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| Author | Hector Laria Mantecon; Kai Wang; Joost Van de Weijer; Bogdan Raducanu; Kai Wang | ||||
| Title | NeRF-Diffusion for 3D-Consistent Face Generation and Editing | Type | Conference Article | ||
| Year | 2024 | Publication | 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
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| Abstract | Generating high-fidelity 3D-aware images without 3D supervision is a valuable capability in various applications. Current methods based on NeRF features, SDF information, or triplane features have limited variation after training. To address this, we propose a novel approach that combines pretrained models for shape and content generation. Our method leverages a pretrained Neural Radiance Field as a shape prior and a diffusion model for content generation. By conditioning the diffusion model with 3D features, we enhance its ability to generate novel views with 3D awareness. We introduce a consistency token shared between the NeRF module and the diffusion model to maintain 3D consistency during sampling. Moreover, our framework allows for text editing of 3D-aware image generation, enabling users to modify the style over 3D views while preserving semantic content. Our contributions include incorporating 3D awareness into a text-to-image model, addressing identity consistency in 3D view synthesis, and enabling text editing of 3D-aware image generation. We provide detailed explanations, including the shape prior based on the NeRF model and the content generation process using the diffusion model. We also discuss challenges such as shape consistency and sampling saturation. Experimental results demonstrate the effectiveness and visual quality of our approach. | ||||
| Address | Roma; Italia; February 2024 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | LAMP | Approved  |
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| Call Number | Admin @ si @ LWW2024 | Serial | 4003 | ||
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| Author | Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera | ||||
| Title | Deep learning with self-supervision and uncertainty regularization to count fish in underwater images | Type | Journal Article | ||
| Year | 2022 | Publication | PloS One | Abbreviated Journal | Plos |
| Volume | 17 | Issue | 5 | Pages | e0267759 |
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| Abstract | Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. | ||||
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| Publisher | Public Library of Science | Place of Publication | Editor | ||
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| Notes | HuPBA | Approved |
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| Call Number | Admin @ si @ TCC2022 | Serial | 3743 | ||
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| 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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| Notes | ISE; 600.157; 600.168 | Approved |
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| Call Number | Admin @ si @ WCS2022 | Serial | 3744 | ||
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| Author | Lu Yu; Xialei Liu; Joost Van de Weijer | ||||
| Title | Self-Training for Class-Incremental Semantic Segmentation | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | Issue | Pages | |||
| Keywords | Class-incremental learning; Self-training; Semantic segmentation. | ||||
| Abstract | In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods. | ||||
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| Notes | LAMP; 600.147; 611.008; | Approved |
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| Call Number | Admin @ si @ YLW2022 | Serial | 3745 | ||
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| Author | Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil | ||||
| Title | EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment | Type | Journal Article | ||
| Year | 2024 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 24 | Issue | 4 | Pages | 1174 |
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| Abstract | High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. | ||||
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| Notes | IAM | Approved |
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| Call Number | Admin @ si @ HYF2024 | Serial | 4019 | ||
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| Author | Victor M. Campello; Carlos Martin-Isla; Cristian Izquierdo; Andrea Guala; Jose F. Rodriguez Palomares; David Vilades; Martin L. Descalzo; Mahir Karakas; Ersin Cavus; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Karim Lekadir | ||||
| Title | Minimising multi-centre radiomics variability through image normalisation: a pilot study | Type | Journal Article | ||
| Year | 2022 | Publication | Scientific Reports | Abbreviated Journal | ScR |
| Volume | 12 | Issue | 1 | Pages | 12532 |
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| Abstract | Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification. | ||||
| Address | 2022/07/22 | ||||
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| Publisher | Springer Nature | Place of Publication | Editor | ||
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| Notes | HuPBA | Approved |
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| Call Number | Admin @ si @ CMI2022 | Serial | 3749 | ||
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| Author | Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach | ||||
| Title | In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy | Type | Journal Article | ||
| Year | 2022 | Publication | Endoscopy International Open | Abbreviated Journal | ENDIO |
| Volume | 10 | Issue | 9 | Pages | E1201-E1207 |
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| Abstract | Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions. | ||||
| Address | 2022 Sep 14 | ||||
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| Publisher | PMID | Place of Publication | Editor | ||
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| Notes | ISE; 600.157 | Approved |
no | ||
| Call Number | Admin @ si @ GTC2022b | Serial | 3752 | ||
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| Author | Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey | ||||
| Title | Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Archaeological Science | Abbreviated Journal | JArchSci |
| Volume | 148 | Issue | Pages | 105654 | |
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| Abstract | This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. | ||||
| Address | December 2022 | ||||
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| Notes | MSIAU; MACO; 600.167 | Approved |
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| Call Number | Admin @ si @ BOL2022 | Serial | 3753 | ||
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| Author | Arnau Baro | ||||
| Title | Reading Music Systems: From Deep Optical Music Recognition to Contextual Methods | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The transcription of sheet music into some machine-readable format can be carried out manually. However, the complexity of music notation inevitably leads to burdensome software for music score editing, which makes the whole process
