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Author Gemma Sanchez; Josep Llados; Enric Marti
Title (down) Segmentation and analysis of linial texture in plans Type Conference Article
Year 1997 Publication Intelligence Artificielle et Complexité. Abbreviated Journal
Volume Issue Pages
Keywords Structural Texture, Voronoi, Hierarchical Clustering, String Matching.
Abstract The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph.
Address Paris, France
Corporate Author Thesis
Publisher Place of Publication Paris Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference AERFAI
Notes DAG;IAM; Approved no
Call Number IAM @ iam @ SLM1997 Serial 1649
Permanent link to this record
 
 
Author Petia Radeva
Title (down) Segmentacion de Imagenes Radiograficas con Snakes. Aplicacion a la Determinacion de la Madurez Osea. Type Miscellaneous
Year 1993 Publication Master Thesis, UPPIA, Universitat Autonoma de Barcelona. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher 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 MILAB Approved no
Call Number BCNPCL @ bcnpcl @ Rad1993b Serial 173
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Author Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli
Title (down) Seeing and Hearing Egocentric Actions: How Much Can We Learn? Type Conference Article
Year 2019 Publication IEEE International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 4470-4480
Keywords
Abstract Our interaction with the world is an inherently multimodal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial, and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a 5.18% improvement over the state of the art on verb classification.
Address Seul; Korea; October 2019
Corporate Author Thesis
Publisher 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 ICCVW
Notes MILAB; no proj Approved no
Call Number Admin @ si @ CLR2019b Serial 3385
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Author German Barquero; Sergio Escalera; Cristina Palmero
Title (down) Seamless Human Motion Composition with Blended Positional Encodings Type Miscellaneous
Year 2024 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
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.
Address
Corporate Author Thesis
Publisher 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 HUPBA Approved no
Call Number Admin @ si @ BEP2024 Serial 4022
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Author Partha Pratim Roy; Umapada Pal; Josep Llados
Title (down) Seal Object Detection in Document Images using GHT of Local Component Shapes Type Conference Article
Year 2010 Publication 10th ACM Symposium On Applied Computing Abbreviated Journal
Volume Issue Pages 23–27
Keywords
Abstract Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents.
Address Sierre, Switzerland
Corporate Author Thesis
Publisher 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 SAC
Notes DAG Approved no
Call Number DAG @ dag @ RPL2010a Serial 1291
Permanent link to this record
 
 
Author Partha Pratim Roy; Umapada Pal; Josep Llados
Title (down) Seal detection and recognition: An approach for document indexing Type Conference Article
Year 2009 Publication 10th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 101–105
Keywords
Abstract Reliable indexing of documents having seal instances can be achieved by recognizing seal information. This paper presents a novel approach for detecting and classifying such multi-oriented seals in these documents. First, Hough Transform based methods are applied to extract the seal regions in documents. Next, isolated text characters within these regions are detected. Rotation and size invariant features and a support vector machine based classifier have been used to recognize these detected text characters. Next, for each pair of character, we encode their relative spatial organization using their distance and angular position with respect to the centre of the seal, and enter this code into a hash table. Given an input seal, we recognize the individual text characters and compute the code for pair-wise character based on the relative spatial organization. The code obtained from the input seal helps to retrieve model hypothesis from the hash table. The seal model to which we get maximum hypothesis is selected for the recognition of the input seal. The methodology is tested to index seal in rotation and size invariant environment and we obtained encouraging results.
Address Barcelona, Spain
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1520-5363 ISBN 978-1-4244-4500-4 Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number DAG @ dag @ RPL2009b Serial 1239
Permanent link to this record
 
