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Author Debora Gil; Guillermo Torres; Carles Sanchez edit  openurl
  Title Transforming radiomic features into radiological words Type Conference Article
  Year 2023 Publication (down) IEEE International Symposium on Biomedical Imaging Abbreviated Journal  
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  Address Cartagena de Indias; Colombia; April 2023  
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  Area Expedition Conference ISBI  
  Notes IAM Approved no  
  Call Number Admin @ si @ GTS2023 Serial 3952  
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Author Pau Cano; Debora Gil; Eva Musulen edit  openurl
  Title Towards automatic detection of helicobacter pylori in histological samples of gastric tissue Type Conference Article
  Year 2023 Publication (down) IEEE International Symposium on Biomedical Imaging Abbreviated Journal  
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  Address Cartagena de Indias; Colombia; April 2023  
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  Notes IAM Approved no  
  Call Number Admin @ si @ CGM2023 Serial 3953  
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Author Guillermo Torres; Debora Gil; Antonio Rosell; Sonia Baeza; Carles Sanchez edit  openurl
  Title A radiomic biopsy for virtual histology of pulmonary nodules Type Conference Article
  Year 2023 Publication (down) IEEE International Symposium on Biomedical Imaging Abbreviated Journal  
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  Abstract Pòster  
  Address Cartagena de Indias; Colombia; April 2023  
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  Area Expedition Conference ISBI  
  Notes IAM Approved no  
  Call Number Admin @ si @ TGR2023b Serial 3954  
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Author Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang edit  url
doi  openurl
  Title Burst Perception-Distortion Tradeoff: Analysis and Evaluation Type Conference Article
  Year 2023 Publication (down) IEEE International Conference on Acoustics, Speech and Signal Processing Abbreviated Journal  
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  Abstract Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment.  
  Address Rodhes Islands; Greece; June 2023  
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  Area Expedition Conference ICASSP  
  Notes CIC; MACO Approved no  
  Call Number Admin @ si @ XHV2023 Serial 3909  
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Author Yifan Wang; Luka Murn; Luis Herranz; Fei Yang; Marta Mrak; Wei Zhang; Shuai Wan; Marc Gorriz Blanch edit  url
doi  openurl
  Title Efficient Super-Resolution for Compression Of Gaming Videos Type Conference Article
  Year 2023 Publication (down) IEEE International Conference on Acoustics, Speech and Signal Processing Abbreviated Journal  
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  Abstract Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming videos at low bitrates. Most state-of-the-art super-resolution networks generalize over a variety of RGB inputs and use a unified network architecture for frames of different levels of degradation, leading to high complexity and redundancy. Since games usually consist of a limited number of fixed scenarios, we specialize one model for each scenario and assign appropriate network capacities for different QPs to perform super-resolution under the guidance of reconstructed high-quality luma components. Experimental results show that our framework achieves a superior quality-complexity trade-off compared to the ESRnet baseline, saving at most 93.59% parameters while maintaining comparable performance. The compression efficiency compared to HEVC is also improved by more than 17% BD-rate gain.  
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  Notes LAMP; MACO Approved no  
  Call Number Admin @ si @ WMH2023 Serial 3911  
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Author Mingyi Yang; Luis Herranz; Fei Yang; Luka Murn; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang; Marta Mrak edit  url
doi  openurl
  Title Semantic Preprocessor for Image Compression for Machines Type Conference Article
  Year 2023 Publication (down) IEEE International Conference on Acoustics, Speech and Signal Processing Abbreviated Journal  
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  Abstract Visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. To achieve a better trade-off between the accuracy of the machine analysis task and bitrate, we propose leveraging pre-extracted semantic information to improve the preprocessor’s ability to accurately identify and filter out task-irrelevant information. Furthermore, we propose a two-part loss function to optimize the preprocessor, consisted of a rate-task performance loss and a semantic distillation loss, which helps the reconstructed image obtain more information that contributes to the accuracy of the task. Experiments show that the proposed preprocessor can save up to 48.83% bitrate compared with the method without the preprocessor, and save up to 36.24% bitrate compared to existing preprocessors for machine vision.  
  Address Rodhes Islands; Greece; June 2023  
  Corporate Author Thesis  
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  Area Expedition Conference ICASSP  
  Notes MACO; LAMP Approved no  
  Call Number Admin @ si @ YHY2023 Serial 3912  
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Author Lei Kang; Lichao Zhang; Dazhi Jiang edit  url
doi  openurl
  Title Learning Robust Self-Attention Features for Speech Emotion Recognition with Label-Adaptive Mixup Type Conference Article
  Year 2023 Publication (down) IEEE International Conference on Acoustics, Speech and Signal Processing Abbreviated Journal  
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  Abstract Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods.  
