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| Author | Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning | Type | Conference Article | ||
| Year | 2019 | Publication | 13th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 80-85 | ||
| Keywords | Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning | ||||
| Abstract | With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of sketches, showing promising results. |
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| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.140; 601.302; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRF2019 | Serial | 3354 | ||
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| Author | Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Table Detection in Invoice Documents by Graph Neural Networks | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 122-127 | ||
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| Abstract | Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research. |
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| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.140; 601.302; 602.167; 600.121; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ RDG2019 | Serial | 3355 | ||
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| Author | Ekta Vats; Anders Hast; Alicia Fornes | ||||
| Title | Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1294-1299 | ||
| Keywords | Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching | ||||
| Abstract | Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method. |
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| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ VHF2019 | Serial | 3356 | ||
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| Author | Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell | ||||
| Title | Segmentation of Distal Airways using Structural Analysis | Type | Journal Article | ||
| Year | 2019 | Publication | PloS one | Abbreviated Journal | Plos |
| Volume | 14 | Issue | 12 | Pages | |
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| Abstract | Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution. | ||||
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| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GSB2019 | Serial | 3357 | ||
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| Author | Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil | ||||
| Title | Selection of Radiomics Features based on their Reproducibility | Type | Conference Article | ||
| Year | 2019 | Publication | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society | Abbreviated Journal | |
| Volume | Issue | Pages | 403-408 | ||
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| Abstract | Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network. | ||||
| Address | Berlin; Alemanya; July 2019 | ||||
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| Area | Expedition | Conference | EMBC | ||
| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ LTS2019 | Serial | 3358 | ||
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| Author | Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez | ||||
| Title | Data Augmentation from Sketch | Type | Conference Article | ||
| Year | 2019 | Publication | International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging | Abbreviated Journal | |
| Volume | 11840 | Issue | Pages | 155-162 | |
| Keywords | Data augmentation; cycleGANs; Multi-objective optimization | ||||
| Abstract | State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.
We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives. |
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| Address | Shenzhen; China; October 2019 | ||||
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Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CLIP | ||
| Notes | IAM; 600.145; 601.337; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GES2019 | Serial | 3359 | ||
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| Author | Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil | ||||
| Title | Back to Front Architecture for Diagnosis as a Service | Type | Conference Article | ||
| Year | 2018 | Publication | 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing | Abbreviated Journal | |
| Volume | Issue | Pages | 343-346 | ||
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| Abstract | Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction. | ||||
| Address | Timisoara; Rumania; September 2018 | ||||
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| Area | Expedition | Conference | SYNASC | ||
| Notes | IAM; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SVA2018 | Serial | 3360 | ||
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| Author | Debora Gil; Antoni Rosell | ||||
| Title | Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? | Type | Abstract | ||
| Year | 2019 | Publication | World Lung Cancer Conference | Abbreviated Journal | |
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| Abstract | Invited speaker | ||||
| Address | Barcelona; September 2019 | ||||
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| Area | Expedition | Conference | IASLC WCLC | ||
| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GiR2019 | Serial | 3361 | ||
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| Author | Rada Deeb; Joost Van de Weijer; Damien Muselet; Mathieu Hebert; Alain Tremeau | ||||
| Title | Deep spectral reflectance and illuminant estimation from self-interreflections | Type | Journal Article | ||
| Year | 2019 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 31 | Issue | 1 | Pages | 105-114 |
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| Abstract | In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ DWM2019 | Serial | 3362 | ||
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| Author | Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz | ||||
| Title | SDIT: Scalable and Diverse Cross-domain Image Translation | Type | Conference Article | ||
| Year | 2019 | Publication | 27th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 1267–1276 | ||
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| Abstract | Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. | ||||
| Address | Nice; Francia; October 2019 | ||||
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| Area | Expedition | Conference | ACM-MM | ||
| Notes | LAMP; 600.106; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WGW2019 | Serial | 3363 | ||
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| Author | Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit | ||||
| Title | Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 149 | Issue | Pages | 164-171 | |
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| Abstract | Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. | ||||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DGV2021 | Serial | 3364 | ||
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| Author | Mohammed Al Rawi; Ernest Valveny | ||||
| Title | Compact and Efficient Multitask Learning in Vision, Language and Speech | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2933-2942 | ||
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| Abstract | Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RaV2019 | Serial | 3365 | ||
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| Author | Eduardo Aguilar; Petia Radeva | ||||
| Title | Class-Conditional Data Augmentation Applied to Image Classification | Type | Conference Article | ||
| Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 11679 | Issue | Pages | 182-192 | |
| Keywords | CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition | ||||
| Abstract | Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty. | ||||
| Address | Salermo; Italy; September 2019 | ||||
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Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CAIP | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ AgR2019 | Serial | 3366 | ||
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| Author | Estefania Talavera; Nicolai Petkov; Petia Radeva | ||||
| Title | Unsupervised Routine Discovery in Egocentric Photo-Streams | Type | Conference Article | ||
| Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 11678 | Issue | Pages | 576-588 | |
| Keywords | Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis | ||||
| Abstract | The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people. | ||||
| Address | Salermo; Italy; September 2019 | ||||
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Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CAIP | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TPR2019a | Serial | 3367 | ||
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| Author | Md.Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig | ||||
| Title | Food Places Classification in Egocentric Images Using Siamese Neural Networks | Type | Conference Article | ||
| Year | 2019 | Publication | 22nd International Conference of the Catalan Association of Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | 145-151 | ||
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| Abstract | Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2. | ||||
| Address | Illes Balears; October 2019 | ||||
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| Area | Expedition | Conference | CCIA | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SBR2019 | Serial | 3368 | ||
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