Sylvain W. Combettes, Paul Boniol, A. Mazarguil, Danping Wang, Diego Vaquero-Ramos, Marion Chauveau, Laurent Oudre, N. Vayatis, P. Vidal, A. Roren, M. Lefèvre-Colau
This article thoroughly describes a data set of 240 multivariate time series collected using 34 Cartesian Optoelectronic Dynamic Anthropometer (CODA) placed on the upper limb of 16 healthy subjects each undergoing 15 predefined movements such as raising their arms or combing their hair. Each sensor records its position in the 3D space. In total, 2.5 hours of time series are collected. A remarkable aspect of this data set is the extensive availability of metadata: subjects’ characteristics (age, height, etc.) as well as movements’ annotations. Indeed, for each subject and each movement, the start and end time stamps of at least two iterations of the same movement are provided. In addition to the study of human motion, this data set can be used to evaluate generic time series analytical tasks such as multivariate time series segmentation, clustering, or classification.
{"title":"Arm-CODA: A Data Set of Upper-limb Human Movement During Routine Examination","authors":"Sylvain W. Combettes, Paul Boniol, A. Mazarguil, Danping Wang, Diego Vaquero-Ramos, Marion Chauveau, Laurent Oudre, N. Vayatis, P. Vidal, A. Roren, M. Lefèvre-Colau","doi":"10.5201/ipol.2024.494","DOIUrl":"https://doi.org/10.5201/ipol.2024.494","url":null,"abstract":"This article thoroughly describes a data set of 240 multivariate time series collected using 34 Cartesian Optoelectronic Dynamic Anthropometer (CODA) placed on the upper limb of 16 healthy subjects each undergoing 15 predefined movements such as raising their arms or combing their hair. Each sensor records its position in the 3D space. In total, 2.5 hours of time series are collected. A remarkable aspect of this data set is the extensive availability of metadata: subjects’ characteristics (age, height, etc.) as well as movements’ annotations. Indeed, for each subject and each movement, the start and end time stamps of at least two iterations of the same movement are provided. In addition to the study of human motion, this data set can be used to evaluate generic time series analytical tasks such as multivariate time series segmentation, clustering, or classification.","PeriodicalId":54190,"journal":{"name":"Image Processing On Line","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614261","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}
{"title":"Implementation of Image Denoising based on Backward Stochastic Differential Equations","authors":"Dariusz Borkowski","doi":"10.5201/ipol.2023.467","DOIUrl":"https://doi.org/10.5201/ipol.2023.467","url":null,"abstract":"","PeriodicalId":54190,"journal":{"name":"Image Processing On Line","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585928","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}
Cyril Voisard, Nicolas de l’Escalopier, A. Moreau, A. Vienne-Jumeau, D. Ricard, Laurent Oudre
{"title":"A Reference Data Set for the Study of Healthy Subject Gait with Inertial Measurements Units","authors":"Cyril Voisard, Nicolas de l’Escalopier, A. Moreau, A. Vienne-Jumeau, D. Ricard, Laurent Oudre","doi":"10.5201/ipol.2023.497","DOIUrl":"https://doi.org/10.5201/ipol.2023.497","url":null,"abstract":"","PeriodicalId":54190,"journal":{"name":"Image Processing On Line","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138588148","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}
Yanhao Li, Marina Gardella, Quentin Bammey, Tina Nikoukhah, Rafael Grompone von Gioi, Miguel Colom, Jean-Michel Morel
We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then the Ponomarenko et al. method is extended to video by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on a real dataset of drone videos show its performance for different parameter settings and different noise levels. Two extensions of the proposed method using subpixel matching and for multiscale noise estimation are respectively analyzed.
{"title":"A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression","authors":"Yanhao Li, Marina Gardella, Quentin Bammey, Tina Nikoukhah, Rafael Grompone von Gioi, Miguel Colom, Jean-Michel Morel","doi":"10.5201/ipol.2023.420","DOIUrl":"https://doi.org/10.5201/ipol.2023.420","url":null,"abstract":"We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then the Ponomarenko et al. method is extended to video by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on a real dataset of drone videos show its performance for different parameter settings and different noise levels. Two extensions of the proposed method using subpixel matching and for multiscale noise estimation are respectively analyzed.","PeriodicalId":54190,"journal":{"name":"Image Processing On Line","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241451","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}
Bertrand Kerautret, Phuc Ngo, Nicolas Passat, Hugues Talbot, Clara Jaquet
In this article, we focus on the algorithm called CCO (Constrained Constructive Optimization), initially proposed by Schreiner and Buxbaum [Computer-Optimization of Vascular Trees, IEEE Transactions on Biomedical Engineering, 40, 1993] and further extended by Karch et al. [A Three-Dimensional Model for Arterial Tree Representation, Generated by Constrained Constructive Optimization, Computers in Biology and Medicine, 29, 1999]. This algorithm can be considered as one of the gold standards for vascular tree structure generation. Modeling and/or simulating the morphology of vascular networks is a challenging but crucial task that can have a strong impact on different applications such as fluid simulation or learning processes related to image segmentation. Various implementations of CCO were proposed over the last years. However, to the best of our knowledge, there does not exist any open-source version that faithfully follows the native CCO algorithm. Our purpose is to propose such an implementation both in 2D and 3D.