 
Author Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz
Title (down) SDIT: Scalable and Diverse Cross-domain Image Translation Type Conference Article
Year 2019 Publication 27th ACM International Conference on Multimedia Abbreviated Journal
Volume Issue Pages 1267–1276
Keywords
Abstract Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.
Address Nice; Francia; October 2019
Corporate Author Thesis
Publisher 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 ACM-MM
Notes LAMP; 600.106; 600.109; 600.141; 600.120 Approved no
Call Number Admin @ si @ WGW2019 Serial 3363
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Author Fei Wang; Kai Wang; Joost Van de Weijer
Title (down) ScrollNet: DynamicWeight Importance for Continual Learning Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
Volume Issue Pages 3345-3355
Keywords
Abstract The principle underlying most existing continual learning (CL) methods is to prioritize stability by penalizing changes in parameters crucial to old tasks, while allowing for plasticity in other parameters. The importance of weights for each task can be determined either explicitly through learning a task-specific mask during training (e.g., parameter isolation-based approaches) or implicitly by introducing a regularization term (e.g., regularization-based approaches). However, all these methods assume that the importance of weights for each task is unknown prior to data exposure. In this paper, we propose ScrollNet as a scrolling neural network for continual learning. ScrollNet can be seen as a dynamic network that assigns the ranking of weight importance for each task before data exposure, thus achieving a more favorable stability-plasticity tradeoff during sequential task learning by reassigning this ranking for different tasks. Additionally, we demonstrate that ScrollNet can be combined with various CL methods, including regularization-based and replay-based approaches. Experimental results on CIFAR100 and TinyImagenet datasets show the effectiveness of our proposed method.
Address Paris; France; October 2023
Corporate Author Thesis
Publisher 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 ICCVW
Notes LAMP Approved no
Call Number Admin @ si @ WWW2023 Serial 3945
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Author Sounak Dey; Palaiahnakote Shivakumara; K.S. Raghunanda; Umapada Pal; Tong Lu; G. Hemantha Kumar; Chee Seng Chan
Title (down) Script independent approach for multi-oriented text detection in scene image Type Journal Article
Year 2017 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 242 Issue Pages 96-112
Keywords
Abstract Developing a text detection method which is invariant to scripts in natural scene images is a challeng- ing task due to different geometrical structures of various scripts. Besides, multi-oriented of text lines in natural scene images make the problem more challenging. This paper proposes to explore ring radius transform (RRT) for text detection in multi-oriented and multi-script environments. The method finds component regions based on convex hull to generate radius matrices using RRT. It is a fact that RRT pro- vides low radius values for the pixels that are near to edges, constant radius values for the pixels that represent stroke width, and high radius values that represent holes created in background and convex hull because of the regular structures of text components. We apply k -means clustering on the radius matrices to group such spatially coherent regions into individual clusters. Then the proposed method studies the radius values of such cluster components that are close to the centroid and far from the cen- troid to detect text components. Furthermore, we have developed a Bangla dataset (named as ISI-UM dataset) and propose a semi-automatic system for generating its ground truth for text detection of arbi- trary orientations, which can be used by the researchers for text detection and recognition in the future. The ground truth will be released to public. Experimental results on our ISI-UM data and other standard datasets, namely, ICDAR 2013 scene, SVT and MSRA data, show that the proposed method outperforms the existing methods in terms of multi-lingual and multi-oriented text detection ability.
Address
Corporate Author Thesis
Publisher 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 DAG; 600.121 Approved no
Call Number Admin @ si @ DSR2017 Serial 3260
Permanent link to this record
 
 
Author Jose Antonio Rodriguez; Florent Perronnin
Title (down) Score Normalization for Hmm-based Word Spotting Using Universal Background Model Type Conference Article
Year 2008 Publication International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 82–87
Keywords
Abstract
Address Montreal (Canada)
Corporate Author Thesis
Publisher 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 ICFHR
Notes Approved no
Call Number Admin @ si @ RoP2008c Serial 1067
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Author Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas
Title (down) Scene Text Visual Question Answering Type Conference Article
Year 2019 Publication 18th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 4291-4301
Keywords
Abstract Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting highlevel semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research.
Address Seul; Corea; October 2019
Corporate Author Thesis
Publisher 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 ICCV
Notes DAG; 600.129; 600.135; 601.338; 600.121 Approved no
Call Number Admin @ si @ BTM2019b Serial 3285
Permanent link to this record
 
 
Author Lluis Gomez; Dimosthenis Karatzas
Title (down) Scene Text Recognition: No Country for Old Men? Type Conference Article
Year 2014 Publication 1st International Workshop on Robust Reading Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher 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 IWRR
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ GoK2014c Serial 2538
Permanent link to this record
 
 
Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias
Title (down) Scene Representations for Autonomous Driving: an approach based on polygonal primitives Type Conference Article
Year 2015 Publication 2nd Iberian Robotics Conference ROBOT2015 Abbreviated Journal
Volume 417 Issue Pages 503-515
Keywords Scene reconstruction; Point cloud; Autonomous vehicles
Abstract In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
Address Lisboa; Portugal; November 2015
Corporate Author Thesis
Publisher 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 ROBOT
Notes ADAS; 600.076; 600.086 Approved no
Call Number Admin @ si @ OSS2015a Serial 2662
Permanent link to this record
 
 
Author Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez
Title (down) Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning Type Conference Article
Year 2023 Publication International Conference on Intelligent Robots and Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.
Address Detroit; USA; October 2023
Corporate Author Thesis
Publisher 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 IROS
Notes ADAS Approved no
Call Number Admin @ si @ XCP2023 Serial 3930
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg
Title (down) Scale Coding Bag-of-Words for Action Recognition Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 1514-1519
Keywords
Abstract Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant
strategy is sub-optimal since it ignores the multi-scale information
available with each bounding box of a person.
This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music,
riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods.
Address Stockholm; August 2014
Corporate Author Thesis
Publisher 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 ICPR
Notes CIC; LAMP; 601.240; 600.074; 600.079 Approved no
Call Number Admin @ si @ KWB2014 Serial 2450
Permanent link to this record