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3rd International Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

作者： Xue, Honghui Ma, Jinshan Cai, Zheyi Fu, Junfang Guo, Feng Weng, Wei Dong, Yunxin Zhang, Zhenchang College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Zhongke Zhongxin Intelligent Technology Co. Ltd Fuzhou China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou China Department of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9783031400698

Recently, in order to pursue better detection results, more convolutional layers and deeper networks are a direction pursued by everyone. However, more and more down-sampling convolution or up-sampling operations generate feature maps of different scales, which makes it difficult to avoid the loss of detailed information of the image, and the distribution of different scales features will be misaligned. In particular, the loss and dislocation of the target boundary information will affect the features learned by the model and reduce the accuracy. This paper proposes a feature alignment method based on non-local idea, and designed two modules—Non Local Align Module (NLA) and Channel Fusion Augment Module (CFA). At the same time, the neighborhood calculation algorithm is also designed for it, which strengthens the binding force on the calculation of boundary information. These two modules can be easily embedded into the current mainstream object detection network to improve the detection effect of the model. Compared to the previous model, the network using our NLA module and CFA module achieves better results than the original model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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First-Principles Study of the Heavy Metals Adsorption on Sns2 And Janus Monolayers

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SSRN 2025年

作者： Zhu, Xiaoyu Liu, Chi Shen, Tao Liu, Xin Sun, Feifei Feng, Yue Heilongjiang Provincial Key Laboratory of Pattern Recognition and Information Perception Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Quantum Manipulation &Control Harbin University of Science and Technology Harbin150080 China College of Science and Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin150080 China

Heavy metal contamination in water bodies caused serious threats to health and ecosystems, necessitating adsorbing materials for rapid decrease in heavy metals. Therefore, we have carried out a first-principles study on the rapid decrease of heavy metals (Hg, As, Pb) by SnXS (X= S, O, Se) monolayer. Among, compared with SnS2, Janus structure SnXS (X= O, Se) narrows the band gap, and increases the electronic interactions between SnXS (X= O, Se) and heavy metals (Hg, As, Pb), thus exhibiting excellent adsorption capacity. In detail, the minimum increase in adsorption capabilities for SnOS and SnSeS are 67.6 % and 27.2 %, respectively. Moreover, the optical properties indicate that at the wavelength of 1550 nm, the refractive indexes of SnSeS are reduced after adsorbing As and Pb. Similarly, at the wavelength of 1310 nm, the refractive index of SnOS is reduced after adsorbing Hg. Hence, SnXS (X= O, Se) shows a prominent refractive index change characteristic, which has the potential as a functional nanomaterial in the field of fiber optic heavy metal ion detection. © 2025, The Authors. All rights reserved.

关键词： Monolayers

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Local Neighbor Propagation on Graphs for Robust Feature Matching

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Su, Lumei Zhou, Jiaxing Wang, Dahan Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University China College of Computer and Control Engineering Minjiang University China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China

Establishing reliable correspondences between two sets of feature points is a critical preprocessing step in many computer vision and pattern recognition tasks. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based feature matching (LNPG) method, to obtain good correspondences for feature matching. LNPG starts from a novel neighborhood graph construction strategy. The strategy leverages the spatial consistency constraint to generate a series of neighborhood sets, and employs the residual information to preserve the local neighborhood relationships of potential inliers (i.e., true matches). Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connections between data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm for robust feature matching. This clustering algorithm introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for cluster detection and cluster merging during the clustering process, respectively. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Experiments on publicly available datasets with different types of image transformations show the superiority of our LNPG in comparison with other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Adaptive Pixel Pair Generation Strategy for Image Matting Methods Based on Pixel Pair Optimization 19th

Adaptive Pixel Pair Generation Strategy for Image Matting Me...

