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| Author | Jose Luis Gomez; Manuel Silva; Antonio Seoane; Agnes Borras; Mario Noriega; German Ros; Jose Antonio Iglesias; Antonio Lopez | ||||
| Title | All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (this http URL). | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ GSS2023 | Serial | 4015 | ||
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| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | A transformer model for boundary detection in continuous sign language | Type | Journal Article | ||
| Year | 2024 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
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| Abstract | Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundaries of isolated signs within a continuous video stream. Additionally, the reliance on handcrafted features in existing models poses a challenge to achieving optimal accuracy. To surmount these challenges, we propose a novel approach utilizing a Transformer-based model. Unlike traditional models, our approach focuses on enhancing accuracy while eliminating the need for handcrafted features. The Transformer model is employed for both ISLR and CSLR. The training process involves using isolated sign videos, where hand keypoint features extracted from the input video are enriched using the Transformer model. Subsequently, these enriched features are forwarded to the final classification layer. The trained model, coupled with a post-processing method, is then applied to detect isolated sign boundaries within continuous sign videos. The evaluation of our model is conducted on two distinct datasets, including both continuous signs and their corresponding isolated signs, demonstrates promising results. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RKE2024 | Serial | 4016 | ||
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| Author | Vacit Oguz Yazici; Longlong Yu; Arnau Ramisa; Luis Herranz; Joost Van de Weijer | ||||
| Title | Main product detection with graph networks for fashion | Type | Journal Article | ||
| Year | 2024 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 83 | Issue | Pages | 3215–3231 | |
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| Abstract | Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused on identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper, we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin. | ||||
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| Notes | LAMP; MACO; 600.147; 600.167; 600.164; 600.161; 600.141; 601.309 | Approved | no | ||
| Call Number | Admin @ si @ YYR2024 | Serial | 4017 | ||
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| Author | Javier Vazquez; Graham D. Finlayson; Luis Herranz | ||||
| Title | Improving the perception of low-light enhanced images | Type | Journal Article | ||
| Year | 2024 | Publication | Optics Express | Abbreviated Journal | |
| Volume | 32 | Issue | 4 | Pages | 5174-5190 |
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| Abstract | Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.e. perceptual quality). As an example, the perception-distortion trade-off states that, close to the optimal results, improving distortion results in worsening perception. This means that current low-light image enhancement methods —that focus on distortion minimization— cannot be optimal in the sense of obtaining a good image in terms of perception errors. In this paper, we propose a post-processing approach in which, given the original low-light image and the result of a specific method, we are able to obtain a result that resembles as much as possible that of the original method, but, at the same time, giving an improvement in the perception of the final image. More in detail, our method follows the hypothesis that in order to minimally modify the perception of an input image, any modification should be a combination of a local change in the shading across a scene and a global change in illumination color. We demonstrate the ability of our method quantitatively using perceptual blind image metrics such as BRISQUE, NIQE, or UNIQUE, and through user preference tests. | ||||
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| Notes | MACO | Approved | no | ||
| Call Number | Admin @ si @ VFH2024 | Serial | 4018 | ||
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| Author | Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang | ||||
| Title | Historical Cryptology | Type | Book Chapter | ||
| Year | 2024 | Publication | Learning and Experiencing Cryptography with CrypTool and SageMath | Abbreviated Journal | |
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| Abstract | Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ MFK2024 | Serial | 4020 | ||
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| Author | Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane | ||||
| Title | TopoX: A Suite of Python Packages for Machine Learning on Topological Domains | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ HPF2024 | Serial | 4021 | ||
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| Author | German Barquero; Sergio Escalera; Cristina Palmero | ||||
| Title | Seamless Human Motion Composition with Blended Positional Encodings | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BEP2024 | Serial | 4022 | ||
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| Author | Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal | ||||
| Title | GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BBL2024b | Serial | 4023 | ||
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| Author | Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang | ||||
| Title | Diffusion-based network for unsupervised landmark detection | Type | Journal Article | ||
| Year | 2024 | Publication | Knowledge-Based Systems | Abbreviated Journal | |
| Volume | 292 | Issue | Pages | 111627 | |
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| Abstract | Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ WWT2024 | Serial | 4024 | ||
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| Author | Fernando Vilariño; Enric Marti | ||||
| Title | New didactic techniques in the EHES applying mobile technologies | Type | Miscellaneous | ||
| Year | 2008 | Publication | Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya | Abbreviated Journal | |
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Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya | Thesis | |||
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| Area | Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya | Expedition | Conference | ||
| Notes | MILAB;IAM;MV;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ VIM2008 | Serial | 1664 | ||
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| Author | Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester | ||||
| Title | Multilocal Creaseness Measure | Type | Journal | ||
| Year | 2012 | Publication | The Insight Journal | Abbreviated Journal | IJ |
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| Keywords | Ridges, Valley, Creaseness, Structure Tensor, Skeleton, | ||||
| Abstract | This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission. | ||||
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Alma IT Systems | Thesis | |||
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| Language | english | Summary Language | english | Original Title | |
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| Notes | IAM;ADAS; | Approved | no | ||
| Call Number | IAM @ iam @ VGL2012 | Serial | 1840 | ||
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| Author | Francesc Tanarro Marquez; Pau Gratacos Marti; F. Javier Sanchez; Joan Ramon Jimenez Minguell; Coen Antens; Enric Sala i Esteva | ||||
| Title | A device for monitoring condition of a railway supply | Type | Patent | ||
| Year | 2012 | Publication | EP 2 404 777 A1 | Abbreviated Journal | |
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| Abstract | of a railway supply line when the supply line is in contact with a head of a pantograph of a vehicle in order to power said vehicle . The device includes a camera ( for monitoring parameters indicative of operating capability of said supply line.
