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| Author | David Berga; Xavier Otazu; Xose R. Fernandez-Vidal; Victor Leboran; Xose M. Pardo | ||||
| Title | Generating Synthetic Images for Visual Attention Modeling | Type | Journal Article | ||
| Year | 2019 | Publication | Perception | Abbreviated Journal | PER |
| Volume | 48 | Issue | Pages | 99 | |
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| Notes | NEUROBIT; no menciona | Approved | no | ||
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Admin @ si @ BOF2019 | Serial | 3309 | ||
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| Author | Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey | ||||
| Title | Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Archaeological Science | Abbreviated Journal | JArchSci |
| Volume | 148 | Issue | Pages | 105654 | |
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| Abstract | This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. | ||||
| Address | December 2022 | ||||
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| Notes | MSIAU; MACO; 600.167 | Approved | no | ||
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Admin @ si @ BOL2022 | Serial | 3753 | ||
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| Author | Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J Gerrits; Jonas Gregorio de Souza; Afifa Khan; Maria Suarez Moreno; Jack Tomaney; Rebecca C Roberts; Cameron A Petrie | ||||
| Title | Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan | Type | Journal Article | ||
| Year | 2023 | Publication | Scientific Reports | Abbreviated Journal | ScR |
| Volume | 13 | Issue | Pages | 11257 | |
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| Abstract | This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map. | ||||
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| Notes | MSIAU | Approved | no | ||
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Admin @ si @ BOL2023 | Serial | 3976 | ||
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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 | ||
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Admin @ si @ BPC2018 | Serial | 3109 | ||
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| Author | Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez | ||||
| Title | Single image super-resolution based on directional variance attention network | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 133 | Issue | Pages | 108997 | |
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| Abstract | Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. | ||||
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| Notes | ISE | Approved | no | ||
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Admin @ si @ BPF2023 | Serial | 3861 | ||
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| Author | Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera | ||||
| Title | Error-Correcting Factorization | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 40 | Issue | Pages | 2388-2401 | |
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| Abstract | Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
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| Notes | HuPBA; no menciona | Approved | no | ||
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Admin @ si @ BPT2018 | Serial | 3015 | ||
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| 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 | |
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| 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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| Notes | DAG; 600.097; 601.302; 601.330; 600.140; 600.121 | Approved | no | ||
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Admin @ si @ BRC2019 | Serial | 3275 | ||
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| Author | Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
| Title | Frequency-based Enhancement Network for Efficient Super-Resolution | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 10 | Issue | Pages | 57383-57397 | |
| Keywords | Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution | ||||
| Abstract | Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet | ||||
| Address | 18 May 2022 | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
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| Notes | ISE | Approved | no | ||
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Admin @ si @ BRF2022a | Serial | 3747 | ||
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| Author | Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal | ||||
| Title | Beyond Document Object Detection: Instance-Level Segmentation of Complex Layouts | Type | Journal Article | ||
| Year | 2021 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 24 | Issue | Pages | 269–281 | |
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| Abstract | Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art. | ||||
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| Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
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Admin @ si @ BRL2021b | Serial | 3574 | ||
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| Author | Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou | ||||
| Title | Multi-Illuminant Estimation with Conditional Random Fields | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 23 | Issue | 1 | Pages | 83-95 |
| Keywords | color constancy; CRF; multi-illuminant | ||||
| Abstract | Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
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Admin @ si @ BRW2014 | Serial | 2451 | ||
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| Author | Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil | ||||
| Title | A benchmark for the evaluation of computational methods for bronchoscopic navigation | Type | Journal Article | ||
| Year | 2022 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCARS |
| Volume | 17 | Issue | 1 | Pages | |
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| Notes | IAM | Approved | no | ||
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Admin @ si @ BSC2022 | Serial | 3832 | ||
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| Author | Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil | ||||
| Title | BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation | Type | Journal Article | ||
| Year | 2023 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
| Volume | 228 | Issue | Pages | 107241 | |
| Keywords | Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation | ||||
| Abstract | Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | IAM; | Approved | no | ||
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Admin @ si @ BSC2023 | Serial | 3702 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Debora Gil; Cristina Rodriguez de Miguel; Fernando Vilariño | ||||
| Title | WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians | Type | Journal Article | ||
| Year | 2015 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 43 | Issue | Pages | 99-111 | |
| Keywords | Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection | ||||
| Abstract | We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice. | ||||
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| ISSN | 0895-6111 | ISBN | Medium | ||
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| Notes | MV; IAM; 600.047; 600.060; 600.075;SIAI | Approved | no | ||
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Admin @ si @ BSF2015 | Serial | 2609 | ||
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| Author | Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados | ||||
| Title | Classification of aesthetic natural scene images using statistical and semantic features | Type | Journal Article | ||
| Year | 2023 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 82 | Issue | 9 | Pages | 13507-13532 |
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| Abstract | Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. | ||||
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| Notes | DAG | Approved | no | ||
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Admin @ si @ BSP2023 | Serial | 3873 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Towards Automatic Polyp Detection with a Polyp Appearance Model | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 9 | Pages | 3166-3182 |
| Keywords | Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot | ||||
| Abstract | This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | IbPRIA | |
| Notes | MV;SIAI | Approved | no | ||
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Admin @ si @ BSV2012; IAM @ iam | Serial | 1997 | ||
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