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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
Keywords
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
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; 600.162 Approved no
Call Number (down) 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
Volume Issue Pages
Keywords
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.
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; 600.140; 600.121 Approved no
Call Number (down) Admin @ si @ KRR2021 Serial 3612
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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
Volume Issue Pages
Keywords
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.
Address Virtual ICFHR; September 2020
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 DAG; 600.129; 600.140; 600.121 Approved no
Call Number (down) Admin @ si @ KRR2020 Serial 3425
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Author Dimosthenis Karatzas; Sergi Robles; Joan Mas; Farshad Nourbakhsh; Partha Pratim Roy
Title ICDAR 2011 Robust Reading Competition – Challege 1: Reading Text in Born-Digital Images (Web and Email) Type Conference Article
Year 2011 Publication 11th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1485-1490
Keywords
Abstract This paper presents the results of the first Challenge of ICDAR 2011 Robust Reading Competition. Challenge 1 is focused on the extraction of text from born-digital images, specifically from images found in Web pages and emails. The challenge was organized in terms of three tasks that look at different stages of the process: text localization, text segmentation and word recognition. In this paper we present the results of the challenge for all three tasks, and make an open call for continuous participation outside the context of ICDAR 2011.
Address Beijing, China
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 978-1-4577-1350-7 Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number (down) Admin @ si @ KRM2011 Serial 1793
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Author Dimosthenis Karatzas; Sergi Robles; Lluis Gomez
Title An on-line platform for ground truthing and performance evaluation of text extraction systems Type Conference Article
Year 2014 Publication 11th IAPR International Workshop on Document Analysis and Systems Abbreviated Journal
Volume Issue Pages 242 - 246
Keywords
Abstract This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The
presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use.
Address Tours; Francia; April 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 978-1-4799-3243-6 Medium
Area Expedition Conference DAS
Notes DAG; 600.056; 600.077 Approved no
Call Number (down) Admin @ si @ KRG2014 Serial 2491
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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
Volume Issue Pages
Keywords
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
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 WACV
Notes DAG; 600.129; 600.140; 601.302; 601.312; 600.121 Approved no
Call Number (down) Admin @ si @ KRF2020 Serial 3446
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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
Volume Issue Pages
Keywords
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
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 ECCV
Notes DAG; 600.140; 600.121; 600.129 Approved no
Call Number (down) Admin @ si @ KPW2020 Serial 3426
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Author J.Kuhn; A.Nussbaumer; J.Pirker; Dimosthenis Karatzas; A. Pagani; O.Conlan; M.Memmel; C.M.Steiner; C.Gutl; D.Albert; Andreas Dengel
Title Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space Type Conference Article
Year 2015 Publication Workshop Proceedings on the 11th International Conference on Intelligent Environments Abbreviated Journal
Volume 19 Issue Pages 373-380
Keywords
Abstract Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually.
Address Praga; Chzech Republic; July 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 IE
Notes DAG; 600.077 Approved no
Call Number (down) Admin @ si @ KNP2015 Serial 2694
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Author Hanne Kause; Patricia Marquez; Andrea Fuster; Aura Hernandez-Sabate; Luc Florack; Debora Gil; Hans van Assen
Title Quality Assessment of Optical Flow in Tagging MRI Type Conference Article
Year 2015 Publication 5th Dutch Bio-Medical Engineering Conference BME2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address The Netherlands; January 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference BME
Notes IAM; ADAS; 600.076; 600.075 Approved no
Call Number (down) Admin @ si @ KMF2015 Serial 2616
