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Author  |
Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo | ||||
| Title | Image vignetting reduction via a maximization of fuzzy entropy | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Conference on Fuzzy Systems | Abbreviated Journal | |
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| Abstract | In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view. | ||||
| Address | Napoles; Italia; July 2017 | ||||
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| Area | Expedition | Conference | FUZZ-IEEE | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LMG2017 | Serial | 2972 | ||
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| Author |
Laura Ruiz | ||||
| Title | Methods about Pattern Recognition | Type | Report | ||
| Year | 2007 | Publication | CVC Technical Report #114 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Rui2007 | Serial | 834 | ||
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| Author |
Lei Kang | ||||
| Title | Robust Handwritten Text Recognition in Scarce Labeling Scenarios: Disentanglement, Adaptation and Generation | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Handwritten documents are not only preserved in historical archives but also widely used in administrative documents such as cheques and claims. With the rise of the deep learning era, many state-of-the-art approaches have achieved good performance on specific datasets for Handwritten Text Recognition (HTR). However, it is still challenging to solve real use cases because of the varied handwriting styles across different writers and the limited labeled data. Thus, both explorin a more robust handwriting recognition architectures and proposing methods to diminish the gap between the source and target data in an unsupervised way are
demanded. In this thesis, firstly, we explore novel architectures for HTR, from Sequence-to-Sequence (Seq2Seq) method with attention mechanism to non-recurrent Transformer-based method. Secondly, we focus on diminishing the performance gap between source and target data in an unsupervised way. Finally, we propose a group of generative methods for handwritten text images, which could be utilized to increase the training set to obtain a more robust recognizer. In addition, by simply modifying the generative method and joining it with a recognizer, we end up with an effective disentanglement method to distill textual content from handwriting styles so as to achieve a generalized recognition performance. We outperform state-of-the-art HTR performances in the experimental results among different scientific and industrial datasets, which prove the effectiveness of the proposed methods. To the best of our knowledge, the non-recurrent recognizer and the disentanglement method are the first contributions in the handwriting recognition field. Furthermore, we have outlined the potential research lines, which would be interesting to explore in the future. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Alicia Fornes;Marçal Rusiñol;Mauricio Villegas | |
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| ISSN | ISBN | 978-84-122714-0-9 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Kan20 | Serial | 3482 | ||
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| Author |
Lei Kang; Juan Ignacio Toledo; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol | ||||
| Title | Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 40th German Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 459-472 | ||
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| Abstract | This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR. | ||||
| Address | Stuttgart; Germany; October 2018 | ||||
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| Area | Expedition | Conference | GCPR | ||
| Notes | DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ KTR2018 | Serial | 3167 | ||
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| Author |
Lei Kang; Lichao Zhang; Dazhi Jiang | ||||
| Title | Learning Robust Self-Attention Features for Speech Emotion Recognition with Label-Adaptive Mixup | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE International Conference on Acoustics, Speech and Signal Processing | Abbreviated Journal | |
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| Abstract | Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods. | ||||
| Address | Rodhes Islands; Greece; June 2023 | ||||
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| Area | Expedition | Conference | ICASSP | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ KZJ2023 | Serial | 3984 | ||
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| Author |
Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas | ||||
| Title | Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step. | ||||
| Address | Aspen; Colorado; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.129; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRF2020 | Serial | 3446 | ||
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| Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title | Distilling Content from Style for Handwritten Word Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | 17th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
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| Abstract | Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes. Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both the generative process and the recognition efficiency in the IAM dataset. |
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| Address | Virtual ICFHR; September 2020 | ||||
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| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.129; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRR2020 | Serial | 3425 | ||
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| Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title | Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 129 | Issue | Pages | 108766 | |
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| Abstract | The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. | ||||
| Address | Sept. 2022 | ||||
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| Notes | DAG; 600.121; 600.162 | Approved | no | ||
| Call Number | Admin @ si @ KRR2022 | Serial | 3556 | ||
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| Author |
Lei Kang; Pau Riba; Marcal Rusinol; Alicia Fornes; Mauricio Villegas | ||||
| Title | Content and Style Aware Generation of Text-line Images for Handwriting Recognition | Type | Journal Article | ||
| Year | 2021 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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| Abstract | Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art. | ||||