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19th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2024

作者： Zheng, Jiamin Wen, Wen Liang, Yihui Feng, Fujian Xu, Xiang School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (纸本)9789819799183

Natural image matting plays a crucial role in numerous real-world applications. Image matting methods based on pixel pair optimization is a type of matting algorithm, which has significant advantages in parallel computation, handling images with similar foreground and background, and operating under limited computing time. However, these methods cannot provide high-quality alpha mattes for high-resolution images within limited computational resources. In this paper, we design an adaptive pixel pair generation strategy to solve the above problem. This strategy adaptively generates pixel pair for the unknown pixel according to its estimated alpha value, which promotes diversity of pixel pair and improves the efficiency of pixel pair optimization. It categorizes unknown pixels into three types and additionally incorporates nearby foreground and background pixels to generate pixel pair. Experimental results show that using our strategy can achieve high-quality alpha mattes and competitive matting performance under limited computational resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pixels

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Fire Detection Based on Flame Enhancement for Weak Fires 19th

Fire Detection Based on Flame Enhancement for Weak Fires

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19th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2024

作者： Chen, Kuan Wen, Wen Feng, Fujian Xu, Xiang Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (纸本)9789819799183

Detecting weak fire, such as overexposed and highly transparent flames, remains a significant challenge in vision-based fire detection. Convolutional Neural Network (CNN) based methods are widely used for automatic fire feature extraction, but they struggle to accurately recognize overexposed flames similar to the background and highly transparent flames that blend with the background, leading to false negative fire detection results. To address this issue, we have established a large-scale fire detection dataset with weak flames and introduce a fire detection method based on flame context enhancement. While employing a flame feature extraction module to identify and delineate the potential flame area, the proposed method primarily introduces a flame context module to capture the effective features of weak flames from this area, significantly enhancing the understanding of fires. Experimental results show that the proposed method can effectively reduce false negatives and improves the recall rate for detecting weak flames. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Premixed flames

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Biologically inspired visual computing:the state of the art

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Frontiers of Computer Science 2021年第1期15卷 1-15页

作者： Wangli HAO Ian Max ANDOLINA Wei WANG Zhaoxiang ZHANG Research Center for Research on Intelligent Perception and Computing Beijing 100190China National Laboratory of Pattern Recognition CASIABeijing 100190China CAS Center for Excellence in Brain Science and Intelligence Technology CASBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China State Key Laboratory of Neuroscience Shanghai 200031China Institute of Neuroscience Chinese Academy of SciencesShanghai 200031China

Visual information is highly advantageous for the evolutionary success of almost all *** information is likewise critical for many computing tasks,and visual computing has achieved tremendous successes in numerous applications over the last 60 years or *** that time,the development of visual computing has moved forwards with inspiration from biological mechanisms many *** particular,deep neural networks were inspired by the hierarchical processing mechanisms that exist in the visual cortex of primate brains(including ours),and have achieved huge breakthroughs in many domainspecific visual *** order to better understand biologically inspired visual computing,we will present a survey of the current work,and hope to offer some new avenues for rethinking visual computing and designing novel neural network architectures.

关键词： brain-inspired vision neural models intelligence novel neural networks

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Autonomous Obstacle Avoidance Scheme Using Monocular Vision Applied to Mobile Robots

Autonomous Obstacle Avoidance Scheme Using Monocular Vision ...

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2020 International Conference on intelligent control, Measurement and Signal Processing and intelligent Oil Field, ICMSP 2020

作者： Song, Yun Yun Yang, Sheng Yi Liu, Chao Wu, Qing Gui School of Data Science and Information Engineering Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang China School of Mechatronics Engineering Guizhou Minzu University Guiyang China

Mobile robots cannot move in an unknown environment with static or slow-moving obstacles effectively. We present an enhanced obstacle avoidance strategy using monocular vision to solve this problem. First, we combine Canny and Otsu to extract the barrier feature and find the critical pixel position of obstacles by the monocular vision. Then the image depth estimation algorithm is used to estimate the gaps. With these parameters of barriers, an improved bug algorithm is proposed to avoid the obstacles autonomously. The experiments show that the proposed obstacles avoidance strategy can effectively make a small mobile robot avoid different kinds of obstacles. © Published under licence by IOP Publishing Ltd.

