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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | Sign Language Recognition: A Deep Survey | Type | Journal Article | ||
| Year | 2021 | Publication | Expert Systems With Applications | Abbreviated Journal | ESWA |
| Volume | 164 | Issue | Pages  |
113794 | |
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| Abstract | Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field. | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKE2021a | Serial | 3521 | ||
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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | Hand sign language recognition using multi-view hand skeleton | Type | Journal Article | ||
| Year | 2020 | Publication | Expert Systems With Applications | Abbreviated Journal | ESWA |
| Volume | 150 | Issue | Pages |
113336 | |
| Keywords | Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition | ||||
| Abstract | Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKE2020a | Serial | 3411 | ||
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| Author | Ruben Tito; Dimosthenis Karatzas; Ernest Valveny | ||||
| Title | Hierarchical multimodal transformers for Multi-Page DocVQA | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 144 | Issue | Pages |
109834 | |
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| Abstract | Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISSN 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.155; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ TKV2023 | Serial | 3825 | ||
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| Author | Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva | ||||
| Title | Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN | Type | Journal Article | ||
| Year | 2023 | Publication | Food Control | Abbreviated Journal | FC |
| Volume | 147 | Issue | Pages |
109554 | |
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| Abstract | Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ MNJ2023 | Serial | 3882 | ||
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| Author | Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia | ||||
| Title | Dense extreme inception network for edge detection | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 139 | Issue | Pages |
109461 | |
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| Abstract | Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. | ||||
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| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SSH2023 | Serial | 3982 | ||
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| Author | Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades | ||||
| Title | VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 139 | Issue | Pages |
109419 | |
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| Abstract | Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. | ||||
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| ISSN | ISSN 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BMC2023 | Serial | 3826 | ||
| Permanent link to this record | |||||
| Author | Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez | ||||
| Title | Image rain removal and illumination enhancement done in one go | Type | Journal Article | ||
| Year | 2022 | Publication | Knowledge-Based Systems | Abbreviated Journal | KBS |
| Volume | 252 | Issue | Pages |
109244 | |
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| Abstract | Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement. | ||||
| Address | Sept 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | ISE; 600.157; 600.168 | Approved | no | ||
| Call Number | Admin @ si @ WCS2022 | Serial | 3744 | ||
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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 | ||
| Call Number | Admin @ si @ BPF2023 | Serial | 3861 | ||
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| Author | Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title | Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 129 | Issue | Pages |
108766 | |
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| Abstract | The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. | ||||
| Address | Sept. 2022 | ||||
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| Notes | DAG; 600.121; 600.162 | Approved | no | ||
| Call Number | Admin @ si @ KRR2022 | Serial | 3556 | ||
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| Author | Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados | ||||
| Title | Table detection in business document images by message passing networks | Type | Journal Article | ||
| Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 127 | Issue | Pages |
108641 | |
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| Abstract | Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. | ||||
| Address | July 2022 | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | DAG; 600.162; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RGR2022 | Serial | 3729 | ||
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| Author | Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva | ||||
| Title | Bayesian deep learning for semantic segmentation of food images | Type | Journal Article | ||
| Year | 2022 | Publication | Computers and Electrical Engineering | Abbreviated Journal | CEE |
| Volume | 103 | Issue | Pages |
108380 | |
| Keywords | Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis | ||||
| Abstract | Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. | ||||
| Address | October 2022 | ||||
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| Publisher | Science Direct | Place of Publication | Editor | ||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANR2022 | Serial | 3763 | ||
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| Author | S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes | ||||
| Title | Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement | Type | Journal Article | ||
| Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 123 | Issue | Pages |
108370 | |
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| Abstract | Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task. | ||||
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| Notes | DAG; 600.124; 600.121; 602.230 | Approved | no | ||
| Call Number | Admin @ si @ JSK2022 | Serial | 3613 | ||
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| Author | Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes | ||||
| Title | Learning graph edit distance by graph neural networks | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 120 | Issue | Pages |
108132 | |
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| Abstract | The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. | ||||
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| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RFL2021 | Serial | 3611 | ||
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| Author | Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol | ||||
| Title | Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 112 | Issue | Pages |
107790 | |
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| Abstract | Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging problem. The main challenge faced when training a language model is to deal with the language model corpus which is usually different to the one used for training the handwritten word recognition system. Thus, the bias between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this work, we introduce Candidate Fusion, a novel way to integrate an external language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two improvements. On the one hand, the sequence-to-sequence recognizer has the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided by the language model. On the other hand, the external language model has the ability to adapt itself to the training corpus and even learn the most commonly errors produced from the recognizer. Finally, by conducting comprehensive experiments, the Candidate Fusion proves to outperform the state-of-the-art language models for handwritten word recognition tasks. |
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| Notes | DAG; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRV2021 | Serial | 3343 | ||
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| Author | Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | Real-time Lexicon-free Scene Text Retrieval | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 110 | Issue | Pages |
107656 | |
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| Abstract | In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. | ||||
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| Notes | DAG; 600.121; 600.129; 601.338 | Approved | no | ||
| Call Number | Admin @ si @ MTD2021 | Serial | 3493 | ||
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