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Image and Information Processing|13 Article(s)
Research and application of uniform material counting method based on machine vision
Suhua XIAO, Mingjuan QIAO, Zhiyong WANG, Wei WANG, Youzhi FU, and Shusen and GUO
Manufacturing and agricultural industries use manual methods to count materials. This leads to low accuracy and inefficiency. This paper proposes a secondary counting method that combines main and differential counting. The area-fill identification algorithm is applied to mark the counted materials. To verify the effectiveness of the proposed counting algorithm, numbers of countings are conducted for different materials, such as the screws, hole gaskets, beans, jujube, etc. The results show that the counting accuracy reaches 98% for materials with size of 2—20 mm. The method has delivered a high-efficiency and high-accuracy automatic intelligent counting, with a wide range of application prospects and reference value. Manufacturing and agricultural industries use manual methods to count materials. This leads to low accuracy and inefficiency. This paper proposes a secondary counting method that combines main and differential counting. The area-fill identification algorithm is applied to mark the counted materials. To verify the effectiveness of the proposed counting algorithm, numbers of countings are conducted for different materials, such as the screws, hole gaskets, beans, jujube, etc. The results show that the counting accuracy reaches 98% for materials with size of 2—20 mm. The method has delivered a high-efficiency and high-accuracy automatic intelligent counting, with a wide range of application prospects and reference value.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 2, 123 (2023)
TBNN: totally-binary neural network for image classification
Qingsong ZHANG, Linjun SUN, Guowei YANG, Baoli LU, Xin NING, and Weijun and LI
Most binary networks apply full precision convolution at the first layer. Changing the first layer to the binary convolution will result in a significant loss of accuracy. In this paper, we propose a new approach to solve this problem by widening the data channel to reduce the information loss of the first convolutional input through the sign function. In addition, widening the channel increases the computation of the first convolution layer, and the problem is solved by using group convolution. The experimental results show that the accuracy of applying this paper's method to state-of-the-art (SOTA) binarization method is significantly improved, proving that this paper's method is effective and feasible. Most binary networks apply full precision convolution at the first layer. Changing the first layer to the binary convolution will result in a significant loss of accuracy. In this paper, we propose a new approach to solve this problem by widening the data channel to reduce the information loss of the first convolutional input through the sign function. In addition, widening the channel increases the computation of the first convolution layer, and the problem is solved by using group convolution. The experimental results show that the accuracy of applying this paper's method to state-of-the-art (SOTA) binarization method is significantly improved, proving that this paper's method is effective and feasible.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 2, 117 (2023)
A BLG1 neural model implements the unique looming selectivity to diving target
Hao LUAN, Mu HUA, Yicheng ZHANG, Shigang YUE, and Qinbing FU
The bistratified lobula giant type 1 (BLG1) neuron is an identified looming-sensitive neuron in crab’s visual brain that demonstrates special sensitivity to diving targets, or descending approaching motions. In this paper, a novel neural model is proposed to shape such unique selectivity through incorporating a bio-plausible feedforward contrast inhibition synapse and a radially extending spatial enhancement distribution. Herein the synaptic connections and neuronal functions of this model are placed within a framework for matching and describing underlying biological findings. The systematic and comparative experiments have validated the proposed computational model that reconciles with the characteristics of BLG1 neurons in crab. The bistratified lobula giant type 1 (BLG1) neuron is an identified looming-sensitive neuron in crab’s visual brain that demonstrates special sensitivity to diving targets, or descending approaching motions. In this paper, a novel neural model is proposed to shape such unique selectivity through incorporating a bio-plausible feedforward contrast inhibition synapse and a radially extending spatial enhancement distribution. Herein the synaptic connections and neuronal functions of this model are placed within a framework for matching and describing underlying biological findings. The systematic and comparative experiments have validated the proposed computational model that reconciles with the characteristics of BLG1 neurons in crab.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 2, 112 (2023)
Multi-object tracking based on deep associated features for UAV applications
Lingyu XIONG, and Guijin TANG