关键词： Mobile robots

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Mixture Loss Function-based Classification Network for Few-shot Learning

Mixture Loss Function-based Classification Network for Few-s...

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Computing, Robotics and System Sciences (ICRSS), International Conference on

作者： Yansha Zhang Feng Pan Jie Wang Lin Wang College of Date Science and Information Engineering GuiZhou Minzu University Guiyang China Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guiyang China

Data augmentation technology can effectively solve the problem of few-shot image classification, but many approaches based on data augmentation generated sample have poor discriminability, which negatively affects the classifier and classification accuracy needs to be improved. In this regard, based on the TFFCN, a mixture loss function-based classification network for few-shot learning (MLFCN) is proposed. First of all, for the prototype tensor features with a simple mean representation, ignoring the different contributions of the sample features of support set for per class to the class prototype tensor features, so that the generated prototype tensor features can not well represent sample features of per class, affecting the discriminability of the generated samples. Introduce the improved adaptively spatial feature fusion module, it uses the convolution, considers the support set of different sample features on the prototype tensor, adaptively assign weights, then weighted and summed to obtain a more representative prototype tensor feature; secondly, aiming at the blurred classification boundary of similar categories in the generated sample features, a mixture loss function is proposed. The loss function is to add the inter-class mean squared error term to the mean square error loss, and stretches the inter-class distance of the generated tensor feature, so that the classification boundary is clearer, so as to train a better classifier. Experimental results show that the improved adaptively spatial feature fusion and the mixing loss function can augment the discriminability of the generated tensor features and further improve the classification accuracy.

关键词： Training Tensors Convolution Prototypes Mean square error methods Feature extraction Generators

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Improved RRT algorithm path planning combined with artificial potential field algorithm 11

Improved RRT algorithm path planning combined with artificia...

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2021 11th International Workshop on Computer Science and Engineering, WCSE 2021

作者： Wang, Xiufen Yang, Shengyi School of Mechanical Electronic Engineering Guizhou Minzu University Guizhou Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guizhou Guiyang550025 China

ISBN: (纸本)9789811817915

In order to solve the problem of low path planning efficiency in complex obstacle environment, an improved Rapidly-exploring Random Trees (RRT) algorithm is proposed. It utilizes artificial potential field to guide the fast expanding random tree to growing towards the target and away from obstacles. Considering the gravitational field of the target node and the repulsion field of the obstacle in the artificial potential field, a certain number of random nodes generated in one iteration are evaluated and selected. The simulation results show that the improved RRT algorithm has strong advantages over the basic RRT algorithm in search ability and computation time. © 2021 11th International Workshop on Computer Science and Engineering, WCSE 2021. All Rights Reserved.

关键词： Motion planning

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Semantic Transformation-Based Data Augmentation for Few-Shot Learning

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SSRN 2023年

作者： Pan, Mei-Hong Xin, Hong-Yi Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China

Few-shot learning (FSL) as a data-scarce method, aims to recognize instances of unseen classes solely based on very few examples. However, the model can easily become overfitted due to the biased distribution formed with extremely limited training data. This paper presents a task specific data augmentation approach by transferring samples from base dataset to the novel tasks in an encoder-decoder paradigm, which guarantees generating semantically meaningful features. Specifically, the feature transfer process is carried out in semantic space. We further impose a compactness constraint to the generated features with the prototypes working as the reference points, which ensures the generated features distribute around the class centers. Moreover, we incorporate the cluster centers of the query set with the prototypes of the support set to reduce the bias of the class centers. With the supervision of the compactness loss, the model is encouraged to generate discriminative features with high inter-class dispersion and intra-class compactness. Extensive experiments show that our method outperforms the state-of-the-arts on four benchmarks, namely MiniImageNet, TieredImageNet, CUB and CIFAR-FS. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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