very time-consuming and prone to errors. Consequently, automatic transcription systems for musical documents represent interesting tools. Document analysis is the subject that deals with the extraction and processing of documents through image and pattern recognition. It is a branch of computer vision. Taking music scores as source, the field devoted to address this task is known as Optical Music Recognition (OMR). Typically, an OMR system takes an image of a music score and automatically extracts its content into some symbolic structure such as MEI or MusicXML. In this dissertation, we have investigated different methods for recognizing a single staff section (e.g. scores for violin, flute, etc.), much in the same way as most text recognition research focuses on recognizing words appearing in a given line image. These methods are based in two different methodologies. On the one hand, we present two methods based on Recurrent Neural Networks, in particular, the Long Short-Term Memory Neural Network. On the other hand, a method based on Sequence to Sequence models is detailed. Music context is needed to improve the OMR results, just like language models and dictionaries help in handwriting recognition. For example, syntactical rules and grammars could be easily defined to cope with the ambiguities in the rhythm. In music theory, for example, the time signature defines the amount of beats per bar unit. Thus, in the second part of this dissertation, different methodologies have been investigated to improve the OMR recognition. We have explored three different methods: (a) a graphic tree-structure representation, Dendrograms, that joins, at each level, its primitives following a set of rules, (b) the incorporation of Language Models to model the probability of a sequence of tokens, and (c) graph neural networks to analyze the music scores to avoid meaningless relationships between music primitives. Finally, to train all these methodologies, and given the method-specificity of the datasets in the literature, we have created four different music datasets. Two of them are synthetic with a modern or old handwritten appearance, whereas the other two are real handwritten scores, being one of them modern and the other old. |
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| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Alicia Fornes | |
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | 978-84-124793-8-6 | Medium | ||
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| Notes | DAG; | Approved |
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| Call Number | Admin @ si @ Bar2022 | Serial | 3754 | ||
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| Author | Ali Furkan Biten | ||||
| Title | A Bitter-Sweet Symphony on Vision and Language: Bias and World Knowledge | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Vision and Language are broadly regarded as cornerstones of intelligence. Even though language and vision have different aims – language having the purpose of communication, transmission of information and vision having the purpose of constructing mental representations around us to navigate and interact with objects – they cooperate and depend on one another in many tasks we perform effortlessly. This reliance is actively being studied in various Computer Vision tasks, e.g. image captioning, visual question answering, image-sentence retrieval, phrase grounding, just to name a few. All of these tasks share the inherent difficulty of the aligning the two modalities, while being robust to language
priors and various biases existing in the datasets. One of the ultimate goal for vision and language research is to be able to inject world knowledge while getting rid of the biases that come with the datasets. In this thesis, we mainly focus on two vision and language tasks, namely Image Captioning and Scene-Text Visual Question Answering (STVQA). In both domains, we start by defining a new task that requires the utilization of world knowledge and in both tasks, we find that the models commonly employed are prone to biases that exist in the data. Concretely, we introduce new tasks and discover several problems that impede performance at each level and provide remedies or possible solutions in each chapter: i) We define a new task to move beyond Image Captioning to Image Interpretation that can utilize Named Entities in the form of world knowledge. ii) We study the object hallucination problem in classic Image Captioning systems and develop an architecture-agnostic solution. iii) We define a sub-task of Visual Question Answering that requires reading the text in the image (STVQA), where we highlight the limitations of current models. iv) We propose an architecture for the STVQA task that can point to the answer in the image and show how to combine it with classic VQA models. v) We show how far language can get us in STVQA and discover yet another bias which causes the models to disregard the image while doing Visual Question Answering. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Dimosthenis Karatzas;Lluis Gomez | |
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-124793-5-5 | Medium | ||
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| Notes | DAG | Approved |
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| Call Number | Admin @ si @ Bit2022 | Serial | 3755 | ||
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| Author | Andres Mafla | ||||
| Title | Leveraging Scene Text Information for Image Interpretation | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Until recently, most computer vision models remained illiterate, largely ignoring the semantically rich and explicit information contained in scene text. Recent progress in scene text detection and recognition has recently allowed exploring its role in a diverse set of open computer vision problems, e.g. image classification, image-text retrieval, image captioning, and visual question answering to name a few. The explicit semantics of scene text closely requires specific modeling similar to language. However, scene text is a particular signal that has to be interpreted according to a comprehensive perspective that encapsulates all the visual cues in an image. Incorporating this information is a straightforward task for humans, but if we are unfamiliar with a language or scripture, achieving a complete world understanding is impossible (e.a. visiting a foreign country with a different alphabet). Despite the importance of scene text, modeling it requires considering the several ways in which scene text interacts with an image, processing and fusing an additional modality. In this thesis, we mainly focus
on two tasks, scene text-based fine-grained image classification, and cross-modal retrieval. In both studied tasks we identify existing limitations in current approaches and propose plausible solutions. Concretely, in each chapter: i) We define a compact way to embed scene text that generalizes to unseen words at training time while performing in real-time. ii) We incorporate the previously learned scene text embedding to create an image-level descriptor that overcomes optical character recognition (OCR) errors which is well-suited to the fine-grained image classification task. iii) We design a region-level reasoning network that learns the interaction through semantics among salient visual regions and scene text instances. iv) We employ scene text information in image-text matching and introduce the Scene Text Aware Cross-Modal retrieval StacMR task. We gather a dataset that incorporates scene text and design a model suited for the newly studied modality. v) We identify the drawbacks of current retrieval metrics in cross-modal retrieval. An image captioning metric is proposed as a way of better evaluating semantics in retrieved results. Ample experimentation shows that incorporating such semantics into a model yields better semantic results while requiring significantly less data to converge. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Dimosthenis Karatzas;Lluis Gomez | |
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-124793-6-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved |
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| Call Number | Admin @ si @ Maf2022 | Serial | 3756 | ||
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| Author | Mohamed Ali Souibgui | ||||
| Title | Document Image Enhancement and Recognition in Low Resource Scenarios: Application to Ciphers and Handwritten Text | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In this thesis, we propose different contributions with the goal of enhancing and recognizing historical handwritten document images, especially the ones with rare scripts, such as cipher documents.
