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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
	Keywords
	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
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	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
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	Sciyo	Place of Publication		Editor	Ahmed Rebai
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	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
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	The Wiley-IS&T Series in Imaging Science and Technology	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-0-470-89084-4	Medium
	Area		Expedition		Conference
	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.
	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; 600.097; 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
	Keywords
	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
	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		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
	Keywords
	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.
	Address	Barcelona; October 2013
	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	978-1-4503-1789-4	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
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	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
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Bordeaux
	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	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
	Keywords
	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.
	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	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 specically, 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.
	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; 600.061; 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
	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	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.
	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; 600.097; 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
	Keywords
	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.
	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			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.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB;			Approved	no
	Call Number	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
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ AID2015			Serial	2640
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