2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)最新文献_第3页

Rethinking Bipartite Graph Matching in Realtime Multi-object Tracking 对实时多目标跟踪中的二部图匹配的再思考

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00124

Z. Zou, Jie Hao, L. Shu

{"title":"Rethinking Bipartite Graph Matching in Realtime Multi-object Tracking","authors":"Z. Zou, Jie Hao, L. Shu","doi":"10.1109/CACML55074.2022.00124","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00124","url":null,"abstract":"Data association is a crucial part for tracking-by-detection framework. Although many works about constructing the matching cost between trajectories and detections have been proposed in the community, few researchers pay attention to how to improve the efficiency of bipartite graph matching in realtime multi-object tracking. In this paper, we start with the optimal solution of integer linear programming, explore the best application of bipartite graph matching in tracking task and evaluate the rationality of cost matrix simultaneously. Frist, we analyze the defects of bipartite graph matching process in some multi-object tracking methods, and establish a criteria of similarity measure between trajectories and detections. Then we design two weight matrices for multi-object tracking by applying our criteria. Besides, a novel tracking process is proposed to handle visual-information-free scenario. Our method improves the accuracy of the graph-matching-based approach at very fast running speed (3000+ FPS). Comprehensive experiments performed on MOT benchmarks demonstrate that the proposed approach achieves the state-of-the-art performance in methods without visual information. Moreover, the efficient matching process can also be assembled on approaches with appearance information to replace cascade matching.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fast Detection Method for Infrared Small Targets in Complex Sea and Sky Background 复杂海天背景下红外小目标的快速检测方法

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00016

Z. Gao, Honghao Chang, Xiang-zheng Cheng, W. Liu, Xiaotian Wang, Wang Ying

{"title":"A Fast Detection Method for Infrared Small Targets in Complex Sea and Sky Background","authors":"Z. Gao, Honghao Chang, Xiang-zheng Cheng, W. Liu, Xiaotian Wang, Wang Ying","doi":"10.1109/CACML55074.2022.00016","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00016","url":null,"abstract":"The traditional infrared small target detection method is easy to cause false alarms in the complex sea and sky background. In this letter, a fast detection method for infrared small targets in the complex sea and sky background is proposed. Firstly, in order to make full use of the Gaussian characteristics of infrared small targets and effectively distinguish the target from the background clutter, the SODD (second order directional derivative) filter is used to improve the target signal-to-clutter ratio (SCR) and suppress the background clutter; Secondly, in order to improve the real-time performance of the algorithm, we improve the existing MPCM algorithm by reducing the difference dimension between the target area and the local background area; Finally, we use pipeline filtering that can take full advantage of candidate target location, size, and total grayscale 3D information to complete multi-frame target validation and achieve continuous target detection in image sequences. The experimental results show that the proposed algorithm can stably and accurately detect infrared small targets in the infrared image sequence.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"1 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113971365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Comprehensive Experiment and Design of Spectrum Refinement for Digital Signal Processing Under the Background of New Engineering 新工程背景下数字信号处理频谱细化综合实验与设计

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00049

Shaoming Wei, Ying-Jen Lin, Xueyin Geng, Jun Wang

引用次数: 0

Joint Event Extraction Based on CNN-BiGRU and Attention Mechanism 基于CNN-BiGRU和注意力机制的联合事件提取