Multi-object tracking (MOT) techniques have been increasingly applied in a diverse range of tasks. Unmanned aerial vehicle (UAV) is one of its typical application scenarios. Due to the scene complexity and the low resolution of moving targets in UAV applications, it is difficult to extract target features and identify them. In order to solve this problem, we propose a new re-identification (re-ID) network to extract association features for tracking in the association stage. Moreover, in order to reduce the complexity of detection model, we perform the lightweight optimization for it. Experimental results show that the proposed re-ID network can effectively reduce the number of identity switches, and surpass current state-of-the-art algorithms. In the meantime, the optimized detector can increase the speed by 27% owing to its lightweight design, which enables it to further meet the requirements of UAV tracking tasks. Multi-object tracking (MOT) techniques have been increasingly applied in a diverse range of tasks. Unmanned aerial vehicle (UAV) is one of its typical application scenarios. Due to the scene complexity and the low resolution of moving targets in UAV applications, it is difficult to extract target features and identify them. In order to solve this problem, we propose a new re-identification (re-ID) network to extract association features for tracking in the association stage. Moreover, in order to reduce the complexity of detection model, we perform the lightweight optimization for it. Experimental results show that the proposed re-ID network can effectively reduce the number of identity switches, and surpass current state-of-the-art algorithms. In the meantime, the optimized detector can increase the speed by 27% owing to its lightweight design, which enables it to further meet the requirements of UAV tracking tasks.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 2, 105 (2023)
Fitting objects with implicit polynomials by deep neural network
Jingyi LIU, Lina YU, Linjun SUN, Yuerong TONG, Min WU, and Weijun and LI
Implicit polynomials (IPs) are considered as a powerful tool for object curve fitting tasks due to their simplicity and fewer parameters. The traditional linear methods, such as 3L, MinVar, and MinMax, often achieve good performances in fitting simple objects, but usually work poorly or even fail to obtain closed curves of complex object contours. To handle the complex fitting issues, taking the advantages of deep neural networks, we designed a neural network model continuity-sparsity constrained network (CSC-Net) with encoder and decoder structure to learn the coefficients of IPs. Further, the continuity constraint is added to ensure the obtained curves are closed, and the sparseness constraint is added to reduce the spurious zero sets of the fitted curves. The experimental results show that better performances have been obtained on both simple and complex object fitting tasks. Implicit polynomials (IPs) are considered as a powerful tool for object curve fitting tasks due to their simplicity and fewer parameters. The traditional linear methods, such as 3L, MinVar, and MinMax, often achieve good performances in fitting simple objects, but usually work poorly or even fail to obtain closed curves of complex object contours. To handle the complex fitting issues, taking the advantages of deep neural networks, we designed a neural network model continuity-sparsity constrained network (CSC-Net) with encoder and decoder structure to learn the coefficients of IPs. Further, the continuity constraint is added to ensure the obtained curves are closed, and the sparseness constraint is added to reduce the spurious zero sets of the fitted curves. The experimental results show that better performances have been obtained on both simple and complex object fitting tasks.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 1, 60 (2023)
Computation and analysis of aero-optic imaging deviation of a blunt nosed aircraft with Mach number 0.5-3
Liang XU, Shiwei ZHAO, Wei XUE, and Tao and WANG
Aero-optic imaging is a kind of optical effect, which describes the imaging deviation on the imaging plane. In this paper, the effect of the change of Mach number of blunt aircraft on the aero-optic imaging deviation is studied. The imaging deviations of Mach number 0.5-3 are analyzed systematically. The results show that with the increase of Mach number, imaging deviation increases gradually, and the increase rate is gradually slow. Imaging deviation slope decreases gradually with the increase of Mach number, and gradually tends to be zero, suggesting that imaging deviation is not sensitive to the change of the larger Mach number. In other words, the Mach number of smaller changes can lead to larger imaging deviation. As the Mach number of the aircraft increases, the slope of the imaging offset tends to be closer and closer to 0. When the Mach number of the aircraft increases to a certain extent, the change of the imaging offset will not have much influence. Therefore, in order to reduce the impact of flight speed on imaging migration, the aircraft should fly at a higher Mach number. Aero-optic imaging is a kind of optical effect, which describes the imaging deviation on the imaging plane. In this paper, the effect of the change of Mach number of blunt aircraft on the aero-optic imaging deviation is studied. The imaging deviations of Mach number 0.5-3 are analyzed systematically. The results show that with the increase of Mach number, imaging deviation increases gradually, and the increase rate is gradually slow. Imaging deviation slope decreases gradually with the increase of Mach number, and gradually tends to be zero, suggesting that imaging deviation is not sensitive to the change of the larger Mach number. In other words, the Mach number of smaller changes can lead to larger imaging deviation. As the Mach number of the aircraft increases, the slope of the imaging offset tends to be closer and closer to 0. When the Mach number of the aircraft increases to a certain extent, the change of the imaging offset will not have much influence. Therefore, in order to reduce the impact of flight speed on imaging migration, the aircraft should fly at a higher Mach number.
