CONVOLUTIONAL NEURAL NETWORKS, PARTICULARLY FOR IMAGE ANALYSIS
A method in which a convolutional neural network is configured to receive an input data structure including a group of values corresponding to signal samples and to generate a corresponding classification output indicative of a selected one among plural predefined classes. The convolutional neural n...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Patent |
| Sprache: | eng ; fre ; ger |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | FRANCINI, Gianluca LEPSØY, Skjalg PORTO BUARQUE DE GUSMÃO, Pedro |
| description | A method in which a convolutional neural network is configured to receive an input data structure including a group of values corresponding to signal samples and to generate a corresponding classification output indicative of a selected one among plural predefined classes. The convolutional neural network includes an ordered sequence of layers, each configured to receive a corresponding layer input data structure including a group of input values, and generate a corresponding layer output data structure including a group of output values by convolving the layer input data structure with at least one corresponding filter including a corresponding group of weights. The layer input data structure of the first layer of the sequence corresponds to the input data structure. The layer input data structure of a generic layer of the sequence different from the first layer corresponds to the layer output data structure generated by a previous layer in the sequence. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_EP3427195B1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>EP3427195B1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_EP3427195B13</originalsourceid><addsrcrecordid>eNrjZLBz9vcL8_cJDfH093P0UfBzDQ0CUyHh_kHewToKAY5BIZ7OoT6OQT6RCm7-QQqevo7urgqOQMWRwZ7BPAysaYk5xam8UJqbQcHNNcTZQze1ID8-tbggMTk1L7Uk3jXA2MTI3NDS1MnQmAglAEQYKjQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>CONVOLUTIONAL NEURAL NETWORKS, PARTICULARLY FOR IMAGE ANALYSIS</title><source>esp@cenet</source><creator>FRANCINI, Gianluca ; LEPSØY, Skjalg ; PORTO BUARQUE DE GUSMÃO, Pedro</creator><creatorcontrib>FRANCINI, Gianluca ; LEPSØY, Skjalg ; PORTO BUARQUE DE GUSMÃO, Pedro</creatorcontrib><description>A method in which a convolutional neural network is configured to receive an input data structure including a group of values corresponding to signal samples and to generate a corresponding classification output indicative of a selected one among plural predefined classes. The convolutional neural network includes an ordered sequence of layers, each configured to receive a corresponding layer input data structure including a group of input values, and generate a corresponding layer output data structure including a group of output values by convolving the layer input data structure with at least one corresponding filter including a corresponding group of weights. The layer input data structure of the first layer of the sequence corresponds to the input data structure. The layer input data structure of a generic layer of the sequence different from the first layer corresponds to the layer output data structure generated by a previous layer in the sequence.</description><language>eng ; fre ; ger</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240501&DB=EPODOC&CC=EP&NR=3427195B1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240501&DB=EPODOC&CC=EP&NR=3427195B1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>FRANCINI, Gianluca</creatorcontrib><creatorcontrib>LEPSØY, Skjalg</creatorcontrib><creatorcontrib>PORTO BUARQUE DE GUSMÃO, Pedro</creatorcontrib><title>CONVOLUTIONAL NEURAL NETWORKS, PARTICULARLY FOR IMAGE ANALYSIS</title><description>A method in which a convolutional neural network is configured to receive an input data structure including a group of values corresponding to signal samples and to generate a corresponding classification output indicative of a selected one among plural predefined classes. The convolutional neural network includes an ordered sequence of layers, each configured to receive a corresponding layer input data structure including a group of input values, and generate a corresponding layer output data structure including a group of output values by convolving the layer input data structure with at least one corresponding filter including a corresponding group of weights. The layer input data structure of the first layer of the sequence corresponds to the input data structure. The layer input data structure of a generic layer of the sequence different from the first layer corresponds to the layer output data structure generated by a previous layer in the sequence.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLBz9vcL8_cJDfH093P0UfBzDQ0CUyHh_kHewToKAY5BIZ7OoT6OQT6RCm7-QQqevo7urgqOQMWRwZ7BPAysaYk5xam8UJqbQcHNNcTZQze1ID8-tbggMTk1L7Uk3jXA2MTI3NDS1MnQmAglAEQYKjQ</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>FRANCINI, Gianluca</creator><creator>LEPSØY, Skjalg</creator><creator>PORTO BUARQUE DE GUSMÃO, Pedro</creator><scope>EVB</scope></search><sort><creationdate>20240501</creationdate><title>CONVOLUTIONAL NEURAL NETWORKS, PARTICULARLY FOR IMAGE ANALYSIS</title><author>FRANCINI, Gianluca ; LEPSØY, Skjalg ; PORTO BUARQUE DE GUSMÃO, Pedro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP3427195B13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>FRANCINI, Gianluca</creatorcontrib><creatorcontrib>LEPSØY, Skjalg</creatorcontrib><creatorcontrib>PORTO BUARQUE DE GUSMÃO, Pedro</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>FRANCINI, Gianluca</au><au>LEPSØY, Skjalg</au><au>PORTO BUARQUE DE GUSMÃO, Pedro</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>CONVOLUTIONAL NEURAL NETWORKS, PARTICULARLY FOR IMAGE ANALYSIS</title><date>2024-05-01</date><risdate>2024</risdate><abstract>A method in which a convolutional neural network is configured to receive an input data structure including a group of values corresponding to signal samples and to generate a corresponding classification output indicative of a selected one among plural predefined classes. The convolutional neural network includes an ordered sequence of layers, each configured to receive a corresponding layer input data structure including a group of input values, and generate a corresponding layer output data structure including a group of output values by convolving the layer input data structure with at least one corresponding filter including a corresponding group of weights. The layer input data structure of the first layer of the sequence corresponds to the input data structure. The layer input data structure of a generic layer of the sequence different from the first layer corresponds to the layer output data structure generated by a previous layer in the sequence.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng ; fre ; ger |
| recordid | cdi_epo_espacenet_EP3427195B1 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | CONVOLUTIONAL NEURAL NETWORKS, PARTICULARLY FOR IMAGE ANALYSIS |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T02%3A54%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=FRANCINI,%20Gianluca&rft.date=2024-05-01&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EEP3427195B1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |