The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Nikos Chatzigrigoriou - One of the best experts on this subject based on the ideXlab platform.
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BIC Shavers - Processing of Plastics
internal, 2017Co-Authors: Nikos ChatzigrigoriouAbstract:different injection gates or the same), the various Multi Component Injection Molding variations are categorized in two main families: 1."Two shot" processes (two … plastic parts are manufactured by a "two shot" processes, by transfering the 1st component. Usually, the rotary table technology is used for the manufacturing
Panagiotis Giannopoulos - One of the best experts on this subject based on the ideXlab platform.
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Gillette Skinguard Materials Analysis
internal, 2019Co-Authors: Panagiotis GiannopoulosAbstract:Polypropylene as the hard plastic substrate and TPE (SEBS based compound with high EPDM content) for the soft component. The logo part is also made using a two shot process
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Fusion5 Materials Analysis
internal, 2018Co-Authors: Panagiotis GiannopoulosAbstract:plastic substrate and TPE (SEBS based compound) for the soft component. The logo part is also made using a two shot process, this time combined with IML (In Mould
Hailin Jin - One of the best experts on this subject based on the ideXlab platform.
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LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: John Collomosse, Tu Bui, Hailin JinAbstract:LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-Shot Process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.
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CVPR - LiveSketch: Query Perturbations for Guided Sketch-Based Visual Search
2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Co-Authors: John Collomosse, Tu Bui, Hailin JinAbstract:LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-Shot Process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.
John Collomosse - One of the best experts on this subject based on the ideXlab platform.
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LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: John Collomosse, Tu Bui, Hailin JinAbstract:LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-Shot Process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.
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CVPR - LiveSketch: Query Perturbations for Guided Sketch-Based Visual Search
2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Co-Authors: John Collomosse, Tu Bui, Hailin JinAbstract:LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-Shot Process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.
Tu Bui - One of the best experts on this subject based on the ideXlab platform.
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LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: John Collomosse, Tu Bui, Hailin JinAbstract:LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-Shot Process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.
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CVPR - LiveSketch: Query Perturbations for Guided Sketch-Based Visual Search
2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Co-Authors: John Collomosse, Tu Bui, Hailin JinAbstract:LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-Shot Process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.