Results 211 to 220 of about 39,091 (262)
Some of the next articles are maybe not open access.
HALF-SIFT: High-Accurate Localized Features for SIFT
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009In this paper, the accuracy of feature points in images detected by the scale invariant feature transform (SIFT) is analyzed. It is shown that there is a systematic error in the feature point localization. The systematic error is caused by the improper subpel and subscale estimation, an interpolation with a parabolic function.
Kai Cordes +3 more
openaire +1 more source
AH-SIFT: Augmented Histogram based SIFT descriptor
2012 19th IEEE International Conference on Image Processing, 2012We propose Augmented Histogram (AH), a conceptually novel and systematic approach to enhancing the representational power of histogram-based local image descriptors such as SIFT. Our method takes a simple form that augments the histogram of local image patch features with a set of circular means and variances.
Hao Tang 0001, Feng Tang
openaire +1 more source
TOP-SIFT: A New Method for SIFT Descriptor Selection
2015 IEEE International Conference on Multimedia Big Data, 2015The large amount of SIFT descriptors in an image and the high dimensionality of SIFT descriptor has made problems for large-scale image dataset in terms of speed and scalability. In this paper, we propose a descriptor selection algorithm via dictionary learning and only a small set of features are reserved, which we refer to as TOP-SIFT.
Yujie Liu 0002 +4 more
openaire +1 more source
Proceedings of the ACM International Conference on Image and Video Retrieval, 2010
The best known Scale-Invariant Feature Transform (SIFT) shows its superior performance in a variety of image processing tasks due to its distinctiveness, invariance to scale, rotation and local geometric distortion. Despite its remarkable performance, SIFT is not invariant to mirror images and grayscale-inverted images.This paper proposes an improved ...
Rui Ma, Jian Chen, Zhong Su
openaire +1 more source
The best known Scale-Invariant Feature Transform (SIFT) shows its superior performance in a variety of image processing tasks due to its distinctiveness, invariance to scale, rotation and local geometric distortion. Despite its remarkable performance, SIFT is not invariant to mirror images and grayscale-inverted images.This paper proposes an improved ...
Rui Ma, Jian Chen, Zhong Su
openaire +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
Scale invariant feature detectors often find stable scales in only a few image pixels. Consequently, methods for feature matching typically choose one of two extreme options: matching a sparse set of scale invariant features, or dense matching using arbitrary scales.
Tal Hassner +3 more
openaire +2 more sources
Scale invariant feature detectors often find stable scales in only a few image pixels. Consequently, methods for feature matching typically choose one of two extreme options: matching a sparse set of scale invariant features, or dense matching using arbitrary scales.
Tal Hassner +3 more
openaire +2 more sources
VF-SIFT: Very Fast SIFT Feature Matching
2010Feature-based image matching is one of the most fundamental issues in computer vision tasks. As the number of features increases, the matching process rapidly becomes a bottleneck. This paper presents a novel method to speed up SIFT feature matching. The main idea is to extend SIFT feature by a few pairwise independent angles, which are invariant to ...
Faraj Alhwarin +2 more
openaire +1 more source
Proceedings of the 14th International Conference on Information Processing in Sensor Networks, 2015
As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world.
Chieh-Jan Mike Liang +7 more
openaire +1 more source
As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world.
Chieh-Jan Mike Liang +7 more
openaire +1 more source
2013 International Conference on 3D Vision, 2013
3D localization approaches establish correspondences between points in a query image and a 3D point cloud reconstruction of the environment. Traditionally, the dataBase models are created from photographs using Structure-from-Motion (SfM) techniques, which requires large collections of densely sampled images.
Dominik Sibbing +3 more
openaire +1 more source
3D localization approaches establish correspondences between points in a query image and a 3D point cloud reconstruction of the environment. Traditionally, the dataBase models are created from photographs using Structure-from-Motion (SfM) techniques, which requires large collections of densely sampled images.
Dominik Sibbing +3 more
openaire +1 more source
A Method of Acceleration Applied in Symmetric-SIFT and SIFT
2013Symmetric-SIFT is an effective technique used for registering multimodal images. It is based on a well-known image registration technique named Scale Invariant Feature Transform (SIFT). Similar to SIFT, Symmetric-SIFT detects many stable keypoints even though not all of which are useful.
Dong Zhao +3 more
openaire +1 more source
Proceedings of the 15th International Conference on Emerging Networking Experiments And Technologies, 2019
Sift is a new consensus protocol for replicating state machines. It disaggregates CPU and memory consumption by creating a novel system architecture enabled by one-sided RDMA operations. We show that this system architecture allows us to develop a consensus protocol which centralizes the replication logic.
Mikhail Kazhamiaka +6 more
openaire +1 more source
Sift is a new consensus protocol for replicating state machines. It disaggregates CPU and memory consumption by creating a novel system architecture enabled by one-sided RDMA operations. We show that this system architecture allows us to develop a consensus protocol which centralizes the replication logic.
Mikhail Kazhamiaka +6 more
openaire +1 more source