  Address Rodhes Islands; Greece; June 2023  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference ICASSP  
  Notes LAMP Approved no  
  Call Number Admin @ si @ KZJ2023 Serial 3984  
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Author Cristhian A. Aguilera-Carrasco; Luis Felipe Gonzalez-Böhme; Francisco Valdes; Francisco Javier Quitral Zapata; Bogdan Raducanu edit  doi
openurl 
  Title A Hand-Drawn Language for Human–Robot Collaboration in Wood Stereotomy Type Journal Article
  Year 2023 Publication (down) IEEE Access Abbreviated Journal ACCESS  
  Volume 11 Issue Pages 100975 - 100985  
  Keywords  
  Abstract This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ AGV2023 Serial 3969  
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Author Armin Mehri; Parichehr Behjati; Angel Sappa edit  url
openurl 
  Title TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution Type Journal Article
  Year 2023 Publication (down) IEEE Access Abbreviated Journal ACCESS  
  Volume 11 Issue Pages 11529-11540  
  Keywords  
  Abstract Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient.  
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  Notes MSIAU Approved no  
  Call Number Admin @ si @ MBS2023 Serial 3876  
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Author Armin Mehri; Parichehr Behjati; Dario Carpio; Angel Sappa edit  url
doi  openurl
  Title SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution Type Journal Article
  Year 2023 Publication (down) IEEE Access Abbreviated Journal ACCESS  
  Volume 11 Issue Pages  
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  Abstract Recent breakthroughs in single image super resolution have investigated the potential of deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs based models suffer from their limited fields and their inability to adapt to the input content. Recently, Transformer based models were presented, which demonstrated major performance gains in Natural Language Processing and Vision tasks while mitigating the drawbacks of CNNs. Nevertheless, Transformer computational complexity can increase quadratically for high-resolution images, and the fact that it ignores the original structures of the image by converting them to the 1D structure can make it problematic to capture the local context information and adapt it for real-time applications. In this paper, we present, SRFormer, an efficient yet powerful Transformer-based architecture, by making several key designs in the building of Transformer blocks and Transformer layers that allow us to consider the original structure of the image (i.e., 2D structure) while capturing both local and global dependencies without raising computational demands or memory consumption. We also present a Gated Multi-Layer Perceptron (MLP) Feature Fusion module to aggregate the features of different stages of Transformer blocks by focusing on inter-spatial relationships while adding minor computational costs to the network. We have conducted extensive experiments on several super-resolution benchmark datasets to evaluate our approach. SRFormer demonstrates superior performance compared to state-of-the-art methods from both Transformer and Convolutional networks, with an improvement margin of 0.1∼0.53dB . Furthermore, while SRFormer has almost the same model size, it outperforms SwinIR by 0.47% and inference time by half the time of SwinIR. The code will be available on GitHub.  
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  Notes MSIAU Approved no  
  Call Number Admin @ si @ MBC2023 Serial 3887  
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Author Jose Luis Gomez edit  openurl
  Title Synth-to-real semi-supervised learning for visual tasks Type Book Whole
  Year 2023 Publication (down) Going beyond Classification Problems for the Continual Learning of Deep Neural Networks Abbreviated Journal  
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  Abstract The curse of data labeling is a costly bottleneck in supervised deep learning, where large amounts of labeled data are needed to train intelligent systems. In onboard perception for autonomous driving, this cost corresponds to the labeling of raw data from sensors such as cameras, LiDARs, RADARs, etc. Therefore, synthetic data with automatically generated ground truth (labels) has aroused as a reliable alternative for training onboard perception models.
However, synthetic data commonly suffers from synth-to-real domain shift, i.e., models trained on the synthetic domain do not show their achievable accuracy when performing in the real world. This shift needs to be addressed by techniques falling in the realm of domain adaptation (DA).
The semi-supervised learning (SSL) paradigm can be followed to address DA. In this case, a model is trained using source data with labels (here synthetic) and leverages minimal knowledge from target data (here the real world) to generate pseudo-labels. These pseudo-labels help the training process to reduce the gap between the source and the target domains. In general, we can assume accessing both, pseudo-labels and a few amounts of human-provided labels for the target-domain data. However, the most interesting and challenging setting consists in assuming that we do not have human-provided labels at all. This setting is known as unsupervised domain adaptation (UDA). This PhD focuses on applying SSL to the UDA setting, for onboard visual tasks related to autonomous driving. We start by addressing the synth-to-real UDA problem on onboard vision-based object detection (pedestrians and cars), a critical task for autonomous driving and driving assistance. In particular, we propose to apply an SSL technique known as co-training, which we adapt to work with deep models that process a multi-modal input. The multi-modality consists of the visual appearance of the images (RGB) and their monocular depth estimation. The synthetic data we use as the source domain contains both, object bounding boxes and depth information. This prior knowledge is the
starting point for the co-training technique, which iteratively labels unlabeled real-world data and uses such pseudolabels (here bounding boxes with an assigned object class) to progressively improve the labeling results. Along this
process, two models collaborate to automatically label the images, in a way that one model compensates for the errors of the other, so avoiding error drift. While this automatic labeling process is done offline, the resulting pseudolabels can be used to train object detection models that must perform in real-time onboard a vehicle. We show that multi-modal co-training improves the labeling results compared to single-modal co-training, remaining competitive compared to human labeling.
