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| Author | Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez | ||||
| Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities | Type | Journal Article | ||
| Year | 2020 | Publication  |
Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 16 | Pages | 4587 |
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| Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. | ||||
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| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSM2020 | Serial | 3452 | ||
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| Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana | ||||
| Title | Saliency from High-Level Semantic Image Features | Type | Journal | ||
| Year | 2020 | Publication |
SN Computer Science | Abbreviated Journal | SN |
| Volume | 1 | Issue | 4 | Pages | 1-12 |
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| Abstract | Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). | ||||
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| Notes | LAMP; 600.120; 600.109; 600.106 | Approved | no | ||
| Call Number | Admin @ si @ AWD2020 | Serial | 3503 | ||
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| Author | Ana Garcia Rodriguez; Jorge Bernal; F. Javier Sanchez; Henry Cordova; Rodrigo Garces Duran; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
| Title | Polyp fingerprint: automatic recognition of colorectal polyps’ unique features | Type | Journal Article | ||
| Year | 2020 | Publication |
Surgical Endoscopy and other Interventional Techniques | Abbreviated Journal | SEND |
| Volume | 34 | Issue | 4 | Pages | 1887-1889 |
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| Abstract | BACKGROUND:
Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('polyp fingerprint'). METHODS: A machine learning technique called Bag of Words was used to describe each endoscopic image containing a polyp in a unique way. The system was tested with 243 white light images belonging to 99 different polyps (for each polyp there were at least two images representing it in two different temporal moments). Images were acquired in routine colonoscopies at Hospital Clínic using high-definition Olympus endoscopes. The method provided for each image the closest match within the dataset. RESULTS: The system matched another image of the same polyp in 221/243 cases (91%). No differences were observed in the number of correct matches according to Paris classification (protruded: 90.7% vs. non-protruded: 91.3%) and size (< 10 mm: 91.6% vs. > 10 mm: 90%). CONCLUSIONS: A CBIR system can match accurately two images containing the same polyp, which could be a helpful aid for polyp image recognition. KEYWORDS: Artificial intelligence; Colorectal polyps; Content-based image retrieval |
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| Notes | MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3403 | ||
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| Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li | ||||
| Title | Multi-modal Face Presentation Attach Detection | Type | Book Whole | ||
| Year | 2020 | Publication |
Synthesis Lectures on Computer Vision | Abbreviated Journal | |
| Volume | 13 | Issue | Pages | ||
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| Notes | HuPBA | Approved | no | ||
| Call Number | Admin @ si @ WGE2020 | Serial | 3440 | ||
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| Author | Sergio Escalera; Ralf Herbrich | ||||
| Title | The NeurIPS’18 Competition: From Machine Learning to Intelligent Conversations | Type | Book Whole | ||
| Year | 2020 | Publication |
The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
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| Abstract | This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. | ||||
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| Publisher | Place of Publication | Editor | Sergio Escalera; Ralf Hebrick | ||
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| ISSN | 2520-1328 | ISBN | 978-3-030-29134-1 | Medium | |
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ HeE2020 | Serial | 3328 | ||
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| Author | Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | 12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding | Type | Book Chapter | ||
| Year | 2020 | Publication |
Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
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| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | GNR2020 | Serial | 3494 | ||
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| Author | Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas | ||||
| Title | Historical review of scene text detection research | Type | Book Chapter | ||
| Year | 2020 | Publication |
Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | K. Alahari; C.V. Jawahar | |
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| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GBK2020 | Serial | 3495 | ||
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| Author | Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval | Type | Book Chapter | ||
| Year | 2020 | Publication |
Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | Analysis”, K. Alahari; C.V. Jawahar | |
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| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AGG2020 | Serial | 3496 | ||
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| Author | Debora Gil; Oriol Ramos Terrades; Raquel Perez | ||||
| Title | Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution | Type | Conference Article | ||
| Year | 2020 | Publication |
Women in Geometry and Topology | Abbreviated Journal | |
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| Address | Barcelona; September 2019 | ||||
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| Notes | IAM; DAG; 600.139; 600.145; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GRP2020 | Serial | 3473 | ||
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| Author | Fernando Vilariño | ||||
| Title | Unveiling the Social Impact of AI | Type | Conference Article | ||
| Year | 2020 | Publication |
Workshop at Digital Living Lab Days Conference | Abbreviated Journal | |
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| Address | September 2020 | ||||
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| Notes | MV; DAG; 600.121; 600.140;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2020 | Serial | 3459 | ||
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| Author | Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer | ||||
| Title | Bookworm continual learning: beyond zero-shot learning and continual learning | Type | Conference Article | ||
| Year | 2020 | Publication |
Workshop TASK-CV 2020 | Abbreviated Journal | |
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| Abstract | We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. | ||||
| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WHD2020 | Serial | 3466 | ||
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