Journal of WSCG最新文献

Bias mitigation techniques in Image Classification: Fair Machine Learning in Human Heritage Collections 图像分类中的偏见缓解技术:人类遗产收藏中的公平机器学习

Journal of WSCG Pub Date : 2023-07-01 DOI: 10.24132/jwscg.2023.6

Dalia Ortiz Pablo, Sushruth Badri, Erik Norén, Christoph Nötzli

{"title":"Bias mitigation techniques in Image Classification: Fair Machine Learning in Human Heritage Collections","authors":"Dalia Ortiz Pablo, Sushruth Badri, Erik Norén, Christoph Nötzli","doi":"10.24132/jwscg.2023.6","DOIUrl":"https://doi.org/10.24132/jwscg.2023.6","url":null,"abstract":"A major problem with using automated classification systems is that if they are not engineered correctly and with fairness considerations, they could be detrimental to certain populations. Furthermore, while engineers have developed cutting-edge technologies for image classification, there is still a gap in the application of these models in human heritage collections, where data sets usually consist of low-quality pictures of people with diverse ethnicity, gender, and age. In this work, we evaluate three bias mitigation techniques using two state-of-the-art neural networks, Xception and EfficientNet, for gender classification. Moreover, we explore the use of transfer learning using a fair data set to overcome the training data scarcity. We evaluated the effectiveness of the bias mitigation pipeline on a cultural heritage collection of photographs from the 19th and 20th centuries, and we used the FairFace data set for the transfer learning experiments. After the evaluation, we found that transfer learning is a good technique that allows better performance when working with a small data set. Moreover, the fairest classifier was found to be accomplished using transfer learning, threshold change, re-weighting and image augmentation as bias mitigation methods.","PeriodicalId":39283,"journal":{"name":"Journal of WSCG","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135509301","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

Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data 基于未标记视频数据的模式独居物种自动个体识别

Journal of WSCG Pub Date : 2023-07-01 DOI: 10.24132/jwscg.2023.1

Vanessa Suessle, Mimi Arandjelovic, Ammie K. Kalan, Anthony Agbor, Christophe Boesch, Gregory Brazzola, Tobias Deschner, Paula Dieguez, Anne-Céline Granjon, Hjalmar Kuehl, Anja Landsmann, Juan Lapuente, Nuria Maldonado, Amelia Meier, Zuzana Rockaiova, Erin G. Wessling, Roman M. Wittig, Colleen T. Downs, Andreas Weinmann, Elke Hergenroether

{"title":"Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data","authors":"Vanessa Suessle, Mimi Arandjelovic, Ammie K. Kalan, Anthony Agbor, Christophe Boesch, Gregory Brazzola, Tobias Deschner, Paula Dieguez, Anne-Céline Granjon, Hjalmar Kuehl, Anja Landsmann, Juan Lapuente, Nuria Maldonado, Amelia Meier, Zuzana Rockaiova, Erin G. Wessling, Roman M. Wittig, Colleen T. Downs, Andreas Weinmann, Elke Hergenroether","doi":"10.24132/jwscg.2023.1","DOIUrl":"https://doi.org/10.24132/jwscg.2023.1","url":null,"abstract":"The manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals. In this study, we developed a pipeline to automatically analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus). We assumed that the same individual was seen throughout one triggered video sequence. With this assumption, multiple images could be assigned to an individual for the initial database filling without pre-labeling. The pipeline was based on well-established components from computer vision and deep learning, particularly convolutional neural networks (CNNs) and scale-invariant feature transform (SIFT) features. We augmented this basis by implementing additional components to substitute otherwise required human interactions. Based on the similarity between frames from the video material, clusters were formed that represented individuals bypassing the open set problem of the unknown total population. The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a success rate of over 83% for correct matches between previously unknown individuals. The proposed pipeline can become a valuable tool for future conservation projects based on camera trap data, reducing the work of manual analysis for individual identification, when labeled data is unavailable.","PeriodicalId":39283,"journal":{"name":"Journal of WSCG","volume":"615 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135509293","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

New Methods and Novel Framework for HypersurfaceCurvature Determination and Analysis 超曲面曲率测定与分析的新方法与新框架

Journal of WSCG Pub Date : 2021-01-01 DOI: 10.24132/jwscg.2021.29.2

Jacob D. Hauenstein, Timothy S Newman

引用次数: 0

Drone interactions within the field of Augmented Reality 无人机在增强现实领域的互动

Journal of WSCG Pub Date : 2021-01-01 DOI: 10.24132/jwscg.2021.29.1

Damia Fuentes Escote, S. Semwal

引用次数: 0

Interactive Individualized Neuroanatomy Labeling for Neuroanatomy Teaching 神经解剖学教学中的交互式个性化神经解剖学标签

Journal of WSCG Pub Date : 2021-01-01 DOI: 10.24132/jwscg.2021.29.4

Felippe T. Angelo, R. Voltoline, G. Gonçalves, Shin-Ting Wu

{"title":"Interactive Individualized Neuroanatomy Labeling for Neuroanatomy Teaching","authors":"Felippe T. Angelo, R. Voltoline, G. Gonçalves, Shin-Ting Wu","doi":"10.24132/jwscg.2021.29.4","DOIUrl":"https://doi.org/10.24132/jwscg.2021.29.4","url":null,"abstract":"As the imaging technology and the understanding of neurological disease improve, a solid understanding of neu-roanatomy has become increasingly relevant. Neuroanatomy teaching includes the practice of cadaveric dissectionand neuroanatomy atlases consisting of images of a brain with its labeled structures. However, the natural inter-individual neuroanatomical variability cannot be taken into account. This work addresses the individual grossneuroanatomy atlas that could enrich medical students’ experiences with various individual variations in anatomi-cal landmarks and their spatial relationships. We propose to deform the CerebrA cortical atlas into the individualanatomical magnetic resonance imaging data to increase students’ opportunity to contact normal neuroanatomicalvariations in the early stages of studies. Besides, we include interactive queries on the labels/names of neu-roanatomical structures from an individual neuroanatomical atlas in a 3D space. An implementation on top ofSimpleITK library and VMTK-Neuro software is presented. We generated a series of surface and internal neu-roanatomy maps from 16 test volumes to attest to the potential of the proposed technique in brain labeling. Forthe age group between 10 to 75, there is evidence that the superficial cortical labeling is accurate with the visualassessment of the degree of concordance between the neuroanatomical and label boundaries.","PeriodicalId":39283,"journal":{"name":"Journal of WSCG","volume":"102 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78980889","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 Dynamic Non-Manifold Mesh Data Structure to Represent Biological Materials. 生物材料的动态非流形网格数据结构。

Journal of WSCG Pub Date : 2018-01-01 DOI: 10.24132/JWSCG.2018.26.1.3

Endre Somogyi

引用次数: 2