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Author Matej Kristan; Jiri Matas; Martin Danelljan; Michael Felsberg; Hyung Jin Chang; Luka Cehovin Zajc; Alan Lukezic; Ondrej Drbohlav; Zhongqun Zhang; Khanh-Tung Tran; Xuan-Son Vu; Johanna Bjorklund; Christoph Mayer; Yushan Zhang; Lei Ke; Jie Zhao; Gustavo Fernandez; Noor Al-Shakarji; Dong An; Michael Arens; Stefan Becker; Goutam Bhat; Sebastian Bullinger; Antoni B. Chan; Shijie Chang; Hanyuan Chen; Xin Chen; Yan Chen; Zhenyu Chen; Yangming Cheng; Yutao Cui; Chunyuan Deng; Jiahua Dong; Matteo Dunnhofer; Wei Feng; Jianlong Fu; Jie Gao; Ruize Han; Zeqi Hao; Jun-Yan He; Keji He; Zhenyu He; Xiantao Hu; Kaer Huang; Yuqing Huang; Yi Jiang; Ben Kang; Jin-Peng Lan; Hyungjun Lee; Chenyang Li; Jiahao Li; Ning Li; Wangkai Li; Xiaodi Li; Xin Li; Pengyu Liu; Yue Liu; Huchuan Lu; Bin Luo; Ping Luo; Yinchao Ma; Deshui Miao; Christian Micheloni; Kannappan Palaniappan; Hancheol Park; Matthieu Paul; HouWen Peng; Zekun Qian; Gani Rahmon; Norbert Scherer-Negenborn; Pengcheng Shao; Wooksu Shin; Elham Soltani Kazemi; Tianhui Song; Rainer Stiefelhagen; Rui Sun; Chuanming Tang; Zhangyong Tang; Imad Eddine Toubal; Jack Valmadre; Joost van de Weijer; Luc Van Gool; Jash Vira; Stephane Vujasinovic; Cheng Wan; Jia Wan; Dong Wang; Fei Wang; Feifan Wang; He Wang; Limin Wang; Song Wang; Yaowei Wang; Zhepeng Wang; Gangshan Wu; Jiannan Wu; Qiangqiang Wu; Xiaojun Wu; Anqi Xiao; Jinxia Xie; Chenlong Xu; Min Xu; Tianyang Xu; Yuanyou Xu; Bin Yan; Dawei Yang; Ming-Hsuan Yang; Tianyu Yang; Yi Yang; Zongxin Yang; Xuanwu Yin; Fisher Yu; Hongyuan Yu; Qianjin Yu; Weichen Yu; YongSheng Yuan; Zehuan Yuan; Jianlin Zhang; Lu Zhang; Tianzhu Zhang; Guodongfang Zhao; Shaochuan Zhao; Yaozong Zheng; Bineng Zhong; Jiawen Zhu; Xuefeng Zhu; Yueting Zhuang; ChengAo Zong; Kunlong Zuo
Title The First Visual Object Tracking Segmentation VOTS2023 Challenge Results Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
Volume Issue Pages 1796-1818
Keywords
Abstract The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website\footnote https://www.votchallenge.net/vots2023/.
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 (down) Admin @ si @ KMD2023 Serial 3939
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Author Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo
Title Leveraging local neighborhood topology for large scale person re-identification Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 12 Pages 3767–3778
Keywords Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100
Abstract In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data.
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 LAMP; 601.240; 600.079 Approved no
Call Number (down) Admin @ si @ KLB2014a Serial 2522
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Author Md.Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig
Title CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.
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; no proj Approved no
Call Number (down) Admin @ si @ KJR2018 Serial 3235
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Author I. King; Zhong Jin
Title Integrated Probability Function and Its Application to Content-Based Image Retrieval By Relevance Feedback Type Journal
Year 2003 Publication Pattern Recognition, 36(9): 2177–2186 (IF: 1.611) 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 Approved no
Call Number (down) Admin @ si @ KiJ2003 Serial 427
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Author Sudeep Katakol; Luis Herranz; Fei Yang; Marta Mrak
Title DANICE: Domain adaptation without forgetting in neural image compression Type Conference Article
Year 2021 Publication Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages 1921-1925
Keywords
Abstract Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.
Address Virtual; June 2021
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 CVPRW
Notes LAMP; 600.120; 600.141; 601.379 Approved no
Call Number (down) Admin @ si @ KHY2021 Serial 3568
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Author Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil
Title Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging Type Book Chapter
Year 2015 Publication Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 Abbreviated Journal
Volume 9534 Issue Pages 69-79
Keywords
Abstract Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm.
Address Munich; Germany; January 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-28711-9 Medium
Area Expedition Conference STACOM
Notes ADAS; IAM; 600.075; 600.076; 600.060; 601.145 Approved no
Call Number (down) Admin @ si @ KHM2015 Serial 2734
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