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00090

Chao Shen, J. Tao, Peng Li, Zhao Lv, Guohua Yang

{"title":"Joint Event Extraction Based on CNN-BiGRU and Attention Mechanism","authors":"Chao Shen, J. Tao, Peng Li, Zhao Lv, Guohua Yang","doi":"10.1109/CACML55074.2022.00090","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00090","url":null,"abstract":"Biomedical event extraction is a very challenging task of information extraction, which plays a key role in medical research, disease analysis and other applications. At present, the task of biomedical event extraction mainly consists of two steps: trigger identification and argument classification. Most research methods use a pipelining approach to accomplish two sub-tasks in stages, which leads to cascading errors. Therefore, a joint event extraction method based on CNN-BiGRU and attention mechanism is proposed, which can extract deeper and more comprehensive features more effectively to complete the task. Firstly, the word vector representation obtained by pretraining language model is combined with part-of-speech vector and position vector. Then input them into Convolutional Neural Network (CNN) and Bi-directional Gated Recurrent Unit (BiGRU) respectively to obtain the local and global feature representations of sentences. Finally, the attention mechanism is used to integrate these two feature representations and jointly deal with these two subtasks. Experiments on MLEE data sets show that the proposed method is superior to the previously proposed biological event extraction method and can effectively extract biomedical events.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124180131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of VIN Image Recognition Technology Based on LabVIEW IMAQ in Road Transport Vehicle Registration 基于LabVIEW IMAQ的VIN图像识别技术在道路运输车辆登记中的应用

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00035

Yucheng Du

引用次数: 0

Dust Image Enhancement Algorithm Based on Feature Transformation 基于特征变换的粉尘图像增强算法

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00064

Lingjun Chen, Caidan Zhao, Yilin Wang, Xiangyu Huang

{"title":"Dust Image Enhancement Algorithm Based on Feature Transformation","authors":"Lingjun Chen, Caidan Zhao, Yilin Wang, Xiangyu Huang","doi":"10.1109/CACML55074.2022.00064","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00064","url":null,"abstract":"In recent years, image enhancement work has been well developed to improve the quality of images, which is beneficial to subsequent task such as object detection, license plate recognition and anomaly detection. But the selection of methods to solve the problems in different scenes is still the main work of image enhancement. In the dust weather environment, due to the absorption and scattering of light by the dust particles suspended in the air, the image obtained by the image acquisition device has the characteristics of yellowish reddish and blurred image, which seriously affects the visual perception of the human eye. And the lack of datasets for dust image enhancement further increases the difficulty of the related task. Thus, based on the color and contour features of the real captured dust images, this paper proposes a synthetic dust image dataset for training deep learning networks. Also, based on the feature transformation idea of Recurrent Generative Adversarial Networks (CycleGAN), we presents a dust image enhancement algorithm which uses an end - to-end deep learning network and avoids the dependence on physical imaging models. Comparison of state-of-the-art approaches available in the literature, our proposed approach obtains better subjective and objective evaluation results on the test set of our proposed database.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of License Plate Recognition System Based on Image Processing 基于图像处理的车牌识别系统设计

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00060

Lei Wang, Kunqin Li

{"title":"Design of License Plate Recognition System Based on Image Processing","authors":"Lei Wang, Kunqin Li","doi":"10.1109/CACML55074.2022.00060","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00060","url":null,"abstract":"With the rapid development of road transportation, the method of relying on manpower for car management has fallen behind. The road traffic management of China has gradually developed towards the direction of Internet. The core of road traffic management is the license plate recognition system, and the license plate automatic recognition system is used in many occasions, such as bridge crossing charges, parking lot unmanaged, illegal vehicle automatic recording and so on. License plate recognition system makes traffic management more convenient and reduces human resources. However, in the process of license plate recognition, external factors are uncontrollable. Weather, sunlight and other external factors will affect the license plate recognition. The defects of the license plate itself, stains and so on will have a greater impact on the license plate recognition. License plate recognition system is based on the appearance and characteristics of license plate, through a series of algorithms to achieve the purpose of recognizing license plate characters. The research focus of this paper mainly includes blue area positioning algorithm, character segmentation algorithm, character recognition algorithm and template matching, and so on. Through the image processing and analysis of license plate photos, the recognition of license plate characters in the image is finally realized. By using the system designed in this paper to process and count the license plate information, it can be concluded that the recognition rate of the designed license plate recognition system can reach more than 92.9%, which meets the design requirement.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128756164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Auditory Measure for Anomaly Detection based on Auto-encoders 一种基于自编码器的异常检测听觉方法

