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Author (up) Robert Benavente; Francesc Tous; Ramon Baldrich; Maria Vanrell edit  openurl
  Title Statical Modelling of a Colour Naming Space. Type Miscellaneous
  Year 2002 Publication Proceedings of the 1st. European Conference on Colour in Graphics Imaging and Vision: 406–411. Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BTB2002 Serial 289  
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Author (up) Robert Benavente; Gemma Sanchez; Ramon Baldrich; Maria Vanrell; Josep Llados edit  openurl
  Title Normalized colour segmentation for human appearance description. Type Miscellaneous
  Year 2000 Publication 15 th International Conference on Pattern Recognition, 3:637–641. Abbreviated Journal  
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  Address Barcelona.  
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  Notes DAG;CIC Approved no  
  Call Number CAT @ cat @ BSB2000 Serial 223  
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Author (up) Robert Benavente; Laura Igual; Fernando Vilariño edit  isbn
openurl 
  Title Current Challenges in Computer Vision Type Book Whole
  Year 2008 Publication Proccedings of the Third Internal Workshop Abbreviated Journal  
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  ISSN ISBN 978-84-936529-0-6 Medium  
  Area Expedition Conference CVCRD  
  Notes MILAB;CIC;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ BIV2008 Serial 1110  
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Author (up) Robert Benavente; M.C. Olive; Maria Vanrell; Ramon Baldrich edit  openurl
  Title Colour Perception: A Simple Method for Colour Naming. Type Miscellaneous
  Year 1999 Publication Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Girona  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BOV1999 Serial 47  
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Author (up) Robert Benavente; Maria Vanrell edit  openurl
  Title A colour naming experiment Type Report
  Year 2001 Publication CVC Technical Report #56 Abbreviated Journal  
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  Address CVC (UAB)  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BeV2001 Serial 78  
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Author (up) Robert Benavente; Maria Vanrell edit  openurl
  Title Fuzzy Colour Naming Based on Sigmoid Membership Functions. Type Miscellaneous
  Year 2004 Publication CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 Abbreviated Journal  
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  Address Aachen (Germany)  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BeV2004 Serial 441  
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Author (up) Robert Benavente; Maria Vanrell edit  openurl
  Title Parametrizacion del Espacio de Categorias de Color Type Miscellaneous
  Year 2007 Publication Proceedings del VIII Congreso Nacional del Color Abbreviated Journal  
  Volume Issue Pages 77–78  
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  Address Madrid (Spain)  
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  Area Expedition Conference CNC’07  
  Notes CAT;CIC Approved no  
  Call Number CAT @ cat @ BeV2007 Serial 905  
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Author (up) Robert Benavente; Maria Vanrell; Ramon Baldrich edit  openurl
  Title Estimation of Fuzzy Sets for Computational Colour Categorization Type Journal
  Year 2004 Publication Color Research and Application, 29(5):342–353 (IF: 0.739) Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BVB2004 Serial 484  
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Author (up) Robert Benavente; Maria Vanrell; Ramon Baldrich edit  url
openurl 
  Title A data set for fuzzy colour naming Type Journal
  Year 2006 Publication Color Research & Application, 31(1):48–56 Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BVB2006 Serial 590  
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Author (up) Robert Benavente; Maria Vanrell; Ramon Baldrich edit  openurl
  Title Parametric Fuzzy Sets for Automatic Color Naming Type Journal
  Year 2008 Publication Journal of the Optical Society of America A Abbreviated Journal  
  Volume 25 Issue 10 Pages 2582–2593  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BVB2008 Serial 1004  
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Author (up) Robert Benavente; Ramon Baldrich; M.C. Olive; Maria Vanrell edit  openurl
  Title Colour Naming Considering the Colour Variability Problem. Type Miscellaneous
  Year 2000 Publication Computacion y Sistemas, 4(1):30–43. Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BBO2000 Serial 242  
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Author (up) Roberto Morales; Juan Quispe; Eduardo Aguilar edit  url
doi  openurl
  Title Exploring multi-food detection using deep learning-based algorithms Type Conference Article
  Year 2023 Publication 13th International Conference on Pattern Recognition Systems Abbreviated Journal  
  Volume Issue Pages 1-7  
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  Abstract People are becoming increasingly concerned about their diet, whether for disease prevention, medical treatment or other purposes. In meals served in restaurants, schools or public canteens, it is not easy to identify the ingredients and/or the nutritional information they contain. Currently, technological solutions based on deep learning models have facilitated the recording and tracking of food consumed based on the recognition of the main dish present in an image. Considering that sometimes there may be multiple foods served on the same plate, food analysis should be treated as a multi-class object detection problem. EfficientDet and YOLOv5 are object detection algorithms that have demonstrated high mAP and real-time performance on general domain data. However, these models have not been evaluated and compared on public food datasets. Unlike general domain objects, foods have more challenging features inherent in their nature that increase the complexity of detection. In this work, we performed a performance evaluation of Efficient-Det and YOLOv5 on three public food datasets: UNIMIB2016, UECFood256 and ChileanFood64. From the results obtained, it can be seen that YOLOv5 provides a significant difference in terms of both mAP and response time compared to EfficientDet in all datasets. Furthermore, YOLOv5 outperforms the state-of-the-art on UECFood256, achieving an improvement of more than 4% in terms of mAP@.50 over the best reported.  
  Address Guayaquil; Ecuador; July 2023  
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  Area Expedition Conference ICPRS  
  Notes MILAB Approved no  
  Call Number Admin @ si @ MQA2023 Serial 3843  
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Author (up) Roger Max Calle Quispe; Maya Aghaei Gavari; Eduardo Aguilar Torres edit  url
openurl 
  Title Towards real-time accurate safety helmets detection through a deep learning-based method Type Journal
  Year 2023 Publication Ingeniare. Revista chilena de ingenieria Abbreviated Journal  
  Volume 31 Issue 12 Pages  
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  Abstract Occupational safety is a fundamental activity in industries and revolves around the management of the necessary controls that must be present to mitigate occupational risks. These controls include verifying the use of Personal Protection Equipment (PPE). Within PPE, safety helmets are vital to reducing severe or fatal consequences caused by head injuries. This problem has been addressed recently by various research based on deep learning to detect the usage of safety helmets by the present people in the industrial field.

