and decreasing the size of the descriptors. By
Massoud Babaie-Zadeh, Mohammad Baghery Daneshvar, Seyed Ghorshi   +2 more
openaire   +2 more sources

An application of scale-invariant feature transform in iris recognition

2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS), 2013
Scale-invariant Feature Transform (SIFT) is an algorithm to find local features in images. SIFT uses Difference-of-Gaussian (DoG) to locate candidate keypoints and performs a detailed fit to locate keypoints, then orientations are added to keypoints and keypoint descriptor is generated for each keypoint.
Xiaodong Chen, Linhua Jiang, Ji Cheng, Weijie Zhao   +3 more
openaire   +2 more sources

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 ...
S.A.M. Gilani, Gul-e-Saman
openaire   +2 more sources

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.
Mark J. Burge, Wilhelm Burger
openaire   +2 more sources

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
openaire   +2 more sources

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.
Zhi Yuan, Sheng Li, Peimin Yan
openaire   +2 more sources