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Author  |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | Word separation in continuous sign language using isolated signs and post-processing | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLVID, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection. | ||||
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ RKE2022b | Serial | 3824 | ||
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences | ||||
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ RKE2022d | Serial | 3828 | ||
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | ZS-GR: zero-shot gesture recognition from RGB-D videos | Type | Journal Article | ||
| Year | 2023 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 82 | Issue | Pages | 43781-43796 | |
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| Abstract | Gesture Recognition (GR) is a challenging research area in computer vision. To tackle the annotation bottleneck in GR, we formulate the problem of Zero-Shot Gesture Recognition (ZS-GR) and propose a two-stream model from two input modalities: RGB and Depth videos. To benefit from the vision Transformer capabilities, we use two vision Transformer models, for human detection and visual features representation. We configure a transformer encoder-decoder architecture, as a fast and accurate human detection model, to overcome the challenges of the current human detection models. Considering the human keypoints, the detected human body is segmented into nine parts. A spatio-temporal representation from human body is obtained using a vision Transformer and a LSTM network. A semantic space maps the visual features to the lingual embedding of the class labels via a Bidirectional Encoder Representations from Transformers (BERT) model. We evaluated the proposed model on five datasets, Montalbano II, MSR Daily Activity 3D, CAD-60, NTU-60, and isoGD obtaining state-of-the-art results compared to state-of-the-art ZS-GR models as well as the Zero-Shot Action Recognition (ZS-AR). | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RKE2023a | Serial | 3879 | ||
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | A deep co-attentive hand-based video question answering framework using multi-view skeleton | Type | Journal Article | ||
| Year | 2023 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 82 | Issue | Pages | 1401–1429 | |
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| Abstract | In this paper, we present a novel hand –based Video Question Answering framework, entitled Multi-View Video Question Answering (MV-VQA), employing the Single Shot Detector (SSD), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and Co-Attention mechanism with RGB videos as the inputs. Our model includes three main blocks: vision, language, and attention. In the vision block, we employ a novel representation to obtain some efficient multiview features from the hand object using the combination of five 3DCNNs and one LSTM network. To obtain the question embedding, we use the BERT model in language block. Finally, we employ a co-attention mechanism on vision and language features to recognize the final answer. For the first time, we propose such a hand-based Video-QA framework including the multi-view hand skeleton features combined with the question embedding and co-attention mechanism. Our framework is capable of processing the arbitrary numbers of questions in the dataset annotations. There are different application domains for this framework. Here, as an application domain, we applied our framework to dynamic hand gesture recognition for the first time. Since the main object in dynamic hand gesture recognition is the human hand, we performed a step-by-step analysis of the hand detection and multi-view hand skeleton impact on the model performance. Evaluation results on five datasets, including two datasets in VideoQA, two datasets in dynamic hand gesture, and one dataset in hand action recognition show that MV-VQA outperforms state-of-the-art alternatives. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RKE2023b | Serial | 3881 | ||
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | A transformer model for boundary detection in continuous sign language | Type | Journal Article | ||
| Year | 2024 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
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| Abstract | Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundaries of isolated signs within a continuous video stream. Additionally, the reliance on handcrafted features in existing models poses a challenge to achieving optimal accuracy. To surmount these challenges, we propose a novel approach utilizing a Transformer-based model. Unlike traditional models, our approach focuses on enhancing accuracy while eliminating the need for handcrafted features. The Transformer model is employed for both ISLR and CSLR. The training process involves using isolated sign videos, where hand keypoint features extracted from the input video are enriched using the Transformer model. Subsequently, these enriched features are forwarded to the final classification layer. The trained model, coupled with a post-processing method, is then applied to detect isolated sign boundaries within continuous sign videos. The evaluation of our model is conducted on two distinct datasets, including both continuous signs and their corresponding isolated signs, demonstrates promising results. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RKE2024 | Serial | 4016 | ||
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Mohammad Sabokrou | ||||
| Title | Sign Language Production: A Review | Type | Conference Article | ||
| Year | 2021 | Publication | Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 3472-3481 | ||
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| Abstract | Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research. | ||||
| Address | Virtual; June 2021 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKE2021b | Serial | 3603 | ||
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Vassilis Athitsos; Mohammad Sabokrou | ||||
| Title | All You Need In Sign Language Production | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Sign Language Production; Sign Language Recog- nition; Sign Language Translation; Deep Learning; Survey; Deaf | ||||
| Abstract | Sign Language is the dominant form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental.
