Mobile terminal gesture recognition based on improved FAST corner detection

Jian, Chengfeng; Xiang, Xiaoyu; Zhang, Meiyu

doi:10.1049/iet-ipr.2018.5959

Cited by 21 publications

(15 citation statements)

References 19 publications

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“…We used the FR‐CNN and tiny version as the backbone of the CKA‐based fingertip detection network, then compared it with the FAST algorithm and the improved FAST algorithm [19]. The dataset we used is Dexter, a gesture dataset of UCI, which is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Based on our previous work [19], we propose the CKA‐based fingertip detection network. Before that, to put our method into context, we will briefly review the key idea of the FAST algorithm.…”

Section: Algorithm Implementationmentioning

confidence: 99%

“…In this work, to solve the problem of the action is difficult to recognise when multiple fingers move in a small range, we propose a dynamic gesture recognition method on the basis of previous studies [19–21]. Firstly, we present a concavity kernel accumulation algorithm (CKA) and regard it as a region proposal generator.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real‐time multi‐trajectory matching for dynamic hand gesture recognition

Jian

2020

IET Image Processing

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Section: Methodsmentioning

confidence: 99%

Section: Algorithm Implementationmentioning

confidence: 99%

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Real‐time multi‐trajectory matching for dynamic hand gesture recognition

Jian

2020

IET Image Processing

Self Cite

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“…Although there is a parameter α in the equation of calculating t, it is different from the fixed value of the traditional FAST algorithm. Because the threshold t is calculated adaptively based on the actual image contrast and the parameter α is just an adjustment coefficient, which determines the number of feature points extracted from the image, it is easier to decide the value of α than to decide the value of t directly [21]. In this paper, the value of α is set as 0.01 by comprehensive consideration of the size of image and the computation complexity, based on the simple trial method.…”

Section: Image Matching Algorithmmentioning

confidence: 99%

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Gong

et al. 2020

Computational Intelligence and Neuroscience

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The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

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“…FAST corner detection algorithm is a feature extraction operation with high computational efficiency and high repeatability. It has been widely used in stereo image matching, image registration [25], target detection [26],target recognition [27], target tracking [28] and other fields, and has become the most popular corner detection method in the field of computer vision. However, the influence of noise and the threshold have great impact on this method.…”

Section: An Improved Fast Algorithm With Adaptive Thresholdingmentioning

confidence: 99%

Feature Detection and Matching With Linear Adjustment and Adaptive Thresholding

Cai

Ling

et al. 2020

IEEE Access

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Image feature detection and matching technologies are crucial aspects in machine vision. However it is still facing the dilemma between fast operation for real-time application and robust matching. To address this issue, we propose a robust and relatively fast method for image feature extraction and matching with linear adjustment and adaptive thresholding (LAAT) in this paper. The major challenge of this method is reducing the sensitivity to the brightness. To solve this problem, we adopt brightness and contrast adjustment for image pairs processed by Gaussian filtering. An adaptive thresholding FAST approach is applied for feature selection to improve the performance. The proposed method is compared with the traditional and state-of-the-art extraction methods on public dataset. Particularly, this paper focuses on the illumination change, image blur, and image rotation aspects. Experiments show that our proposed algorithm is superior to other algorithms in the comprehensive evaluation of various parameters, especially for illumination and blur transformations.

show abstract

Mobile terminal gesture recognition based on improved FAST corner detection

Cited by 21 publications

References 19 publications

Real‐time multi‐trajectory matching for dynamic hand gesture recognition

Real‐time multi‐trajectory matching for dynamic hand gesture recognition

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Feature Detection and Matching With Linear Adjustment and Adaptive Thresholding

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