Given the success of co-training in the context of object detection, we have also adapted this technique to a more crucial and challenging visual task, namely, onboard semantic segmentation. In fact, providing labels for a single image
can take from 30 to 90 minutes for a human labeler, depending on the content of the image. Thus, developing automatic labeling techniques for this visual task is of great interest to the automotive industry. In particular, the new co-training framework addresses synth-to-real UDA by an initial stage of self-training. Intermediate models arising from this stage are used to start the co-training procedure, for which we have elaborated an accurate collaboration policy between the two models performing the automatic labeling. Moreover, our co-training seamlessly leverages datasets from different synthetic domains. In addition, the co-training procedure is agnostic to the loss function used to train the semantic segmentation models which perform the automatic labeling. We achieve state-of-the-art results on publicly available benchmark datasets, again, remaining competitive compared to human labeling.
Finally, on the ground of our previous experience, we have designed and implemented a new SSL technique for UDA in the context of visual semantic segmentation. In this case, we mimic the labeling methodology followed by human labelers. In particular, rather than labeling full images at a time, categories of semantic classes are defined and only those are labeled in a labeling pass. In fact, different human labelers can become specialists in labeling different categories. Afterward, these per-category-labeled layers are combined to provide fully labeled images. Our technique is inspired by this methodology since we perform synth-to-real UDA per category, using the self-training stage previously developed as part of our co-training framework. The pseudo-labels obtained for each category are finally
fused to obtain fully automatically labeled images. In this context, we have also contributed to the development of a new photo-realistic synthetic dataset based on path-tracing rendering. Our new SSL technique seamlessly leverages publicly available synthetic datasets as well as this new one to obtain state-of-the-art results on synth-to-real UDA for semantic segmentation. We show that the new dataset allows us to reach better labeling accuracy than previously existing datasets, at the same time that it complements well them when combined. Moreover, we also show that the new human-inspired SSL technique outperforms co-training.		  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher IMPRIMA Place of Publication Editor Antonio Lopez  
  Language Summary Language Original Title  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ Gom2023 Serial 3961  
Permanent link to this record
 
 
Author Olivier Penacchio; Xavier Otazu; Arnold J Wilkings; Sara M. Haigh edit  url
openurl 
  Title A mechanistic account of visual discomfort Type Journal Article
  Year 2023 Publication (down) Frontiers in Neuroscience Abbreviated Journal FN  
  Volume 17 Issue Pages  
  Keywords  
  Abstract Much of the neural machinery of the early visual cortex, from the extraction of local orientations to contextual modulations through lateral interactions, is thought to have developed to provide a sparse encoding of contour in natural scenes, allowing the brain to process efficiently most of the visual scenes we are exposed to. Certain visual stimuli, however, cause visual stress, a set of adverse effects ranging from simple discomfort to migraine attacks, and epileptic seizures in the extreme, all phenomena linked with an excessive metabolic demand. The theory of efficient coding suggests a link between excessive metabolic demand and images that deviate from natural statistics. Yet, the mechanisms linking energy demand and image spatial content in discomfort remain elusive. Here, we used theories of visual coding that link image spatial structure and brain activation to characterize the response to images observers reported as uncomfortable in a biologically based neurodynamic model of the early visual cortex that included excitatory and inhibitory layers to implement contextual influences. We found three clear markers of aversive images: a larger overall activation in the model, a less sparse response, and a more unbalanced distribution of activity across spatial orientations. When the ratio of excitation over inhibition was increased in the model, a phenomenon hypothesised to underlie interindividual differences in susceptibility to visual discomfort, the three markers of discomfort progressively shifted toward values typical of the response to uncomfortable stimuli. Overall, these findings propose a unifying mechanistic explanation for why there are differences between images and between observers, suggesting how visual input and idiosyncratic hyperexcitability give rise to abnormal brain responses that result in visual stress.  
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  Notes NEUROBIT Approved no  
  Call Number Admin @ si @ POW2023 Serial 3886  
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Author Zahra Raisi-Estabragh; Carlos Martin-Isla; Louise Nissen; Liliana Szabo; Victor M. Campello; Sergio Escalera; Simon Winther; Morten Bottcher; Karim Lekadir; and Steffen E. Petersen edit  url
openurl 
  Title Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset Type Journal Article
  Year 2023 Publication (down) Frontiers in Cardiovascular Medicine Abbreviated Journal FCM  
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  Notes HUPBA Approved no  
  Call Number Admin @ si @ RMN2023 Serial 3937  
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Author Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva edit  url
openurl 
  Title Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN Type Journal Article
  Year 2023 Publication (down) Food Control Abbreviated Journal FC  
  Volume 147 Issue Pages 109554  
  Keywords  
  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 Alejandro Ariza-Casabona; Bartlomiej Twardowski; Tri Kurniawan Wijaya edit  url
openurl 
  Title Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation Type Conference Article
  Year 2023 Publication (down) European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval Abbreviated Journal  
  Volume 13980 Issue Pages 49–65  
  Keywords  
  Abstract Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method.  
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  Series Editor Series Title Abbreviated Series Title LNCS  
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  ISSN ISBN Medium  
  Area Expedition Conference ECIR  
  Notes LAMP Approved no  
  Call Number Admin @ si @ ATK2023 Serial 3933  
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