To this end, sign language recognition and production are two necessary parts for making such a two-way system. Signlanguage recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. To have more realistic perspectives to sign language, we present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language, the main differences between spoken language and sign language. Furthermore, we present the fundamental components of a bi-directional sign language translation system, discussing the main challenges in this area. Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented. Finally, a general framework for SLP and performance evaluation, and also a discussion on the recent developments, advantages, and limitations in SLP, commenting on possible lines for future research are presented. |
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ RKE2022c | Serial | 3698 | ||
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| Author |
Reuben Dorent; Aaron Kujawa; Marina Ivory; Spyridon Bakas; Nikola Rieke; Samuel Joutard; Ben Glocker; Jorge Cardoso; Marc Modat; Kayhan Batmanghelich; Arseniy Belkov; Maria Baldeon Calisto; Jae Won Choi; Benoit M. Dawant; Hexin Dong; Sergio Escalera; Yubo Fan; Lasse Hansen; Mattias P. Heinrich; Smriti Joshi; Victoriya Kashtanova; Hyeon Gyu Kim; Satoshi Kondo; Christian N. Kruse; Susana K. Lai-Yuen; Hao Li; Han Liu; Buntheng Ly; Ipek Oguz; Hyungseob Shin; Boris Shirokikh; Zixian Su; Guotai Wang; Jianghao Wu; Yanwu Xu; Kai Yao; Li Zhang; Sebastien Ourselin, | ||||
| Title | CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation | Type | Journal Article | ||
| Year | 2023 | Publication | Medical Image Analysis | Abbreviated Journal | MIA |
| Volume | 83 | Issue | Pages | 102628 | |
| Keywords | Domain Adaptation; Segmen tation; Vestibular Schwnannoma | ||||
| Abstract | Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice – VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice – VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ DKI2023 | Serial | 3706 | ||
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| Author |
Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera | ||||
| Title | Deep Frequency Re-Calibration U-Net for Medical Image Segmentation | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 3274-3283 | ||
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| Abstract | We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. | ||||
| Address | VIRTUAL; October 2021 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ ABA2021 | Serial | 3645 | ||
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| Author |
Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
| Title | Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions | Type | Conference Article | ||
| Year | 2019 | Publication | Visual Recognition for Medical Images workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 406-415 | ||
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| Abstract | In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ AAF2019 | Serial | 3324 | ||
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| Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
| Title | Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation | Type | Conference Article | ||
| Year | 2020 | Publication | Bioimage computation workshop | Abbreviated Journal | |
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| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ AAF2020 | Serial | 3520 | ||
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| Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera | ||||
| Title | Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Circuits and Systems for Video Technology | Abbreviated Journal | TCSVT |
| Volume | 29 | Issue | 6 | Pages | 1729-1740 |
| Keywords | Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding | ||||
| Abstract | Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. | ||||
| Address | June 2019, | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ AAK2018 | Serial | 3213 | ||
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| Author |
Ricard Balague | ||||
| Title | Exploring the combination of color cues for intrinsic image decomposition | Type | Report | ||
| Year | 2014 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 178 | Issue | Pages | ||
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| Abstract | Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth. | ||||
| Address | UAB; September 2014 | ||||
| Corporate Author | Thesis | Master's thesis | |||
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| Notes | CIC; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ Bal2014 | Serial | 2579 | ||
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| Author |
Ricard Borras; Agata Lapedriza; Laura Igual | ||||
| Title | Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition | Type | Conference Article | ||
| Year | 2012 | Publication | 9th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 7325 | Issue | II | Pages | 98-105 |
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| Abstract | This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems. | ||||
| Address | Aveiro, Portugal | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-31297-7 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | OR; MILAB;MV | Approved | no | ||
| Call Number | Admin @ si @ BLI2012 | Serial | 2009 | ||
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| Author |
Ricard Coll; Alicia Fornes; Josep Llados | ||||
| Title | Graphological Analysis of Handwritten Text Documents for Human Resources Recruitment | Type | Conference Article | ||
| Year | 2009 | Publication | 10th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1081–1085 | ||
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| Abstract | The use of graphology in recruitment processes has become a popular tool in many human resources companies. This paper presents a model that links features from handwritten images to a number of personality characteristics used to measure applicant aptitudes for the job in a particular hiring scenario. In particular we propose a model of measuring active personality and leadership of the writer. Graphological features that define such a profile are measured in terms of document and script attributes like layout configuration, letter size, shape, slant and skew angle of lines, etc. After the extraction, data is classified using a neural network. An experimental framework with real samples has been constructed to illustrate the performance of the approach. | ||||
| Address | Barcelona, Spain | ||||
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| ISSN | 1520-5363 | ISBN | 978-1-4244-4500-4 | Medium | |
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ CFL2009 | Serial | 1221 | ||
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