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| Author | Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | ICDAR 2019 Competition on Scene Text Visual Question Answering | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1563-1570 | ||
| Keywords | |||||
| Abstract | This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23,038 images annotated with 31,791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that can exploit scene text to achieve holistic image understanding. | ||||
| Address | Sydney; Australia; September 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.129; 601.338; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BTM2019c | Serial  |
3286 | ||
| Permanent link to this record | |||||
| Author | Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | Scene Text Visual Question Answering | Type | Conference Article | ||
| Year | 2019 | Publication | 18th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 4291-4301 | ||
| Keywords | |||||
| Abstract | Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting highlevel semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research. | ||||
| Address | Seul; Corea; October 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCV | ||
| Notes | DAG; 600.129; 600.135; 601.338; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BTM2019b | Serial |
3285 | ||
| Permanent link to this record | |||||
| Author | Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | ICDAR 2019 Competition on Scene Text Visual Question Answering | Type | Conference Article | ||
| Year | 2019 | Publication | 3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed
by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23, 038 images annotated with 31, 791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that can exploit scene text to achieve holistic image understanding. |
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| Address | Sydney; Australia; September 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CLVL | ||
| Notes | DAG; 600.129; 601.338; 600.135; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BTM2019a | Serial |
3284 | ||
| Permanent link to this record | |||||
| Author | Marçal Rusiñol; Lluis Gomez; A. Landman; M. Silva Constenla; Dimosthenis Karatzas | ||||
| Title | Automatic Structured Text Reading for License Plates and Utility Meters | Type | Conference Article | ||
| Year | 2019 | Publication | BMVC Workshop on Visual Artificial Intelligence and Entrepreneurship | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Reading text in images has attracted interest from computer vision researchers for
many years. Our technology focuses on the extraction of structured text – such as serial numbers, machine readings, product codes, etc. – so that it is able to center its attention just on the relevant textual elements. It is conceived to work in an end-to-end fashion, bypassing any explicit text segmentation stage. In this paper we present two different industrial use cases where we have applied our automatic structured text reading technology. In the first one, we demonstrate an outstanding performance when reading license plates compared to the current state of the art. In the second one, we present results on our solution for reading utility meters. The technology is commercialized by a recently created spin-off company, and both solutions are at different stages of integration with final clients. |
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| Address | Cardiff; UK; September 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | BMVC-VAIE19 | ||
| Notes | DAG; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RGL2019 | Serial |
3283 | ||
| Permanent link to this record | |||||
| Author | Marçal Rusiñol | ||||
| Title | Classificació semàntica i visual de documents digitals | Type | Journal | ||
| Year | 2019 | Publication | Revista de biblioteconomia i documentacio | Abbreviated Journal | |
| Volume | Issue | Pages | 75-86 | ||
| Keywords | |||||
| Abstract | Se analizan los sistemas de procesamiento automático que trabajan sobre documentos digitalizados con el objetivo de describir los contenidos. De esta forma contribuyen a facilitar el acceso, permitir la indización automática y hacer accesibles los documentos a los motores de búsqueda. El objetivo de estas tecnologías es poder entrenar modelos computacionales que sean capaces de clasificar, agrupar o realizar búsquedas sobre documentos digitales. Así, se describen las tareas de clasificación, agrupamiento y búsqueda. Cuando utilizamos tecnologías de inteligencia artificial en los sistemas de
clasificación esperamos que la herramienta nos devuelva etiquetas semánticas; en sistemas de agrupamiento que nos devuelva documentos agrupados en clusters significativos; y en sistemas de búsqueda esperamos que dada una consulta, nos devuelva una lista ordenada de documentos en función de la relevancia. A continuación se da una visión de conjunto de los métodos que nos permiten describir los documentos digitales, tanto de manera visual (cuál es su apariencia), como a partir de sus contenidos semánticos (de qué hablan). En cuanto a la descripción visual de documentos se aborda el estado de la cuestión de las representaciones numéricas de documentos digitalizados tanto por métodos clásicos como por métodos basados en el aprendizaje profundo (deep learning). Respecto de la descripción semántica de los contenidos se analizan técnicas como el reconocimiento óptico de caracteres (OCR); el cálculo de estadísticas básicas sobre la aparición de las diferentes palabras en un texto (bag-of-words model); y los métodos basados en aprendizaje profundo como el método word2vec, basado en una red neuronal que, dadas unas cuantas palabras de un texto, debe predecir cuál será la siguiente palabra. Desde el campo de las ingenierías se están transfiriendo conocimientos que se han integrado en productos o servicios en los ámbitos de la archivística, la biblioteconomía, la documentación y las plataformas de gran consumo, sin embargo los algoritmos deben ser lo suficientemente eficientes no sólo para el reconocimiento y transcripción literal sino también para la capacidad de interpretación de los contenidos. |
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| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.084; 600.135; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ Rus2019 | Serial |
3282 | ||
| Permanent link to this record | |||||
| Author | Lu Yu; Vacit Oguz Yazici; Xialei Liu; Joost Van de Weijer; Yongmei Cheng; Arnau Ramisa | ||||
| Title | Learning Metrics from Teachers: Compact Networks for Image Embedding | Type | Conference Article | ||