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00026

Tao Liu, Meiqian Duan, Luyang Sun, Bo Zhang

{"title":"An Auditory Measure for Anomaly Detection based on Auto-encoders","authors":"Tao Liu, Meiqian Duan, Luyang Sun, Bo Zhang","doi":"10.1109/CACML55074.2022.00026","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00026","url":null,"abstract":"We aim at detecting anomalies of several hydro-turbines and electric generators in a power plant based on their auditory signals. Auto-encoders implemented with artificial neural networks are used for this task. For each device, an auto-encoder is trained to describe the audio properties of normal signals of the device. For inference, conventionally, residual spectra between input and prediction produced by auto-encoders are used for anomalies detection. The frame energies of the residual spectra are used for such detection; higher energies are used as indications of presence of anomalies. This approach does not fit well with the industry environment of this work. Audio signals of the devices have quite large variances. Frame energies of the residual spectra are influenced by those variances dramatically, making the conventional approach unable to make robust detection. To deal with this problem, we propose a measure called Peaks-to-Noise Ratio(PNR) to estimate the auditory energies(instead of the physical energies) of residual spectra to determine confidences of anomaly occurrences. Experiments showed that this measure is more robust than conventional ones against the energy variances of the residuals.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128338300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Recommendation algorithm for agricultural products based on attention factor decomposer and knowledge graph 基于关注因子分解和知识图谱的农产品推荐算法

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00110

Honghui Xie, Jun Yang, Conggang Huang, Zhen Wang, Yi Liu

{"title":"Recommendation algorithm for agricultural products based on attention factor decomposer and knowledge graph","authors":"Honghui Xie, Jun Yang, Conggang Huang, Zhen Wang, Yi Liu","doi":"10.1109/CACML55074.2022.00110","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00110","url":null,"abstract":"To alleviate the distress of data sparsity and cold start in agricultural products e-commerce platforms, this paper proposes an agricultural products recommendation algorithm based on the combination of attention factor decomposer and knowledge graph. The algorithm constructs a knowledge graph for the produce dataset, models the higher-order connectivity of the produce knowledge graph in an end-to-end manner under the space of the knowledge graph, recursively propagates embeddings from the neighbors of the nodes, and extracts the potential feature vectors of the produce by using the attention factor decomposer as the message aggregation of the neighboring nodes. Using MLP, the agricultural product feature vectors and user embedding vectors are integrated and sent to the prediction module, and user click-through rate prediction is obtained by vector inner product operation. Experimenting on an agricultural e-commerce dataset, the ACC and AUC are improved by 1.60% and 1.14%, respectively, compared with the optimal baseline model KGCN. Thus, it verifies the effectiveness as well as feasibility of the improved algorithm on agricultural products data, which can provide a new idea and method for agricultural products e-commerce platform.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134223304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Label-Aware Recurrent Reading for Multi-Label Classification 多标签分类的标签感知循环阅读

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00091

Sheng Ming, Huajun Liu, Ziming Luo, Peng Huang, Mark Junjie Li

{"title":"Label-Aware Recurrent Reading for Multi-Label Classification","authors":"Sheng Ming, Huajun Liu, Ziming Luo, Peng Huang, Mark Junjie Li","doi":"10.1109/CACML55074.2022.00091","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00091","url":null,"abstract":"Multi-label classification (MLC) is an essential branch of natural language processing where a given instance may be associated with multiple labels. Recently, neural network approaches invested considerable dependency between labels and the instance, achieving state-of-the-art performance. However, the existing methods ignore the hidden correlations between each document's semantic information and labels. In this paper, inspired by the cognitive process of human reading, we propose a Label-Aware Recurrent Reading (LARD) network based on neuroscience. LARD modeled the MLC problem as a decision-making process of recurrent reading and constructs label-aware document representation according to the top-down mechanism of neuroscience. The model outputs the prediction of all labels after each reading, and in the process of recurrent reading, the prediction accuracy is improved. Besides, the attention mechanism is applied to make the weight of words dynamically adjust according to the topdown classification prediction information, taking into account the different contributions of words to labels. Experiments show that our model has better performance than the existing models.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122366049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0