In the first part, some effective end-to-end models for Document Image Enhancement (DIE) using deep learning models were presented. First, Generative Adversarial Networks (cGAN) for different tasks (document clean-up, binarization, deblurring, and watermark removal) were explored. Next, we further improve the results by recovering the degraded document images into a clean and readable form by integrating a text recognizer into the cGAN model to promote the generated document image to be more readable. Afterward, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The second part of the thesis addresses Handwritten Text Recognition (HTR) in low resource scenarios, i.e. when only few labeled training data is available. We propose novel methods for recognizing ciphers with rare scripts. First, a few-shot object detection based method was proposed. Then, we incorporate a progressive learning strategy that automatically assignspseudo-labels to a set of unlabeled data to reduce the human labor of annotating few pages while maintaining the good performance of the model. Secondly, a data generation technique based on Bayesian Program Learning (BPL) is proposed to overcome the lack of data in such rare scripts. Thirdly, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE). This latter self-supervised model is designed to tackle two tasks, text recognition and document image enhancement. The proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time, it requires substantially fewer data samples to converge. In the third part of the thesis, we analyze, from the user perspective, the usage of HTR systems in low resource scenarios. This contrasts with the usual research on HTR, which often focuses on technical aspects only and rarely devotes efforts on implementing software tools for scholars in Humanities. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Alicia Fornes;Yousri Kessentini | |
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| ISSN | ISBN | 978-84-124793-8-6 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved |
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| Call Number | Admin @ si @ Sou2022 | Serial | 3757 | ||
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| Author | Danna Xue; Fei Yang; Pei Wang; Luis Herranz; Jinqiu Sun; Yu Zhu; Yanning Zhang | ||||
| Title | SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision | Type | Conference Article | ||
| Year | 2022 | Publication | 30th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 6539-6548 | ||
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| Abstract | Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework. | ||||
| Address | Lisboa, Portugal, October 2022 | ||||
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| Publisher | Association for Computing Machinery | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-9203-7 | Medium | ||
| Area | Expedition | Conference | MM | ||
| Notes | MACO; 600.161; 601.400 | Approved |
no | ||
| Call Number | Admin @ si @ XYW2022 | Serial | 3758 | ||
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| Author | Sonia Baeza; Debora Gil; I.Garcia Olive; M.Salcedo; J.Deportos; Carles Sanchez; Guillermo Torres; G.Moragas; Antoni Rosell | ||||
| Title | A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients | Type | Journal Article | ||
| Year | 2022 | Publication | EJNMMI Physics | Abbreviated Journal | EJNMMI-PHYS |
| Volume | 9 | Issue | 1, Article 84 | Pages | 1-17 |
| Keywords | |||||
| Abstract | Background: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans.
Methods: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classifcation neural network that optimizes a weighted crossentropy loss trained to discriminate between three diferent types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using diferent confguration of parameters were tested. Results: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining diferent types of image patterns with PE presented a sensitivity, specifcity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specifcity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia. Conclusion: This radiomic diagnostic system was able to identify the diferent lung imaging patterns and is a frst step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. |
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| Address | 5 dec 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM | Approved |
no | ||
| Call Number | Admin @ si @ BGG2022 | Serial | 3759 | ||
| Permanent link to this record | |||||
| Author | David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina | ||||
| Title | A Survey of FPGA-Based Vision Systems for Autonomous Cars | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACESS |
| Volume | 10 | Issue | Pages | 132525-132563 | |
| Keywords | Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures | ||||
| Abstract | On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges. | ||||
| Address | 16 December 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM; 600.166 | Approved |
no | ||
| Call Number | Admin @ si @ CNB2022 | Serial | 3760 | ||
| Permanent link to this record | |||||