These works have achieved promising results for safety helmet detection using object detection methods from the YOLO family. In this work, we propose to analyze the performance of Scaled-YOLOv4, a novel model of the YOLO family that has yet to be previously studied for this problem. The performance of the Scaled-YOLOv4 is evaluated on two public databases, carefully selected among the previously proposed datasets for the occupational safety framework. We demonstrate the superiority of Scaled-YOLOv4 in terms of mAP and Fl-score concerning the previous works for both databases. Further, we summarize the currently available datasets for safety helmet detection purposes and discuss their suitability.		  
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  Call Number Admin @ si @ CAA2023 Serial 3846  
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Author (up) Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos edit   pdf
openurl 
  Title Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer Type Conference Article
  Year 2017 Publication 18th World Conference on Lung Cancer Abbreviated Journal  
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  Abstract Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.

The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.

We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.		  
  Address Yokohama; Japan; October 2017  
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  Area Expedition Conference IASLC WCLC  
  Notes IAM; 600.096; 600.075; 600.145 Approved no  
  Call Number Admin @ si @ OGM2017 Serial 3044  
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Author (up) Rozenn Dhayot; Fernando Vilariño; Gerard Lacey edit  doi
openurl 
  Title Improving the Quality of Color Colonoscopy Videos Type Journal Article
  Year 2008 Publication EURASIP Journal on Image and Video Processing Abbreviated Journal EURASIP JIVP  
  Volume 139429 Issue 1 Pages 1-9  
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  Area 800 Expedition Conference  
  Notes MV;SIAI Approved no  
  Call Number fernando @ fernando @ Serial 2422  
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Select All    Deselect All [181–190] << 191 192 193 194 195 196 197 198 199 200 >> [201–210]
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