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Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell | ||||
Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
Year | 2016 | Publication | Chest Journal | Abbreviated Journal | CHEST |
Volume | 150 | Issue | 4 | Pages | 1003A |
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Notes | IAM; 600.096; 600.075 | Approved | no | ||
Call Number | Admin @ si @ DGC2016 | Serial | 3099 | ||
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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 | 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 | 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 | 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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Series Editor | Series Title | 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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Author | Albert Clapes; Ozan Bilici; Dariia Temirova; Egils Avots; Gholamreza Anbarjafari; Sergio Escalera | ||||
Title | From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation | Type | Conference Article | ||
Year | 2018 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 2373-2382 | ||
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Address | Salt Lake City; USA; June 2018 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3116 | ||
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Author | Mohammad A. Haque; Ruben B. Bautista; Kamal Nasrollahi; Sergio Escalera; Christian B. Laursen; Ramin Irani; Ole K. Andersen; Erika G. Spaich; Kaustubh Kulkarni; Thomas B. Moeslund; Marco Bellantonio; Golamreza Anbarjafari; Fatemeh Noroozi | ||||
Title | Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures | Type | Conference Article | ||
Year | 2018 | Publication | 13th IEEE Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 250 - 257 | ||
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Abstract | Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate. | ||||
Address | Xian; China; May 2018 | ||||
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Area | Expedition | Conference | FG | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ HBN2018 | Serial | 3117 | ||
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Author | Rain Eric Haamer; Kaustubh Kulkarni; Nasrin Imanpour; Mohammad Ahsanul Haque; Egils Avots; Michelle Breisch; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Xavier Baro; Ahmad R. Naghsh-Nilchi; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
Title | Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification | Type | Conference Article | ||
Year | 2018 | Publication | 8th International Workshop on Human Behavior Understanding | Abbreviated Journal | |
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Abstract | Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames. | ||||
Address | Xian; China; May 2018 | ||||
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Area | Expedition | Conference | FGW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ HKI2018 | Serial | 3118 | ||
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Author | Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli | ||||
Title | Recurrent Neural Networks for Remote Sensing Image Classification | Type | Journal Article | ||
Year | 2018 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
Volume | 12 | Issue | 7 | Pages | 1040 - 1045 |
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Abstract | Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ LÇE2018 | Serial | 3119 | ||
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Author | Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari | ||||
Title | Dominant and Complementary Emotion Recognition from Still Images of Faces | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 6 | Issue | Pages | 26391 - 26403 | |
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Abstract | Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ GLW2018 | Serial | 3122 | ||
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