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Author  |
Thierry Brouard; Jordi Gonzalez; Caifeng Shan; Massimo Piccardi; Larry S. Davis | ||||
| Title | Special issue on background modeling for foreground detection in real-world dynamic scenes | Type | Journal Article | ||
| Year | 2014 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
| Volume | 25 | Issue | 5 | Pages | 1101-1103 |
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| Abstract | Although background modeling and foreground detection are not mandatory steps for computer vision applications, they may prove useful as they separate the primal objects usually called “foreground” from the remaining part of the scene called “background”, and permits different algorithmic treatment in the video processing field such as video surveillance, optical motion capture, multimedia applications, teleconferencing and human–computer interfaces. Conventional background modeling methods exploit the temporal variation of each pixel to model the background, and the foreground detection is made using change detection. The last decade witnessed very significant publications on background modeling but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, i | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0932-8092 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE; 600.078 | Approved | no | ||
| Call Number | BGS2014a | Serial | 2411 | ||
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| Author |
Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel | ||||
| Title | Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures | Type | Book Chapter | ||
| Year | 2010 | Publication | Bayesian Network | Abbreviated Journal | |
| Volume | Issue | Pages | 13-37 | ||
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| Publisher | Sciyo | Place of Publication | Editor | Ahmed Rebai | |
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| ISSN | ISBN | 978-953-307-124-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ BDL2010 | Serial | 1461 | ||
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| Author |
Theo Gevers; Arjan Gijsenij; Joost Van de Weijer; J.M. Geusebroek | ||||
| Title | Color in Computer Vision: Fundamentals and Applications | Type | Book Whole | ||
| Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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| Publisher | The Wiley-IS&T Series in Imaging Science and Technology | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-0-470-89084-4 | Medium | ||
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| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ GGG2012a | Serial | 2068 | ||
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| Author |
Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam | ||||
| Title | Subgraph spotting in graph representations of comic book images | Type | Journal Article | ||
| Year | 2018 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 112 | Issue | Pages | 118-124 | |
| Keywords | Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images | ||||
| Abstract | Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. | ||||
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| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ LLD2018 | Serial | 3150 | ||
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 265-269 | ||
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| Abstract | In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods. | ||||
| Address | Washington; USA; August 2013 | ||||
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| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DTR2013b | Serial | 2331 | ||
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Document noise removal using sparse representations over learned dictionary | Type | Conference Article | ||
| Year | 2013 | Publication | Symposium on Document engineering | Abbreviated Journal | |
| Volume | Issue | Pages | 161-168 | ||
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| Abstract | best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art. |
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| Address | Barcelona; October 2013 | ||||
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| ISSN | ISBN | 978-1-4503-1789-4 | Medium | ||
| Area | Expedition | Conference | ACM-DocEng | ||
| Notes | DAG; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ DTR2013a | Serial | 2330 | ||
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Text/graphic separation using a sparse representation with multi-learned dictionaries | Type | Conference Article | ||
| Year | 2012 | Publication | 21st International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Graphics Recognition; Layout Analysis; Document Understandin | ||||
| Abstract | In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds. | ||||
| Address | Tsukuba | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DTR2012a | Serial | 2135 | ||
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Noise suppression over bi-level graphical documents using a sparse representation | Type | Conference Article | ||
| Year | 2012 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
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| Address | Bordeaux | ||||
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| Area | Expedition | Conference | CIFED | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DTR2012b | Serial | 2136 | ||
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 156-160 | ||
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| Abstract | This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2014 | Serial | 2543 | ||
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Sparse representation over learned dictionary for symbol recognition | Type | Journal Article | ||
| Year | 2016 | Publication | Signal Processing | Abbreviated Journal | SP |
| Volume | 125 | Issue | Pages | 36-47 | |
| Keywords | Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points | ||||
| Abstract | In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. | ||||
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| Notes | DAG; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2016 | Serial | 2946 | ||
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary | Type | Book Chapter | ||
| Year | 2016 | Publication | Recent Trends in Image Processing and Pattern Recognition | Abbreviated Journal | |
| Volume | 709 | Issue | Pages | ||
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| Area | Expedition | Conference | RTIP2R | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ HTR2016 | Serial | 2956 | ||
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| Author |
Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone | ||||
| Title | DSD: document sparse-based denoising algorithm | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
| Volume | 22 | Issue | 1 | Pages | 177–186 |
| Keywords | Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models | ||||
| Abstract | In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising. | ||||
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| Notes | DAG; 600.097; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DRT2019 | Serial | 3254 | ||
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| Author |
Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang | ||||
| Title | Diffusion-based network for unsupervised landmark detection | Type | Journal Article | ||
| Year | 2024 | Publication | Knowledge-Based Systems | Abbreviated Journal | |
| Volume | 292 | Issue | Pages | 111627 | |
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| Abstract | Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ WWT2024 | Serial | 4024 | ||
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| Author |
Tadashi Araki; Sumit K. Banchhor; Narendra D. Londhe; Nobutaka Ikeda; Petia Radeva; Devarshi Shukla; Luca Saba; Antonella Balestrieri; Andrew Nicolaides; Shoaib Shafique; John R. Laird; Jasjit S. Suri | ||||
| Title | Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Medical Systems | Abbreviated Journal | JMS |
| Volume | 40 | Issue | 3 | Pages | 51:1-51:20 |
| Keywords | Interventional cardiology; Atherosclerosis; Coronary arteries; IVUS; calcium volume; Soft computing; Performance Reliability; Accuracy | ||||
| Abstract | Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm3, 27.79 ± 10.94 mm3, 46.44 ± 19.13 mm3 and 35.92 ± 16.44 mm3 respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student’s t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80 %. Out procedure and protocol is along the line with method previously published clinically. | ||||
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| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @ ABL2016 | Serial | 2729 | ||
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| Author |
Tadashi Araki; Nobutaka Ikeda; Nilanjan Dey; Sayan Chakraborty; Luca Saba; Dinesh Kumar; Elisa Cuadrado Godia; Xiaoyi Jiang; Ajay Gupta; Petia Radeva; John R. Laird; Andrew Nicolaides; Jasjit S. Suri | ||||
| Title | A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound | Type | Journal Article | ||
| Year | 2015 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
| Volume | 118 | Issue | 2 | Pages | 158-172 |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ AID2015 | Serial | 2640 | ||
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