The device is intended to monitor condition tive of operating capability of said supply line. The device includes a reflective element. comprising a pattern , intended to be arranged onto the pantograph head . The camera is intended to be arranged on the vehicle (10) so as to register the pattern position regarding a vertical direction. |
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ALSTOM Transport SA | Thesis | |||
| Publisher | European Patent Office | Place of Publication | Editor | ||
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| Notes | MV | Approved | no | ||
| Call Number | IAM @ iam @ MMS2012 | Serial | 1854 | ||
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| Author | David Roche; Debora Gil; Jesus Giraldo | ||||
| Title | An inference model for analyzing termination conditions of Evolutionary Algorithms | Type | Conference Article | ||
| Year | 2011 | Publication | 14th Congrès Català en Intel·ligencia Artificial | Abbreviated Journal | |
| Volume | Issue | Pages | 216-225 | ||
| Keywords | Evolutionary Computation Convergence, Termination Conditions, Statistical Inference | ||||
| Abstract | In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution. |
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| Address | Lleida, Catalonia (Spain) | ||||
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Associació Catalana Intel·ligència Artificial | Thesis | |||
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| ISSN | ISBN | 978-1-60750-841-0 | Medium | ||
| Area | Expedition | Conference | CCIA | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ RGG2011a | Serial | 1677 | ||
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| Author | Debora Gil; Agnes Borras; Manuel Ballester; Francesc Carreras; Ruth Aris; Manuel Vazquez; Enric Marti; Ferran Poveda | ||||
| Title | MIOCARDIA: Integrating cardiac function and muscular architecture for a better diagnosis | Type | Conference Article | ||
| Year | 2011 | Publication | 14th International Symposium on Applied Sciences in Biomedical and Communication Technologies | Abbreviated Journal | |
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| Abstract | Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. The MIOCARDIA project is a multidisciplinary project in cooperation with l'Hospital de la Santa Creu i de Sant Pau, Clinica la Creu Blanca and Barcelona Supercomputing Center. The ultimate goal of this project is defining a computational model of the myocardium. The model takes into account the deep interrelation between the anatomy and the mechanics of the heart. The paper explains the workflow of the MIOCARDIA project. It also introduces a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and provides evidences of a global helical organization. | ||||
| Address | Barcelona; Spain | ||||
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Association for Computing Machinery | Thesis | |||
| Publisher | Place of Publication | Barcelona, Spain | Editor | Association for Computing Machinery | |
| Language | english | Summary Language | english | Original Title | |
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| ISSN | ISBN | 978-1-4503-0913-4 | Medium | ||
| Area | Expedition | Conference | ISABEL | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GGB2011 | Serial | 1691 | ||
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| Author | Jorge Bernal | ||||
| Title | Use of Projection and Back-projection Methods in Bidimensional Computed Tomography Image Reconstruction | Type | Report | ||
| Year | 2009 | Publication | CVC Tecnical Report | Abbreviated Journal | |
| Volume | 141 | Issue | Pages | ||
| Keywords | Projection, Back-projection, CT scan, Euclidean geometry, Radon transform | ||||
| Abstract | One of the biggest drawbacks related to the use of CT scanners is the cost (in memory and in time) associated. In this project many methods to simulate their functioning, but in a more feasible way (taking an industrial point of view), will be studied.
The main group of techniques that are being used are the one entitled as ’back-projection’. The concept behind is to simulate the X ray emission in CT scans by lines that cross with the image we want to reconstruct. In the first part of this document euclidean geometry is used to face the tasks of projec- tion and back-projection. After analysing the results achieved it has been proved that this approach does not lead to a fully perfect reconstruction (and also has some other problems related to running time and memory cost). Because of this in the second part of the document ’Filtered Back-projection’ method is introduced in order to improve the results. Filtered Back-projection methods rely on mathematical transforms (Fourier, Radon) in order to provide more accurate results that can be obtained in much less time. The main cause of this better results is the use of a filtering process before the back-projection in order to avoid high frequency-caused errors. As a result of this project two different implementations (one for each approach) had been implemented in order to compare their performance. |
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Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Barcelona, Spain | Editor | ||
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| Area | 800 | Expedition | Conference | ||
| Notes | MV; | Approved | no | ||
| Call Number | IAM @ iam @ Ber2009 | Serial | 1693 | ||
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