| Year | 2019 | Publication | 32nd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2907-2916 | ||
| Keywords | |||||
| Abstract | Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the
communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5% to 44.6%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semisupervised learning and cross quality distillation. |
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| Address | Long beach; California; june 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YYL2019 | Serial |
3281 | ||
| Permanent link to this record | |||||
| Author | Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Exploring Hate Speech Detection in Multimodal Publications | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research. | ||||
| Address | Aspen; March 2020 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GGG2020a | Serial |
3280 | ||
| Permanent link to this record | |||||
| Author | Lichao Zhang; Martin Danelljan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan | ||||
| Title | Multi-Modal Fusion for End-to-End RGB-T Tracking | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2252-2261 | ||
| Keywords | |||||
| Abstract | We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a carefully designed target prediction network trained end-to-end using a discriminative loss. We analyze the effectiveness of modality fusion in each of the main components in DiMP, i.e. feature extractor, target estimation network, and classifier. We consider several fusion mechanisms acting at different levels of the framework, including pixel-level, feature-level and response-level. Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities. As data to train our model, we generate a large-scale RGB-T dataset by considering an annotated RGB tracking dataset (GOT-10k) and synthesizing paired TIR images using an image-to-image translation approach. We perform extensive experiments on VOT-RGBT2019 dataset and RGBT210 dataset, evaluating each type of modality fusing on each model component. The results show that the proposed fusion mechanisms improve the performance of the single modality counterparts. We obtain our best results when fusing at the feature-level on both the IoU-Net and the model predictor, obtaining an EAO score of 0.391 on VOT-RGBT2019 dataset. With this fusion mechanism we achieve the state-of-the-art performance on RGBT210 dataset. | ||||
| Address | Seul; Corea; October 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCVW | ||
| Notes | LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ ZDG2019 | Serial |
3279 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Jialuo Chen; Alicia Fornes; Beata Megyesi | ||||
| Title | Towards a generic unsupervised method for transcription of encoded manuscripts | Type | Conference Article | ||
| Year | 2019 | Publication | 3rd International Conference on Digital Access to Textual Cultural Heritage | Abbreviated Journal | |
| Volume | Issue | Pages | 73-78 | ||
| Keywords | A. Baró, J. Chen, A. Fornés, B. Megyesi. | ||||
| Abstract | Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods. | ||||
| Address | Brussels; May 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DATeCH | ||
| Notes | DAG; 600.097; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BCF2019 | Serial |
3276 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | From Optical Music Recognition to Handwritten Music Recognition: a Baseline | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 123 | Issue | Pages | 1-8 | |
| Keywords | |||||
| Abstract | Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. | ||||
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| Area | Expedition | Conference | |||
| Notes | DAG; 600.097; 601.302; 601.330; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRC2019 | Serial |
3275 | ||
| Permanent link to this record | |||||
| Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; V. Leboran; Xose M. Pardo | ||||
| Title | Psychophysical evaluation of individual low-level feature influences on visual attention | Type | Journal Article | ||
| Year | 2019 | Publication | Vision Research | Abbreviated Journal | VR |
| Volume | 154 | Issue | Pages | 60-79 | |
| Keywords | Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset | ||||
| Abstract | In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images. | ||||
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| Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BFO2019a | Serial |
3274 | ||
| Permanent link to this record | |||||
| Author | Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu | ||||
| Title | Automatic Image-Based Waste Classification | Type | Conference Article | ||
| Year | 2019 | Publication | International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning | Abbreviated Journal | |
| Volume | 11487 | Issue | Pages | 422–431 | |
| Keywords | Computer Vision; Deep learning; Convolutional neural networks; Waste classification | ||||
| Abstract | The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset. | ||||
| Address | Almeria; June 2019 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IWINAC | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | RSV2019 | Serial |
3273 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Image Vegetation Index through a Cycle Generative Adversarial Network | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition-Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. | ||||
| Address | Long beach; California; USA; June 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2019 | Serial |
3272 | ||
| Permanent link to this record | |||||
| Author | Armin Mehri; Angel Sappa | ||||
| Title | Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition-Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art | ||||
| Address | Long beach; California; USA; June 2019 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MeS2019 | Serial |
3271 | ||
| Permanent link to this record | |||||
| Author | Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez | ||||
| Title | Robust Detection of Outdoor Urban Advertising Panels in Static Images | Type | Conference Article | ||
| Year | 2019 | Publication | 18th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 246-256 | ||
| Keywords | Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality | ||||
| Abstract | One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images. | ||||
| Address | Aquila; Italia; June 2019 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | PAAMS | ||
| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSS2019 | Serial |
3270 | ||
| Permanent link to this record | |||||