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| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRR2021 | Serial | 3612 | ||
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| Author |
Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol | ||||
| Title | Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 112 | Issue | Pages | 107790 | |
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| Abstract | Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging problem. The main challenge faced when training a language model is to deal with the language model corpus which is usually different to the one used for training the handwritten word recognition system. Thus, the bias between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this work, we introduce Candidate Fusion, a novel way to integrate an external language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two improvements. On the one hand, the sequence-to-sequence recognizer has the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided by the language model. On the other hand, the external language model has the ability to adapt itself to the training corpus and even learn the most commonly errors produced from the recognizer. Finally, by conducting comprehensive experiments, the Candidate Fusion proves to outperform the state-of-the-art language models for handwritten word recognition tasks. |
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| Notes | DAG; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRV2021 | Serial | 3343 | ||
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| Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title | GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. | ||||
| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCV | ||
| Notes | DAG; 600.140; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ KPW2020 | Serial | 3426 | ||
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| Author |
Lei Li; Fuping Wu; Sihan Wang; Xinzhe Luo; Carlos Martin-Isla; Shuwei Zhai; Jianpeng Zhang; Yanfei Liu; Zhen Zhang; Markus J. Ankenbrand; Haochuan Jiang; Xiaoran Zhang; Linhong Wang; Tewodros Weldebirhan Arega; Elif Altunok; Zhou Zhao; Feiyan Li; Jun Ma; Xiaoping Yang; Elodie Puybareau; Ilkay Oksuz; Stephanie Bricq; Weisheng Li;Kumaradevan Punithakumar; Sotirios A. Tsaftaris; Laura M. Schreiber; Mingjing Yang; Guocai Liu; Yong Xia; Guotai Wang; Sergio Escalera; Xiahai Zhuag | ||||
| Title | MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images | Type | Journal Article | ||
| Year | 2023 | Publication | Medical Image Analysis | Abbreviated Journal | MIA |
| Volume | 87 | Issue | Pages | 102808 | |
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| Abstract | Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were and for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/). | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LWW2023a | Serial | 3878 | ||
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| Author |
Leonardo Galteri; Dena Bazazian; Lorenzo Seidenari; Marco Bertini; Andrew Bagdanov; Anguelos Nicolaou; Dimosthenis Karatzas; Alberto del Bimbo | ||||
| Title | Reading Text in the Wild from Compressed Images | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International workshop on Egocentric Perception, Interaction and Computing | Abbreviated Journal | |
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| Abstract | Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts
that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant impact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates. |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV - EPIC | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GBS2017 | Serial | 3006 | ||
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| Author |
Lichao Zhang | ||||
| Title | Towards end-to-end Networks for Visual Tracking in RGB and TIR Videos | Type | Book Whole | ||
| Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In the current work, we identify several problems of current tracking systems. The lack of large-scale labeled datasets hampers the usage of deep learning, especially end-to-end training, for tracking in TIR images. Therefore, many methods for tracking on TIR data are still based on hand-crafted features. This situation also happens in multi-modal tracking, e.g. RGB-T tracking. Another reason, which hampers the development of RGB-T tracking, is that there exists little research on the fusion mechanisms for combining information from RGB and TIR modalities. One of the crucial components of most trackers is the update module. For the currently existing end-to-end tracking architecture, e.g, Siamese trackers, the online model update is still not taken into consideration at the training stage. They use no-update or a linear update strategy during the inference stage. While such a hand-crafted approach to updating has led to improved results, its simplicity limits the potential gain likely to be obtained by learning to update.
To address the data-scarcity for TIR and RGB-T tracking, we use image-to-image translation to generate a large-scale synthetic TIR dataset. This dataset allows us to perform end-to-end training for TIR tracking. Furthermore, we investigate several fusion mechanisms for RGB-T tracking. The multi-modal trackers are also trained in an end-to-end manner on the synthetic data. To improve the standard online update, we pose the updating step as an optimization problem which can be solved by training a neural network. Our approach thereby reduces the hand-crafted components in the tracking pipeline and sets a further step in the direction of a complete end-to-end trained tracking network which also considers updating during optimization. |
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| Address | November 2019 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Abel Gonzalez;Fahad Shahbaz Khan | |
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| ISSN | ISBN | 978-84-1210011-1-9 | Medium | ||
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| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Zha2019 | Serial | 3393 | ||
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| Author |
Lichao Zhang; Abel Gonzalez-Garcia; Joost Van de Weijer; Martin Danelljan; Fahad Shahbaz Khan | ||||
| Title | Synthetic Data Generation for End-to-End Thermal Infrared Tracking | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 28 | Issue | 4 | Pages | 1837 - 1850 |
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| Abstract | The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers. | ||||
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| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YGW2019 | Serial | 3228 | ||
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