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Waste Classification Using Support Vector Machine with SIFT-PCA Feature Extraction
2020 4th International Conference on Informatics and Computational Sciences (ICICoS), 2020Population growth and changes in public consumption patterns cause increases in the volume, types and characteristics of the waste. This increase requires waste management effort.
Adita Putri Puspaningrum +6 more
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IEEE Transactions on Geoscience and Remote Sensing, 2020
In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique.
Pouriya Etezadifar, H. Farsi
semanticscholar +1 more source
In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique.
Pouriya Etezadifar, H. Farsi
semanticscholar +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
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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
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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
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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
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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
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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
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Proceedings of the 17th ACM international conference on Multimedia, 2009
Scale-invariant feature transform (SIFT) is a powerful tool extensively used in the community of pattern recognition and computer vision. However, the security issue of SIFT is relatively unexplored in the literature. This paper investigates the potential weakness of SIFT, meaning that the SIFT features can be deleted or destroyed while maintaining ...
Chao-Yung Hsu +2 more
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Scale-invariant feature transform (SIFT) is a powerful tool extensively used in the community of pattern recognition and computer vision. However, the security issue of SIFT is relatively unexplored in the literature. This paper investigates the potential weakness of SIFT, meaning that the SIFT features can be deleted or destroyed while maintaining ...
Chao-Yung Hsu +2 more
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TOP-SIFT: the selected SIFT descriptor based on dictionary learning
The Visual Computer, 2018The large amount of SIFT descriptors in an image and the high dimensionality of SIFT descriptor have made problems for the large-scale image database in terms of speed and scalability. In this paper, we present a descriptor selection algorithm based on dictionary learning to remove the redundant features and reserve only a small set of features, which ...
Yujie Liu 0002 +4 more
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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
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Proceedings of the December 5-7, 1972, fall joint computer conference, part I on - AFIPS '72 (Fall, part I), 1972
Many computer applications have stringent requirements for continued correct operation of the computer in the presence of internal faults. The subject of design of such highly reliable computers has been extensively studied, and numerous techniques have been developed to achieve this high reliability.
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Many computer applications have stringent requirements for continued correct operation of the computer in the presence of internal faults. The subject of design of such highly reliable computers has been extensively studied, and numerous techniques have been developed to achieve this high reliability.
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Linear sifting of decision diagrams
Proceedings of the 34th annual conference on Design automation conference - DAC '97, 1997We propose a new algorithm, called linear sifting, for theoptimization of decision diagrams that combines the efficiency of sifting and the power of linear transformations. We show that the new algorithm is applicable to large examples, and that inmany cases it leads to substantiallymore compact diagrams when compared to simple variablereordering.
Christoph Meinel +2 more
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