Optimization of SIFT algorithm for fast-image feature extraction in line-scanning ophthalmoscope

He, Yi; Deng, Guohua; Wang, Yuan‐Yuan; Ling, Wei; Yang, Jinsheng; Li, Xiqi; Zhang, Yudong

doi:10.1016/j.ijleo.2017.09.075

Cited by 34 publications

(14 citation statements)

References 20 publications

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“…This is used to achieve the image feature extraction. This performance is eventually used for matching image [14]. An SIFT improvement with the comparative method of measuring the entire response belongs to the neighbor's interest point [15].…”

Section: Feature Extraction Siftmentioning

confidence: 99%

Spotting Brain and Pancreatic Tumor using Fuzzy C-Mean Segmentation and SIFT Extraction Through Sparse Representation Method

2019

IJRTE

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Diagnosis of Neoplasm is an utmost recurrent and lethal technique for detecting a malignant primary tumor. Imaging techniques empower researchers and medical practitioners to evaluate disorders and activities inside the human brain earlier than performing invasive surgery. Here presents the spotting and detection of brain tumor and pancreatic tumor segmentation and classification progression with several stages DBCWMF algorithm filter with histogram equation, Precise Fuzzy C-segmentation, and SIFT extraction and classification with Sparse representation. These techniques provide a better ability in clinical practices in terms of speed, accuracy, innovation. Experimental results were evaluated using TCIA database and hospital database, where the proposed approaches were verified simultaneously with data progression and incredibly effective for brain and pancreatic tumor in MR images and CT scan images both.

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Section: Feature Extraction Siftmentioning

confidence: 99%

Spotting Brain and Pancreatic Tumor using Fuzzy C-Mean Segmentation and SIFT Extraction Through Sparse Representation Method

2019

IJRTE

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“…The scale-invariant feature transform (SIFT) algorithm [9][10][11][12] is a partial matching algorithm. It obtains key points in different scale spaces established by the Gaussian fuzzy function.…”

Section: Related Work 21 Siftmentioning

confidence: 99%

An Efficient Feature Extraction Scheme for Mobile Anti-Shake in Augmented Reality

et al. 2018

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Abstract:In recent years, augmented reality on mobile devices has become popular. Mobile shakes are the most typical type of interference in mobile augmented reality. To negate such interference, anti-shake is an urgent requirement. To enhance anti-shake efficiency, we propose an efficient feature extraction scheme for mobile anti-shake in augmented reality. The scheme directly detects corners to avoid the non-extreme constraint such that the efficiency of feature extraction is improved. Meanwhile, the scheme only updates the added corners during mobile shakes, which improves the accuracy of feature extraction. In the experiments, the memory consumption of existing methods is almost double compared to that in our scheme. Further, the runtime of our scheme is only half of the runtime of the existing methods. The experimental results demonstrate that our scheme performs better than the existing classic methods on mobile anti-shake in terms of memory consumption, efficiency, and accuracy.

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“…Speeded up robust features (SURF) are presented based on Hessian matrix and image convolutions to faster the computing [18]. There are some other techniques that have also been introduced to achieve better performance [9,[31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Image Matching via a Novel Feature Describing Model

Zhou

Duan

et al. 2019

Applied Sciences

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Computer vision has been developed greatly in the past several years, and many useful and interesting technologies have been presented and widely applied. Image matching is an important technology based on similarity measurement. In this paper, we propose a novel feature describing model based on scale space and local principle component analysis for heterogeneous image matching. The traditional uniform eight-direction statistics is updated by a task-related k-direction statistics based on prior information of the keypoints. In addition, the k directions are determined by an approximately solution of a Min-Max problem. The principle component analysis is introduced to compute the main directions of local patches based on the gradient field. In addition, the describing vector is formed by then implementing PCA on each sub-patch of a 4 × 4 mesh. Experimental results show the accuracy and efficiency of proposed method.

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Optimization of SIFT algorithm for fast-image feature extraction in line-scanning ophthalmoscope

Cited by 34 publications

References 20 publications

Spotting Brain and Pancreatic Tumor using Fuzzy C-Mean Segmentation and SIFT Extraction Through Sparse Representation Method

Spotting Brain and Pancreatic Tumor using Fuzzy C-Mean Segmentation and SIFT Extraction Through Sparse Representation Method

An Efficient Feature Extraction Scheme for Mobile Anti-Shake in Augmented Reality

Heterogeneous Image Matching via a Novel Feature Describing Model

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