The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
Sotiris Malassiotis - One of the best experts on this subject based on the ideXlab platform.
-
ICRA - Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
2016 IEEE International Conference on Robotics and Automation (ICRA), 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
-
Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
Proceedings - IEEE International Conference on Robotics and Automation, 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
Christos Kampouris - One of the best experts on this subject based on the ideXlab platform.
-
ICRA - Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
2016 IEEE International Conference on Robotics and Automation (ICRA), 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
-
Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
Proceedings - IEEE International Conference on Robotics and Automation, 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
-
A review of woven Fabric Pattern recognition based on image processing technology
Research journal of textile and apparel, 2016Co-Authors: Rui ZhangAbstract:Purpose The purpose of this paper is introducing the image processing technology used for Fabric analysis, which has the advantages of objective, digital and quick response. Design/methodology/approach This paper briefly describes the key process and module of some typical automatic recognition systems for Fabric analysis presented by previous researchers; the related methods and algorithms used for the texture and Pattern identification are also introduced. Findings Compared with the traditional subjective method, the image processing technology method has been proved to be rapid, accurate and reliable for quality control. Originality/value The future trends and limitations in the field of weave Pattern recognition for woven Fabrics have been summarized at the end of this paper.
-
Pattern Generation Based on Julia Set and its Application in the Design of Seamless Underwear Products
Advanced Materials Research, 2011Co-Authors: Li Chen, Rui ZhangAbstract:In order to improve the design capacity of knitted Fabric Pattern and explore new design thought, this article studies the fractal image generation of Julia set and applies it into the Pattern design of seamless underwear products. It adopts VB programming to generate Julia set Pattern based on escape time algorithm, and then uses part imaging and amplification or changes the function orders to get a lot of colorful Patterns. When these generated Patterns are imported under the help of Photon software, color part in the Patterns will be replaced with pre-designed Fabric Patterns and finally comes with Patterns identifiable to seamless underwear knitting machine. Such method greatly expands the design thoughts of seamless underwear products.
Ioannis Mariolis - One of the best experts on this subject based on the ideXlab platform.
-
ICRA - Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
2016 IEEE International Conference on Robotics and Automation (ICRA), 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
-
Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
Proceedings - IEEE International Conference on Robotics and Automation, 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
Evangelos Skartados - One of the best experts on this subject based on the ideXlab platform.
-
ICRA - Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
2016 IEEE International Conference on Robotics and Automation (ICRA), 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.
-
Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment
Proceedings - IEEE International Conference on Robotics and Automation, 2016Co-Authors: Christos Kampouris, Evangelos Skartados, A. Kargakos, Georgia Peleka, Ioannis Mariolis, Dimitra Triantafyllou, Sotiris MalassiotisAbstract:Perception of garments is a challenging task for robots due to the large variety in shapes, Fabric Patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric stereo sensors that interacts with the garment through a combination of different basic actions. By applying machine learning techniques on the autonomously acquired data of different modalities we recognize the manipulated garment's type, Fabric Pattern, and material. Despite the challenges imposed by the unconstrained environment, promising performances are achieved for the majority of the recognition tasks.