2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)最新文献_第2页

Abnormal Scene Classification using Image Captioning Technique: A Landslide Case Study 基于图像字幕技术的异常场景分类:以滑坡为例

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179000

Narongthat Thanyawet, P. Ratsamee, Yuki Uranishi, H. Takemura

{"title":"Abnormal Scene Classification using Image Captioning Technique: A Landslide Case Study","authors":"Narongthat Thanyawet, P. Ratsamee, Yuki Uranishi, H. Takemura","doi":"10.1109/ICPRS58416.2023.10179000","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179000","url":null,"abstract":"Disaster occurs in various regions, which affects human life and activities. For instance, the one disaster we faced in the mountainous area was a landslide that could occur for various reasons, such as heavy precipitation, failure of slope stability, or earthquake. After this event, humans must evaluate the damage and the damaged area to recover it. Therefore, we must focus on autonomous damage detection to evaluate the damaged region and plan for the decision-maker for recovery. This research discusses a new technique for detecting dam-age in disaster situations, specifically landslides. The technique combines a transformer model for object detection and a vision encoder-decoder for image captioning. By using language to detect abnormal scenes rather than just visual features, the proposed method can accurately predict the extent of damage in the affected region. The results of this study show that the proposed method outperforms the traditional ResNet50 classification method in terms of accuracy, AUC, precision, recall, and F1 score. These findings suggest that the proposed technique could be an effective tool for evaluating the scope of damage after a disaster, which could help the recovery process.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116605266","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 on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches 多模态图像融合环路的应用:基于深度学习方法的发展趋势

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179005

G. Bastidas-Guacho, P. Moreno-Vallejo, B. Vintimilla, A. Sappa

{"title":"Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches","authors":"G. Bastidas-Guacho, P. Moreno-Vallejo, B. Vintimilla, A. Sappa","doi":"10.1109/ICPRS58416.2023.10179005","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179005","url":null,"abstract":"Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. There are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. Then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123515605","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

Melanoma Cancer Classification using Deep Convolutional Neural Networks 使用深度卷积神经网络进行黑色素瘤癌症分类

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179049

José M. Cadena, Noel Pérez, D. Benítez, Felipe Grijalva, Ricardo Flores, Oscar Camacho, Yovani Marrero-Ponce

{"title":"Melanoma Cancer Classification using Deep Convolutional Neural Networks","authors":"José M. Cadena, Noel Pérez, D. Benítez, Felipe Grijalva, Ricardo Flores, Oscar Camacho, Yovani Marrero-Ponce","doi":"10.1109/ICPRS58416.2023.10179049","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179049","url":null,"abstract":"Cancerous melanoma is a relatively rare skin lesion that, if detected, can cause death due to its high mortality rate. The excessive production of melanocytes causes cancerous melanoma in the skin due to high exposure to solar radiation and poor skin care against these conditions. For this reason, we decided to use deep learning models to help detect melanoma without removing skin samples for biopsies. In this work, we proposed a new deep learning model called CNN-2, based on a deep convolutional neural network architecture to successfully classify skin lesions on a data set of 2860 skin lesions taken from the ISIC Archive. The proposed model CNN-2 was trained for 50 epochs, using a three-repeated 10-fold stratified cross-validation scheme. CNN-2 reached an AUC score of 0.915 ± 0.02. Although this model was trained for only 50 epochs, the AUC scored did not represent any statistical differences from other more complex models. Furthermore, the CNN-2 model achieved an AUC score of 0.9626 when used in a test dataset. This CNN-2 model allowed one to distinguish between benign skin lesions and melanoma.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114441691","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

Multi-Channel Gaussian Derivative Neural Networks for Crowd Analysis 多通道高斯导数神经网络用于人群分析

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179046

Hugo Gavilima-Pilataxi, Julio Ibarra-Fiallo

引用次数: 1

Automatic Collection of Transcribed Speech for Low Resources Languages 低资源语言转录语音的自动收集

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179033

Thales Aguiar, M. Da Costa-Abreu

引用次数: 0

HO3-SLAM: Human-Object Occlusion Ordering as Add-on for Enhancing Traversability Prediction in Dynamic SLAM HO3-SLAM:基于人-目标遮挡排序的动态SLAM可穿越性预测增强方法

