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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 | Vacit Oguz Yazici | ||||
Title | Towards Smart Fashion: Visual Recognition of Products and Attributes | Type | Book Whole | ||
Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Artificial intelligence is innovating the fashion industry by proposing new applications and solutions to the problems encountered by researchers and engineers working in the industry. In this thesis, we address three of these problems. In the first part of the thesis, we tackle the problem of multi-label image classification which is very related to fashion attribute recognition. In the second part of the thesis, we address two problems that are specific to fashion. Firstly, we address the problem of main product detection which is the task of associating correct image parts (e.g. bounding boxes) with the fashion product being sold. Secondly, we address the problem of color naming for multicolored fashion items. The task of multi-label image classification consists in assigning various concepts such as objects or attributes to images. Usually, there are dependencies that can be learned between the concepts to capture label correlations (chair and table classes are more likely to co-exist than chair and giraffe).
If we treat the multi-label image classification problem as an orderless set prediction problem, we can exploit recurrent neural networks (RNN) to capture label correlations. However, RNNs are trained to predict ordered sequences of tokens, so if the order of the predicted sequence is different than the order of the ground truth sequence, there will be penalization although the predictions are correct. Therefore, in the first part of the thesis, we propose an orderless loss function which will order the labels in the ground truth sequence dynamically in a way that the minimum loss is achieved. This results in a significant improvement of RNN models on multi-label image classification over the previous methods. However, RNNs suffer from long term dependencies when the cardinality of set grows bigger. The decoding process might stop early if the current hidden state cannot find any object and outputs the termination token. This would cause the remaining classes not to be predicted and lower recall metric. Transformers can be used to avoid the long term dependency problem exploiting their selfattention modules that process sequential data simultaneously. Consequently, we propose a novel transformer model for multi-label image classification which surpasses the state-of-the-art results by a large margin. In the second part of thesis, we focus on two fashion-specific problems. Main product detection is the task of associating image parts with the fashion product that is being sold, generally using associated textual metadata (product title or description). Normally, in fashion e-commerces, products are represented by multiple images where a person wears the product along with other fashion items. If all the fashion items in the images are marked with bounding boxes, we can use the textual metadata to decide which item is the main product. The initial work treated each of these images independently, discarding the fact that they all belong to the same product. In this thesis, we represent the bounding boxes from all the images as nodes in a fully connected graph. This allows the algorithm to learn relations between the nodes during training and take the entire context into account for the final decision. Our algorithm results in a significant improvement of the state-ofthe-art. Moreover, we address the problem of color naming for multicolored fashion items, which is a challenging task due to the external factors such as illumination changes or objects that act as clutter. In the context of multi-label classification, the vaguely defined lines between the classes in the color space cause ambiguity. For example, a shade of blue which is very close to green might cause the model to incorrectly predict the color blue and green at the same time. Based on this, models trained for color naming are expected to recognize the colors and their quantities in both single colored and multicolored fashion items. Therefore, in this thesis, we propose a novel architecture with an additional head that explicitly estimates the number of colors in fashion items. This removes the ambiguity problem and results in better color naming performance. |
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Address | January 2022 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Joost Van de Weijer;Arnau Ramisa | |
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ISSN | ISBN | 978-84-122714-6-1 | Medium | ||
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Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ Ogu2022 | Serial | 3631 | ||
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Author | Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez | ||||
Title | End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data | Type | Journal Article | ||
Year | 2022 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
Volume | 16 | Issue | 1 | Pages | 50-66 |
Keywords | Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation | ||||
Abstract | Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. | ||||
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Notes | HUPBA; ISE; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ MEB2022 | Serial | 3652 | ||
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Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
Title | Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD | Type | Journal | ||
Year | 2022 | Publication | Journal of Ambient Intelligence and Humanized Computing | Abbreviated Journal | |
Volume | 13 | Issue | Pages | 591–611 | |
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Abstract | One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ RKE2022a | Serial | 3660 | ||
