New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings最新文献

Decision-Support System for Safety and Security Assessment and Management in Smart Cities 智慧城市安全与安全评估与管理决策支持系统

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2023-06-23 DOI: 10.1007/978-3-031-13324-4_3

Javier González-Villa, A. Cuesta, M. Spagnolo, Marisa Zanotti, Luke Summers, Alexander Elms, Anay Dhaya, K. Jedlička, J. Martolos, D. Cetinkaya

引用次数: 0

Effect of Gender, Pose and Camera Distance on Human Body Dimensions Estimation 性别、姿势和相机距离对人体尺寸估计的影响

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-05-24 DOI: 10.48550/arXiv.2205.12028

Yansel G'onzalez Tejeda, H. A. Mayer

{"title":"Effect of Gender, Pose and Camera Distance on Human Body Dimensions Estimation","authors":"Yansel G'onzalez Tejeda, H. A. Mayer","doi":"10.48550/arXiv.2205.12028","DOIUrl":"https://doi.org/10.48550/arXiv.2205.12028","url":null,"abstract":"Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multi-task regression problem that can be tackled with machine learning techniques, particularly convolutional neural networks (CNN). Despite the community's tremendous effort to advance human shape analysis, there is a lack of systematic experiments to assess CNNs estimation of human body dimensions from images. Our contribution lies in assessing a CNN estimation performance in a series of controlled experiments. To that end, we augment our recently published neural anthropometer dataset by rendering images with different camera distance. We evaluate the network inference absolute and relative mean error between the estimated and actual HBDs. We train and evaluate the CNN in four scenarios: (1) training with subjects of a specific gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera distance. Not only our experiments demonstrate that the network can perform the task successfully, but also reveal a number of relevant facts that contribute to better understand the task of HBDE.","PeriodicalId":91934,"journal":{"name":"New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings","volume":"38 1","pages":"179-190"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76320716","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

Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification 在几分钟内标记牛群:个体荷斯坦-弗里西亚牛识别

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-04-22 DOI: 10.48550/arXiv.2204.10905

Jing Gao, T. Burghardt, N. Campbell

{"title":"Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification","authors":"Jing Gao, T. Burghardt, N. Campbell","doi":"10.48550/arXiv.2204.10905","DOIUrl":"https://doi.org/10.48550/arXiv.2204.10905","url":null,"abstract":". We describe a practically evaluated approach for training visual cattle ID systems for a whole farm requiring only ten minutes of labelling effort. In particular, for the task of automatic identiﬁcation of individual Holstein-Friesians in real-world farm CCTV, we show that self-supervision, metric learning, cluster analysis, and active learning can complement each other to signiﬁcantly reduce the annotation requirements usually needed to train cattle identiﬁcation frameworks. Evaluating the approach on the test portion of the publicly available Cows2021 dataset, for training we use 23,350 frames across 435 single individual tracklets generated by automated oriented cattle detection and tracking in operational farm footage. Self-supervised metric learning is ﬁrst employed to initialise a candidate identity space where each tracklet is considered a distinct entity. Grouping entities into equivalence classes representing cattle identities is then performed by automated merging via cluster analysis and active learning. Critically, we identify the inﬂection point at which automated choices cannot replicate improvements based on human intervention to reduce annotation to a minimum. Experimental results show that cluster analysis and a few minutes of labelling after automated self-supervision can improve the test identiﬁcation accuracy of 153 identities to 92.44% (ARI=0.93) from the 74.9% (ARI=0.754) ob-tained by self-supervision only. These promising results indicate that a tailored combination of human and machine reasoning in visual cattle ID pipelines can be highly effective whilst requiring only minimal labelling effort. We provide all key source code and network weights with this paper for easy result reproduction.","PeriodicalId":91934,"journal":{"name":"New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings","volume":"40 1","pages":"384-396"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73810041","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: 2

A Mobile Food Recognition System for Dietary Assessment 用于膳食评估的移动食品识别系统

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-04-20 DOI: 10.48550/arXiv.2204.09432

Seymanur Akti, M. Qaraqe, H. K. Ekenel

{"title":"A Mobile Food Recognition System for Dietary Assessment","authors":"Seymanur Akti, M. Qaraqe, H. K. Ekenel","doi":"10.48550/arXiv.2204.09432","DOIUrl":"https://doi.org/10.48550/arXiv.2204.09432","url":null,"abstract":"Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. Therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.","PeriodicalId":91934,"journal":{"name":"New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings","volume":"8 1","pages":"71-81"},"PeriodicalIF":0.0,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89365788","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: 4

Practical Recommendations for Replay-based Continual Learning Methods 基于重播的持续学习方法的实用建议

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-03-19 DOI: 10.1007/978-3-031-13324-4_47

Gabriele Merlin, Vincenzo Lomonaco, Andrea Cossu, Antonio Carta, D. Bacciu

引用次数: 12

MONstEr: A Deep Learning-Based System for the Automatic Generation of Gaming Assets MONstEr:基于深度学习的自动生成游戏资产系统

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13321-3_25

Michele Brocchini, M. Mameli, E. Balloni, Laura Della Sciucca, L. Rossi, M. Paolanti, E. Frontoni, P. Zingaretti

引用次数: 0

Deep Regression by Feature Regularization for COVID-19 Severity Prediction 基于特征正则化的深度回归新冠肺炎严重程度预测

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13324-4_42

Davide Tricarico, Hafiza Ayesha Hoor Chaudhry, A. Fiandrotti, Marco Grangetto

引用次数: 2

Suggestions for the Environmental Sustainability from Precision Livestock Farming and Replacement in Dairy Cows 精准养殖对环境可持续性的建议及奶牛替代

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13324-4_30

D. Lovarelli, D. Berckmans, J. Bacenetti, M. Guarino

引用次数: 0

Virtual and Augmented Reality for Quality Control of Aircraft Interiors 虚拟和增强现实技术在飞机内饰质量控制中的应用

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13321-3_20

N. Mosca, Gaetano Pernisco, Maria di Summa, V. Renó, M. Nitti, E. Stella

引用次数: 1

The Morra Game: Developing an Automatic Gesture Recognition System to Interface Human and Artificial Players Morra游戏:开发一种人机交互的自动手势识别系统

New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-13324-4_21

F. Delogu, Francesco De Bartolomeo, Sergio Solinas, Carla Meloni, B. Mercante, Paolo Enrico, Rachele Fanari, Antonello Zizi

引用次数: 0