Optoelectronics Letters
- Publication Date: Jan. 01, 2023
- Vol. 19, Issue 1, 55 (2023)
Depth image super-resolution algorithm based on struc-tural features and non-local means
Jing WANG, Wei-zhong ZHANG, Bao-xiang HUANG, and Huan YANG
The resolution and quality of the depth map captured by depth cameras are limited due to sensor hardware limitations, which becomes a roadblock for further computer vision applications. In order to solve this problem, we propose a new method to enhance low-resolution depth maps using high-resolution color images. The structural-aware term is intro-duced because of the availability of structural information in color images and the assumption of identical structural features within local neighborhoods of color images and depth images captured from the same scene. We integrate the structural-aware term with color similarity and depth similarity within local neighborhoods to design a local weighting filter based on structural features. To use non-local self-similarity of images, the local weighting filter is combined with the concept of non-local means, and then a non-local weighting filter based on structural features is designed. Some ex-perimental results show that super-resolution depth image can be reconstructed well by the process of the non-local fil-ter and the local filter based on structural features. The proposed method can reconstruct much better high-resolution depth images compared with previously reported methods. The resolution and quality of the depth map captured by depth cameras are limited due to sensor hardware limitations, which becomes a roadblock for further computer vision applications. In order to solve this problem, we propose a new method to enhance low-resolution depth maps using high-resolution color images. The structural-aware term is intro-duced because of the availability of structural information in color images and the assumption of identical structural features within local neighborhoods of color images and depth images captured from the same scene. We integrate the structural-aware term with color similarity and depth similarity within local neighborhoods to design a local weighting filter based on structural features. To use non-local self-similarity of images, the local weighting filter is combined with the concept of non-local means, and then a non-local weighting filter based on structural features is designed. Some ex-perimental results show that super-resolution depth image can be reconstructed well by the process of the non-local fil-ter and the local filter based on structural features. The proposed method can reconstruct much better high-resolution depth images compared with previously reported methods.
Optoelectronics Letters
- Publication Date: Jan. 01, 2018
- Vol. 14, Issue 5, 391 (2018)
Scaling-based energy-quality multilevel control for aerial imagery
Xiao-hui GONG, Hao LIU, Jia-tong SUN, Xin-sheng ZHANG, and Xiao-fan SUN
This paper designs an energy-quality multilevel framework for the coding and transmission of aerial images, and then introduces a scaling-based intra encoder with flexible sampling factor (SF) and quantization parameter (QP). By experimentally investigating how different coding configurations affect the complexity-rate-quality characteristics of aerial images, this paper derives a configuration estimation model between energy-quality level and appropriate (SF, QP) configuration. By utilizing the model, a bivariate control scheme is proposed so as to progressively adjust sender's energy consumption under quality constraints. The experimental results show that the proposed scheme can achieve better energy-quality tradeoff with a wider quality range, and reduce the energy consumption above a certain quality. This paper designs an energy-quality multilevel framework for the coding and transmission of aerial images, and then introduces a scaling-based intra encoder with flexible sampling factor (SF) and quantization parameter (QP). By experimentally investigating how different coding configurations affect the complexity-rate-quality characteristics of aerial images, this paper derives a configuration estimation model between energy-quality level and appropriate (SF, QP) configuration. By utilizing the model, a bivariate control scheme is proposed so as to progressively adjust sender's energy consumption under quality constraints. The experimental results show that the proposed scheme can achieve better energy-quality tradeoff with a wider quality range, and reduce the energy consumption above a certain quality.
Optoelectronics Letters
- Publication Date: Jan. 01, 2018
- Vol. 14, Issue 5, 384 (2018)
A band selection method of hyperspectral remote sens-ing based on particle frog leaping algorithm
Lin-lin MU, Chao-zhu ZHANG, Peng-fei CHI, and Lian LIU
Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a band selection method for hyperspectral remote sensing images based on subspace partition and particle frog leaping optimization algorithm is proposed. Three new evolution strategies are designed to form a probabilistic network extension structure to avoid local convergence. At the same time, the information entropy of the selected band subset is used as the weight of inter-class separability, and a new band selection criterion function is constructed. The simulation results show that the proposed algorithm has certain advantages over the existing similar algorithms in terms of classification accuracy and running time. Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a band selection method for hyperspectral remote sensing images based on subspace partition and particle frog leaping optimization algorithm is proposed. Three new evolution strategies are designed to form a probabilistic network extension structure to avoid local convergence. At the same time, the information entropy of the selected band subset is used as the weight of inter-class separability, and a new band selection criterion function is constructed. The simulation results show that the proposed algorithm has certain advantages over the existing similar algorithms in terms of classification accuracy and running time.
Optoelectronics Letters
- Publication Date: Jan. 01, 2018
- Vol. 14, Issue 4, 316 (2018)
Multi-focus image fusion with half weighted gradient and self-similarity
Chao-ben DU, Ying LIU, and She-sheng GAO
In order to get a satisfactory image fusion effect, getting a focus map is very necessary and usually difficult to finish. In this paper, we address this problem with a half weighted gradient approach, aiming to obtain a direct mapping be-tween focus map and source images. Based on the advantages of multi-scale weighted gradient, while abandoning the shortcomings of weighted gradient, a new multi-focus image fusion method called half weighted gradient and self-similarity (HWGSS) is proposed. Experimental results validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations. In order to get a satisfactory image fusion effect, getting a focus map is very necessary and usually difficult to finish. In this paper, we address this problem with a half weighted gradient approach, aiming to obtain a direct mapping be-tween focus map and source images. Based on the advantages of multi-scale weighted gradient, while abandoning the shortcomings of weighted gradient, a new multi-focus image fusion method called half weighted gradient and self-similarity (HWGSS) is proposed. Experimental results validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.
Optoelectronics Letters
- Publication Date: Jan. 01, 2018
- Vol. 14, Issue 4, 311 (2018)
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