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Author | Fei Yang; Yaxing Wang; Luis Herranz; Yongmei Cheng; Mikhail Mozerov | ||||
Title | A Novel Framework for Image-to-image Translation and Image Compression | Type | Journal Article | ||
Year | 2022 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 508 | Issue | Pages | 58-70 | |
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Abstract | Data-driven paradigms using machine learning are becoming ubiquitous in image processing and communications. In particular, image-to-image (I2I) translation is a generic and widely used approach to image processing problems, such as image synthesis, style transfer, and image restoration. At the same time, neural image compression has emerged as a data-driven alternative to traditional coding approaches in visual communications. In this paper, we study the combination of these two paradigms into a joint I2I compression and translation framework, focusing on multi-domain image synthesis. We first propose distributed I2I translation by integrating quantization and entropy coding into an I2I translation framework (i.e. I2Icodec). In practice, the image compression functionality (i.e. autoencoding) is also desirable, requiring to deploy alongside I2Icodec a regular image codec. Thus, we further propose a unified framework that allows both translation and autoencoding capabilities in a single codec. Adaptive residual blocks conditioned on the translation/compression mode provide flexible adaptation to the desired functionality. The experiments show promising results in both I2I translation and image compression using a single model. | ||||
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Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ YWH2022 | Serial | 3679 | ||
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Author | Alex Gomez-Villa; Adrian Martin; Javier Vazquez; Marcelo Bertalmio; Jesus Malo | ||||
Title | On the synthesis of visual illusions using deep generative models | Type | Journal Article | ||
Year | 2022 | Publication | Journal of Vision | Abbreviated Journal | JOV |
Volume | 22(8) | Issue | 2 | Pages | 1-18 |
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Abstract | Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference. | ||||
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Notes | LAMP; 600.161; 611.007 | Approved | no | ||
Call Number | Admin @ si @ GMV2022 | Serial | 3682 | ||
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Author | Yasuko Sugito; Javier Vazquez; Trevor Canham; Marcelo Bertalmio | ||||
Title | Image quality evaluation in professional HDR/WCG production questions the need for HDR metrics | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 31 | Issue | Pages | 5163 - 5177 | |
Keywords | Measurement; Image color analysis; Image coding; Production; Dynamic range; Brightness; Extraterrestrial measurements | ||||
Abstract | In the quality evaluation of high dynamic range and wide color gamut (HDR/WCG) images, a number of works have concluded that native HDR metrics, such as HDR visual difference predictor (HDR-VDP), HDR video quality metric (HDR-VQM), or convolutional neural network (CNN)-based visibility metrics for HDR content, provide the best results. These metrics consider only the luminance component, but several color difference metrics have been specifically developed for, and validated with, HDR/WCG images. In this paper, we perform subjective evaluation experiments in a professional HDR/WCG production setting, under a real use case scenario. The results are quite relevant in that they show, firstly, that the performance of HDR metrics is worse than that of a classic, simple standard dynamic range (SDR) metric applied directly to the HDR content; and secondly, that the chrominance metrics specifically developed for HDR/WCG imaging have poor correlation with observer scores and are also outperformed by an SDR metric. Based on these findings, we show how a very simple framework for creating color HDR metrics, that uses only luminance SDR metrics, transfer functions, and classic color spaces, is able to consistently outperform, by a considerable margin, state-of-the-art HDR metrics on a varied set of HDR content, for both perceptual quantization (PQ) and Hybrid Log-Gamma (HLG) encoding, luminance and chroma distortions, and on different color spaces of common use. | ||||
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Notes | 600.161; 611.007 | Approved | no | ||
Call Number | Admin @ si @ SVG2022 | Serial | 3683 | ||
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Author | Idoia Ruiz; Joan Serrat | ||||
Title | Hierarchical Novelty Detection for Traffic Sign Recognition | Type | Journal Article | ||
Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 22 | Issue | 12 | Pages | 4389 |
Keywords | Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision | ||||
Abstract | Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. | ||||
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Notes | ADAS; 600.154 | Approved | no | ||
Call Number | Admin @ si @ RuS2022 | Serial | 3684 | ||
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Author | Xavier Otazu; Xim Cerda-Company | ||||
Title | The contribution of luminance and chromatic channels to color assimilation | Type | Journal Article | ||
Year | 2022 | Publication | Journal of Vision | Abbreviated Journal | JOV |
Volume | 22(6) | Issue | 10 | Pages | 1-15 |