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179060

Jonathan Tay Yu Liang, Tanaka Kanji

{"title":"HO3-SLAM: Human-Object Occlusion Ordering as Add-on for Enhancing Traversability Prediction in Dynamic SLAM","authors":"Jonathan Tay Yu Liang, Tanaka Kanji","doi":"10.1109/ICPRS58416.2023.10179060","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179060","url":null,"abstract":"Detecting traversable regions from monocular vision is a fundamental problem for indoor robot navigation. In existing works, this is typically formulated as the problem of reconstructing floor regions that are directly visible to the robot. However, the assumption of floor visibility is often violated in crowded scenes such as office environments, which make the existing solutions less effective. To complement existing solutions, we propose a novel approach based on pedestrian observation, i.e., observing the behaviour of pedestrians assuming that the floor region they are standing on is traversable. In our observation, it is recently becoming realistic to implement this new approach by combining state-of-the-art of computer vision techniques, including (1) detection of dynamic objects (e.g., YOLO object detector), (2) absolute localization of stationary objects (e.g., SLAM), and (3) relative localization between dynamic and stationary objects (e.g., Occlusion ordering). A prototype system was implemented by extending state-of-the-art techniques of dynamic SLAM and object detector models, and its basic performance was demonstrated through real-world experiments. We believe that our approach can offer a valuable solution for challenging application domains such as human robot collaboration and long-horizon action planning.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126254428","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

A centered kernel alignment-based strategy for pest evolution tracing: Myiopsitta monachus case 一种基于中心核比对的害虫进化追踪策略:紫斑蝇案例

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179040

Andrea Viazzi, Avril Maciel Rios, J. S. Blandon, J. González

引用次数: 0

In-depth Benchmarking of Transfer Learning Techniques for Improved Bottle Recognition 改进瓶子识别的迁移学习技术的深度基准测试

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10178995

Dominik Bittner, Ricky-Ricardo Hendricks, Luca Horn, J. Mottok

引用次数: 0

Extending the U-Net Architecture for Strokes Segmentation on CT Scan Images 扩展U-Net结构在CT扫描图像笔画分割中的应用

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179074

Iván Guerrón, Noel Pérez, D. Benítez, Felipe Grijalva, Daniel Riofrío, Maria Baldeon-Calisto

{"title":"Extending the U-Net Architecture for Strokes Segmentation on CT Scan Images","authors":"Iván Guerrón, Noel Pérez, D. Benítez, Felipe Grijalva, Daniel Riofrío, Maria Baldeon-Calisto","doi":"10.1109/ICPRS58416.2023.10179074","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179074","url":null,"abstract":"Brain stroke is the second leading cause of death worldwide after heart disease, and one of the most concerning types is intracranial hemorrhage (ICH). This type of bleeding, caused by ruptures of blood vessels within the brain, affects the brain, prevents cell oxygenation, and causes nerve damage. Although recent medical advances have helped many patients, doctors are still subject to human errors when detecting and segmenting intracranial hemorrhages due to long working hours. For this reason, deep learning models have been introduced to help reduce errors in this task. In this regard, we proposed a new deep learning method called D-Unet based on the standard U-net architecture to successfully detect and segment intracranial hemorrhage lesions in a data set of computerized tomography images belonging to 82 patients. Both the D-Unet and the U-net were trained under the same experimental conditions using a ten-fold stratified cross-validation schema repeated three times. The means obtained from the IoU and DICE coefficient scores of $0.68pm 0.08$ and $0.81pm 0.06$ for the D-Unet and $0.59pm 0.10$ and $0.72pm 0.09$ for the U- net, demonstrated that the D-Unet tends to perform better than its baseline method. Furthermore, the best selected D-Unet model in the training stage was validated in an external test set, scoring 0.86 for IoU and 0.89 for DICE. This performance evaluation on the test data set confirmed the quality and generalizability of the model, successfully detecting and segmenting ICH of different types, shapes, sizes, and locations.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132574575","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

Spatiotemporal Variations of $mathbf{PM}_{2.5}$ Concentration and Its Heterogeneous Relationship with Natural and Humanity Factors in Handan of China 邯郸市$mathbf{PM}_{2.5}$浓度时空变化及其与自然、人文因素的异质关系

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10178996

Die Lu, Tanhua Jin, Xiang Zhang, Han Wang, Shifan Han, Yuhui Huang, Jun Cao, Lizhong Gao, Junyan Yang, Qiao Wang

引用次数: 0