Research on Binocular Visual System of Robotic Arm Based on Improved SURF Algorithm

Sheng, Haiyan; Wei, Shimin; Yu, Xin; Tang, Lijun

doi:10.1109/jsen.2019.2951601

Cited by 22 publications

(9 citation statements)

References 14 publications

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“…Figure 10 shows the result of SURF matching [ 44 ] on the corner-based segmented images. Due to the high similarity between the detected feature points on the checkboard, there must be a lot of mismatches in SURF matching.…”

Section: The Proposed Methodsmentioning

confidence: 99%

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

Yang

Guo

Song

et al. 2022

Entropy

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This paper proposes an improved human-body-segmentation algorithm with attention-based feature fusion and a refined corner-based feature-point design with sub-pixel stereo matching for the anthropometric system. In the human-body-segmentation algorithm, four CBAMs are embedded in the four middle convolution layers of the backbone network (ResNet101) of PSPNet to achieve better feature fusion in space and channels, so as to improve accuracy. The common convolution in the residual blocks of ResNet101 is substituted by group convolution to reduce model parameters and computational cost, thereby optimizing efficiency. For the stereo-matching scheme, a corner-based feature point is designed to obtain the feature-point coordinates at sub-pixel level, so that precision is refined. A regional constraint is applied according to the characteristic of the checkerboard corner points, thereby reducing complexity. Experimental results demonstrated that the anthropometric system with the proposed CBAM-based human-body-segmentation algorithm and corner-based stereo-matching scheme can significantly outperform the state-of-the-art system in accuracy. It can also meet the national standards GB/T 2664-2017, GA 258-2009 and GB/T 2665-2017; and the textile industry standards FZ/T 73029-2019, FZ/T 73017-2014, FZ/T 73059-2017 and FZ/T 73022-2019.

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Section: The Proposed Methodsmentioning

confidence: 99%

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

Yang

Guo

Song

et al. 2022

Entropy

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“…Recently, most of the studies [12][13][14][15][16] on SURF have been related to combination with other algorithms [12][13][14][15] or application-specific modification [16] without reducing high memory usage and heavy computations in SURF itself. To be specific, [12] improved SURF by adding feedback mechanism based on CNN in order to reduce distortion in image mosaic.…”

Section: Related Workmentioning

confidence: 99%

“…[16] modified SURF by adjusting parameters by lowering hessian threshold and increasing the overall size of box filters and the image pyramid levels in order to differentiate characteristics of high-quality fundus images. Therefore, this paper is differentiated from the recent studies [12][13][14][15][16] in the sense that the proposed schemes dynamically optimize SURF itself to reduce high memory usage and heavy computations.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic optimization of hessian determinant image pyramid for memory‐efficient and high performance keypoint detection in SURF

Cho

Kim

2021

IET Image Processing

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With increasing demand for scale-invariant and fast object recognition, speeded up robust features (SURF) have emerged and become a widely used feature extraction algorithm in computer vision. Nevertheless, SURF still requires high memory usage and heavy computations caused by the keypoint detection procedure that produces huge image pyramid composed of many hessian determinant (HD) data for supporting the scale-invariance. Therefore, in this paper, dynamic optimization schemes of the HD image pyramid are proposed for completely removing the redundancies of the keypoint detection with keeping the original functionality of SURF without any loss. The proposed approach has shown to reduce memory usage by 45-51%, the execution time by 37-50% and the power consumption by 18-42% when compared with the keypoint detection in the original SURF algorithm.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Machine vision, which is the family of methods used to provide imaging-based and imageprocessing-based inspections and analyses, is an indispensable element of automation. The implementations of machine vision approaches can be found in process control [1], automatic quality control [2], and especially in industrial robot programming and guidance [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network

Štursa¹,

Honc²,

Doležel³

2021

Adv. sci. technol. eng. syst. j.

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Programming industrial robots in a real-life environment is a significant task necessary to be dealt with in modern facilities. The "pick up and place" task is undeniably one of the regular robot programming problems which needs to be solved. At the beginning of the "pick and place" task, the position determination and exact detection of the objects for picking must be performed. In this paper, an advanced approach to the detection and positioning of various objects is introduced. The approach is based on two consecutive steps. Firstly, the captured scene, containing attentive objects, is transformed using a segmentation neural network. The output of the segmentation process is a schematic image in which the types and positions of objects are represented by gradient circles of various colors. Secondly, these particular circle positions are determined by finding the local maxima in the schematic image. The proposed approach is tested on a complex detection and positioning problem by evaluation of total accuracy.

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Research on Binocular Visual System of Robotic Arm Based on Improved SURF Algorithm

Cited by 22 publications

References 14 publications

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

Dynamic optimization of hessian determinant image pyramid for memory‐efficient and high performance keypoint detection in SURF

Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network

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