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Author Dimosthenis Karatzas; Marçal Rusiñol; Coen Antens; Miquel Ferrer
Title (up) Segmentation Robust to the Vignette Effect for Machine Vision Systems Type Conference Article
Year 2008 Publication 19th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect.
Address Tampa, USA
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ISSN ISBN Medium
Area Expedition Conference ICPR
Notes DAG Approved no
Call Number DAG @ dag @ KRA2008 Serial 1065
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Author Felipe Lumbreras
Title (up) Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. Type Book Whole
Year 2001 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ Lum2001 Serial 188
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Author Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes
Title (up) Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images Type Conference Article
Year 2023 Publication Document Analysis and Recognition – ICDAR 2023 Workshops Abbreviated Journal
Volume 14193 Issue Pages 83-93
Keywords Historical Manuscripts; Symbol Alignment
Abstract Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ TSS2023 Serial 3850
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Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
Title (up) Segmentation-free Word Spotting with Exemplar SVMs Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 12 Pages 3967–3978
Keywords Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression
Abstract In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
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Notes DAG; 600.045; 600.056; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ AGF2014b Serial 2485
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Author S. Casanovas
Title (up) Seguiment de moviment articulat mitjançant flux òptic i metodes estocastics Type Report
Year 2000 Publication CVC Technical Report #43 Abbreviated Journal
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Address CVC (UAB)
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Notes Approved no
Call Number Admin @ si @ Cas2000 Serial 344
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Author Daniel Ponsa; Antonio Lopez
Title (up) Seguimiento Visual de Contornos Computerizado Type Miscellaneous
Year 2009 Publication UAB Divulga, Revista de divulgacion cientifica Abbreviated Journal
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Notes spreading;ADAS Approved no
Call Number ADAS @ adas @ PoL2009b Serial 1270
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Author X. Varona
Title (up) Seguimiento visual robusto en entornos complejos, Tesis. Type Miscellaneous
Year 2001 Publication Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ Var2001 Serial 214
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Author Mario Hernandez; Joao Sanchez; Jordi Vitria
Title (up) Selected papers from Iberian Conference on Pattern Recognition and Image Analysis Type Book Whole
Year 2012 Publication Pattern Recognition Abbreviated Journal
Volume 45 Issue 9 Pages 3047-3582
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ISSN 0031-3203 ISBN Medium
Area Expedition Conference
Notes OR;MV Approved no
Call Number Admin @ si @ HSV2012 Serial 2069
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Author H.M.G. Stokman; Theo Gevers
Title (up) Selection and Fusion of Color Models for Image Feature Detection Type Journal
Year 2007 Publication IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.29(3):371–381 Abbreviated Journal
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Notes ALTRES;ISE Approved no
Call Number Admin @ si @ StG2007 Serial 948
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Author Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil
Title (up) Selection of Radiomics Features based on their Reproducibility Type Conference Article
Year 2019 Publication 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society Abbreviated Journal
Volume Issue Pages 403-408
Keywords
Abstract Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.
Address Berlin; Alemanya; July 2019
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Area Expedition Conference EMBC
Notes IAM; 600.139; 600.145 Approved no
Call Number Admin @ si @ LTS2019 Serial 3358
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Author Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders
Title (up) Selective Search for Object Recognition Type Journal Article
Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 104 Issue 2 Pages 154-171
Keywords
Abstract This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html).
Address
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ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ USG2013 Serial 2362
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Author Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez
Title (up) Selective Spatio-Temporal Interest Points Type Journal Article
Year 2012 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 116 Issue 3 Pages 396-410
Keywords
Abstract Recent progress in the field of human action recognition points towards the use of Spatio-TemporalInterestPoints (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques.
Address
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Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
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ISSN 1077-3142 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ CHM2012 Serial 1806
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Author Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas
Title (up) Selective Style Transfer for Text Type Conference Article
Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 805-812
Keywords transfer; text style transfer; data augmentation; scene text detection
Abstract This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available.
Address Sydney; Australia; September 2019
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Area Expedition Conference ICDAR
Notes DAG; 600.129; 600.135; 601.338; 601.310; 600.121 Approved no
Call Number GBG2019 Serial 3265
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Author Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer
Title (up) Self-supervised blur detection from synthetically blurred scenes Type Journal Article
Year 2019 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 92 Issue Pages 103804
Keywords
Abstract Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.
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Notes LAMP; 600.109; 600.120 Approved no
Call Number Admin @ si @ AGG2019 Serial 3301
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Author Jiaolong Xu; Liang Xiao; Antonio Lopez
Title (up) Self-supervised Domain Adaptation for Computer Vision Tasks Type Journal Article
Year 2019 Publication IEEE Access Abbreviated Journal ACCESS
Volume 7 Issue Pages 156694 - 156706
Keywords
Abstract Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ XXL2019 Serial 3302
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