{"title":"OpenCCO: An Implementation of Constrained Constructive Optimization for Generating 2D and 3D Vascular Trees","authors":"Bertrand Kerautret, Phuc Ngo, Nicolas Passat, Hugues Talbot, Clara Jaquet","doi":"10.5201/ipol.2023.477","DOIUrl":"https://doi.org/10.5201/ipol.2023.477","url":null,"abstract":"In this article, we focus on the algorithm called CCO (Constrained Constructive Optimization), initially proposed by Schreiner and Buxbaum [Computer-Optimization of Vascular Trees, IEEE Transactions on Biomedical Engineering, 40, 1993] and further extended by Karch et al. [A Three-Dimensional Model for Arterial Tree Representation, Generated by Constrained Constructive Optimization, Computers in Biology and Medicine, 29, 1999]. This algorithm can be considered as one of the gold standards for vascular tree structure generation. Modeling and/or simulating the morphology of vascular networks is a challenging but crucial task that can have a strong impact on different applications such as fluid simulation or learning processes related to image segmentation. Various implementations of CCO were proposed over the last years. However, to the best of our knowledge, there does not exist any open-source version that faithfully follows the native CCO algorithm. Our purpose is to propose such an implementation both in 2D and 3D.","PeriodicalId":54190,"journal":{"name":"Image Processing On Line","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903024","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}
In this paper we review the evolution of deep architectures for semantic segmentation. The (cid:28)rst successful model was fully convolutional network (FCN) published in CVPR in 2015. Since then, the subject has become very popular and many methods have been published, mainly proposing improvements of FCN. We describe in detail the Pyramid Scene Parsing Network (PSPnet) and DeepLabV3, in addition to FCN, which provide a multi-scale description and increase the resolution of segmentation. In recent years, convolutional architectures have reached a bottleneck and have been surpassed by transformers from natural language processing (NLP), even though these models are generally larger and slower. We have chosen to discuss about the Segmentation Transformer (SETR), a (cid:28)rst architecture with a transformer backbone. We also discuss SegFormer, that includes a multi-scale interpretation and tricks to decrease the size and inference time of the network. The networks presented in the demo come from the MM-Segmentation library, an open source semantic segmentation toolbox based on PyTorch. We propose to compare these methods qualitatively
{"title":"Semantic Segmentation: A Zoology of Deep Architectures","authors":"Aitor Artola","doi":"10.5201/ipol.2023.447","DOIUrl":"https://doi.org/10.5201/ipol.2023.447","url":null,"abstract":"In this paper we review the evolution of deep architectures for semantic segmentation. The (cid:28)rst successful model was fully convolutional network (FCN) published in CVPR in 2015. Since then, the subject has become very popular and many methods have been published, mainly proposing improvements of FCN. We describe in detail the Pyramid Scene Parsing Network (PSPnet) and DeepLabV3, in addition to FCN, which provide a multi-scale description and increase the resolution of segmentation. In recent years, convolutional architectures have reached a bottleneck and have been surpassed by transformers from natural language processing (NLP), even though these models are generally larger and slower. We have chosen to discuss about the Segmentation Transformer (SETR), a (cid:28)rst architecture with a transformer backbone. We also discuss SegFormer, that includes a multi-scale interpretation and tricks to decrease the size and inference time of the network. The networks presented in the demo come from the MM-Segmentation library, an open source semantic segmentation toolbox based on PyTorch. We propose to compare these methods qualitatively","PeriodicalId":54190,"journal":{"name":"Image Processing On Line","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70650058","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}
Given a digital curve and a maximum error, we propose an algorithm that computes a simplification of the curve such that the Frechet distance between the original and the simplified curve is less than the error. The algorithm uses an approximation of the Frechet distance, but a guarantee over the quality of the simplification is proved. Moreover, even if the theoretical complexity of the algorithm is in O(n log(n)), experiments show a linear behaviour in practice.
{"title":"A Near-Linear Time Guaranteed Algorithm for Digital Curve Simplification Under the Fréchet Distance","authors":"Isabelle Sivignon","doi":"10.5201/ipol.2014.70","DOIUrl":"https://doi.org/10.5201/ipol.2014.70","url":null,"abstract":"Given a digital curve and a maximum error, we propose an algorithm that computes a simplification of the curve such that the Frechet distance between the original and the simplified curve is less than the error. The algorithm uses an approximation of the Frechet distance, but a guarantee over the quality of the simplification is proved. Moreover, even if the theoretical complexity of the algorithm is in O(n log(n)), experiments show a linear behaviour in practice.","PeriodicalId":54190,"journal":{"name":"Image Processing On Line","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2011-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78452367","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}
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