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| Author | Ivet Rafegas | ||||
| Title | Color in Visual Recognition: from flat to deep representations and some biological parallelisms | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations. Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
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| Address | November 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell | |
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| ISSN | ISBN | 978-84-945373-7-0 | Medium | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Raf2017 | Serial | 3100 | ||
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| Author | C. Alejandro Parraga | ||||
| Title | Colours and Colour Vision: An Introductory Survey | Type | Journal Article | ||
| Year | 2017 | Publication | Perception | Abbreviated Journal | PER |
| Volume | 46 | Issue | 5 | Pages | 640-641 |
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| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Par2017 | Serial | 3101 | ||
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| Author | Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas | ||||
| Title | Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic | Type | Conference Article | ||
| Year | 2018 | Publication | Jornades Imatge i Recerca | Abbreviated Journal | |
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| Area | Expedition | Conference | JIR | ||
| Notes | DAG; 600.084; 600.135; 601.338; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GRB2018 | Serial | 3173 | ||
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| Author | Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 97-102 | ||
| Keywords | Robust Reading; End-to-end Systems; CNN; Utility Meters | ||||
| Abstract | In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GRK2018 | Serial | 3102 | ||
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| Author | Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou | ||||
| Title | The Robust Reading Competition Annotation and Evaluation Platform | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 61-66 | ||
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| Abstract | The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | KGR2018 | Serial | 3103 | ||
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| Author | David Aldavert; Marçal Rusiñol | ||||
| Title | Manuscript text line detection and segmentation using second-order derivatives analysis | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 293 - 298 | ||
| Keywords | text line detection; text line segmentation; text region detection; second-order derivatives | ||||
| Abstract | In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 302.065; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AlR2018a | Serial | 3104 | ||
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| Author | David Aldavert; Marçal Rusiñol | ||||
| Title | Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 223 - 228 | ||
| Keywords | Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information | ||||
| Abstract | Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AlR2018b | Serial | 3105 | ||
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| Author | V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol | ||||
| Title | Field Extraction by hybrid incremental and a-priori structural templates | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 251 - 256 | ||
| Keywords | Layout Analysis; information extraction; incremental learning | ||||
| Abstract | In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ PHR2018 | Serial | 3106 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma | ||||
| Title | Scale coding bag of deep features for human attribute and action recognition | Type | Journal Article | ||
| Year | 2018 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
| Volume | 29 | Issue | 1 | Pages | 55-71 |
| Keywords | Action recognition; Attribute recognition; Bag of deep features | ||||
| Abstract | Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art. | ||||
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| Notes | LAMP; 600.068; 600.079; 600.106; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ KWR2018 | Serial | 3107 | ||
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| Author | Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy | ||||
| Title | End-to-end Driving via Conditional Imitation Learning | Type | Conference Article | ||
| Year | 2018 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
| Volume | Issue | Pages | 4693 - 4700 | ||
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| Abstract | Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL | ||||
| Address | Brisbane; Australia; May 2018 | ||||
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| Area | Expedition | Conference | ICRA | ||
| Notes | ADAS; 600.116; 600.124; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ CML2018 | Serial | 3108 | ||
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| Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva | ||||
| Title | Egocentric video description based on temporally-linked sequences | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
| Volume | 50 | Issue | Pages | 205-216 | |
| Keywords | egocentric vision; video description; deep learning; multi-modal learning | ||||
| Abstract | Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BPC2018 | Serial | 3109 | ||
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| Author | Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel | ||||
| Title | A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 465-473 | |
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| Abstract | Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. | ||||
| Address | Povoa de Varzim; Portugal; June 2018 | ||||
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Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICIAR | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SRR2018a | Serial | 3110 | ||
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| Author | Stefan Lonn; Petia Radeva; Mariella Dimiccoli | ||||
| Title | A picture is worth a thousand words but how to organize thousands of pictures? | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ LRD2018 | Serial | 3111 | ||
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| Author | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig | ||||
| Title | SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. | Type | Conference Article | ||
| Year | 2018 | Publication | 21st International Conference on Medical Image Computing & Computer Assisted Intervention | Abbreviated Journal | |
| Volume | 2 | Issue | Pages | 21-29 | |
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| Abstract | Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU. | ||||
| Address | Granada; Espanya; September 2018 | ||||
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| Area | Expedition | Conference | MICCAI | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SRA2018 | Serial | 3112 | ||
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| Author | Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
| Title | CuisineNet: Food Attributes Classification using Multi-scale Convolution Network | Type | Conference Article | ||
| Year | 2018 | Publication | 21st International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | 365-372 | ||
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| Abstract | Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. | ||||
| Address | Roses; catalonia; October 2018 | ||||
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| Area | Expedition | Conference | CCIA | ||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SJR2018 | Serial | 3113 | ||
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