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Abstract | Color induction is the phenomenon where the physical and the perceived colors of an object differ owing to the color distribution and the spatial configuration of the surrounding objects. Previous works studying this phenomenon on the lsY MacLeod–Boynton color space, show that color assimilation is present only when the magnocellular pathway (i.e., the Y axis) is activated (i.e., when there are luminance differences). Concretely, the authors showed that the effect is mainly induced by the koniocellular pathway (s axis), but not by the parvocellular pathway (l axis), suggesting that when magnocellular pathway is activated it inhibits the koniocellular pathway. In the present work, we study whether parvo-, konio-, and magnocellular pathways may influence on each other through the color induction effect. Our results show that color assimilation does not depend on a chromatic–chromatic interaction, and that chromatic assimilation is driven by the interaction between luminance and chromatic channels (mainly the magno- and the koniocellular pathways). Our results also show that chromatic induction is greatly decreased when all three visual pathways are simultaneously activated, and that chromatic pathways could influence each other through the magnocellular (luminance) pathway. In addition, we observe that chromatic channels can influence the luminance channel, hence inducing a small brightness induction. All these results show that color induction is a highly complex process where interactions between the several visual pathways are yet unknown and should be studied in greater detail. | ||||
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Notes | Neurobit; 600.128; 600.120; 600.158 | Approved | no | ||
Call Number | Admin @ si @ OtC2022 | Serial | 3685 | ||
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Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution | Type | Journal Article | ||
Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 22 | Issue | 6 | Pages | 2254 |
Keywords | Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images | ||||
Abstract | This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. | ||||
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Notes | MSIAU; | Approved | no | ||
Call Number | Admin @ si @ RSV2022b | Serial | 3688 | ||
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Author | Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez | ||||
Title | Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition | Type | Journal Article | ||
Year | 2022 | Publication | ACM Transactions on Multimedia Computing, Communications, and Applications | Abbreviated Journal | ACMTMC |
Volume | Issue | Pages | |||
Keywords | Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition | ||||
Abstract | Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. | ||||
Address | May 2022 | ||||
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Notes | ISE; 600.157 | Approved | no | ||
Call Number | Admin @ si @ GYW2022 | Serial | 3692 | ||
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Author | David Berga; Xavier Otazu | ||||
Title | A neurodynamic model of saliency prediction in v1 | Type | Journal Article | ||
Year | 2022 | Publication | Neural Computation | Abbreviated Journal | NEURALCOMPUT |
Volume | 34 | Issue | 2 | Pages | 378-414 |
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Abstract | Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex. | ||||
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Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
Call Number | Admin @ si @ BeO2022 | Serial | 3696 | ||
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Author | Miquel Angel Piera; Jose Luis Muñoz; Debora Gil; Gonzalo Martin; Jordi Manzano | ||||
Title | A Socio-Technical Simulation Model for the Design of the Future Single Pilot Cockpit: An Opportunity to Improve Pilot Performance | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 10 | Issue | Pages | 22330-22343 | |
Keywords | Human factors ; Performance evaluation ; Simulation; Sociotechnical systems ; System performance | ||||
Abstract | The future deployment of single pilot operations must be supported by new cockpit computer services. Such services require an adaptive context-aware integration of technical functionalities with the concurrent tasks that a pilot must deal with. Advanced artificial intelligence supporting services and improved communication capabilities are the key enabling technologies that will render future cockpits more integrated with the present digitalized air traffic management system. However, an issue in the integration of such technologies is the lack of socio-technical analysis in the design of these teaming mechanisms. A key factor in determining how and when a service support should be provided is the dynamic evolution of pilot workload. This paper investigates how the socio-technical model-based systems engineering approach paves the way for the design of a digital assistant framework by formalizing this workload. The model was validated in an Airbus A-320 cockpit simulator, and the results confirmed the degraded pilot behavioral model and the performance impact according to different contextual flight deck information. This study contributes to practical knowledge for designing human-machine task-sharing systems. | ||||
Address | Feb 2022 | ||||
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Notes | IAM; | Approved | no | ||
Call Number | Admin @ si @ PMG2022 | Serial | 3697 | ||
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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 | Guillermo Torres; Sonia Baeza; Carles Sanchez; Ignasi Guasch; Antoni Rosell; Debora Gil | ||||
Title | An Intelligent Radiomic Approach for Lung Cancer Screening | Type | Journal Article | ||
Year | 2022 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
Volume | 12 | Issue | 3 | Pages | 1568 |
Keywords | Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization | ||||
Abstract | The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed
forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection. |
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Address | Jan 2022 | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ TBS2022 | Serial | 3699 | ||
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