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	Author	Jon Almazan
	Title	Learning to Represent Handwritten Shapes and Words for Matching and Recognition			Type	Book Whole
	Year	2014	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Writing is one of the most important forms of communication and for centuries, handwriting had been the most reliable way to preserve knowledge. However, despite the recent development of printing houses and electronic devices, handwriting is still broadly used for taking notes, doing annotations, or sketching ideas. Transferring the ability of understanding handwritten text or recognizing handwritten shapes to computers has been the goal of many researches due to its huge importance for many different fields. However, designing good representations to deal with handwritten shapes, e.g. symbols or words, is a very challenging problem due to the large variability of these kinds of shapes. One of the consequences of working with handwritten shapes is that we need representations to be robust, i.e., able to adapt to large intra-class variability. We need representations to be discriminative, i.e., able to learn what are the differences between classes. And, we need representations to be efficient, i.e., able to be rapidly computed and compared. Unfortunately, current techniques of handwritten shape representation for matching and recognition do not fulfill some or all of these requirements. Through this thesis we focus on the problem of learning to represent handwritten shapes aimed at retrieval and recognition tasks. Concretely, on the first part of the thesis, we focus on the general problem of representing any kind of handwritten shape. We first present a novel shape descriptor based on a deformable grid that deals with large deformations by adapting to the shape and where the cells of the grid can be used to extract different features. Then, we propose to use this descriptor to learn statistical models, based on the Active Appearance Model, that jointly learns the variability in structure and texture of a given class. Then, on the second part, we focus on a concrete application, the problem of representing handwritten words, for the tasks of word spotting, where the goal is to find all instances of a query word in a dataset of images, and recognition. First, we address the segmentation-free problem and propose an unsupervised, sliding-window-based approach that achieves state-of- the-art results in two public datasets. Second, we address the more challenging multi-writer problem, where the variability in words exponentially increases. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace, and where those that represent the same word are close together. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. This leads to a low-dimensional, unified representation of word images and strings, resulting in a method that allows one to perform either image and text searches, as well as image transcription, in a unified framework. We evaluate our methods on different public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Ernest Valveny;Alicia Fornes
	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; 600.077			Approved	no
	Call Number	Admin @ si @ Alm2014			Serial	2572
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	Author	Jon Almazan
	Title	Deforming the Blurred Shape Model for Shape Description and Recognition			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	163	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Master's 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	Admin @ si @ Alm2010			Serial	1354
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	Author	Jose Manuel Alvarez; Felipe Lumbreras; Antonio Lopez; Theo Gevers
	Title	Understanding Road Scenes using Visual Cues			Type	Miscellaneous
	Year	2012	Publication	European Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	DEMO
	Address	Florence; Italy
	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	ISE			Approved	no
	Call Number	Admin @ si @ ALL2012			Serial	2795
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	Author	Jose Manuel Alvarez; Antonio Lopez
	Title	Photometric Invariance by Machine Learning			Type	Book Chapter
	Year	2012	Publication	Color in Computer Vision: Fundamentals and Applications	Abbreviated Journal
	Volume	7	Issue		Pages	113-134
	Keywords	road detection
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	iConcept Press Ltd	Place of Publication		Editor	Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek
	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	ADAS			Approved	no
	Call Number	Admin @ si @ AlL2012			Serial	2186
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	Author	Murad Al Haj
	Title	Face Detection in Color Images Using Primitive Shape Features			Type	Report
	Year	2008	Publication	CVC Technical Report #118	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Bellaterra (Spain)
	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	ISE			Approved	no
	Call Number	Admin @ si @ AlH2008			Serial	945
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	Author	Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras
	Title	Combining Priors, Appearance and Context for Road Detection			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	15	Issue	3	Pages	1168-1178
	Keywords	Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout
	Abstract	Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1524-9050	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076;ISE			Approved	no
	Call Number	Admin @ si @ ALG2014			Serial	2501
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	Author	Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez
	Title	Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features			Type	Conference Article
	Year	2012	Publication	12th European Conference on Computer Vision – Workshops and Demonstrations	Abbreviated Journal
	Volume	7584	Issue		Pages	586-595
	Keywords	road detection
	Abstract	Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process. Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	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	978-3-642-33867-0	Medium
	Area		Expedition		Conference	ECCVW
	Notes	ADAS;ISE			Approved	no
	Call Number	Admin @ si @ ALG2012; ADAS @ adas			Serial	2187
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	Author	David Aldavert
	Title	Efficient and Scalable Handwritten Word Spotting on Historical Documents using Bag of Visual Words			Type	Book Whole
	Year	2021	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Word spotting can be defined as the pattern recognition tasked aimed at locating and retrieving a specific keyword within a document image collection without explicitly transcribing the whole corpus. Its use is particularly interesting when applied in scenarios where Optical Character Recognition performs poorly or can not be used at all. This thesis focuses on such a scenario, word spotting on historical handwritten documents that have been written by a single author or by multiple authors with a similar calligraphy. This problem requires a visual signature that is robust to image artifacts, flexible to accommodate script variations and efficient to retrieve information in a rapid manner. For this, we have developed a set of word spotting methods that on their foundation use the well known Bag-of-Visual-Words (BoVW) representation. This representation has gained popularity among the document image analysis community to characterize handwritten words in an unsupervised manner. However, most approaches on this field rely on a basic BoVW configuration and disregard complex encoding and spatial representations. We determine which BoVW configurations provide the best performance boost to a spotting system. Then, we extend the segmentation-based word spotting, where word candidates are given a priori, to segmentation-free spotting. The proposed approach seeds the document images with overlapping word location candidates and characterizes them with a BoVW signature. Retrieval is achieved comparing the query and candidate signatures and returning the locations that provide a higher consensus. This is a simple but powerful approach that requires a more compact signature than in a segmentation-based scenario. We first project the BoVW signature into a reduced semantic topics space and then compress it further using Product Quantizers. The resulting signature only requires a few dozen bytes, allowing us to index thousands of pages on a common desktop computer. The final system still yields a performance comparable to the state-of-the-art despite all the information loss during the compression phases. Afterwards, we also study how to combine different modalities of information in order to create a query-by-X spotting system where, words are indexed using an information modality and queries are retrieved using another. We consider three different information modalities: visual, textual and audio. Our proposal is to create a latent feature space where features which are semantically related are projected onto the same topics. Creating thus a new feature space where information from different modalities can be compared. Later, we consider the codebook generation and descriptor encoding problem. The codebooks used to encode the BoVW signatures are usually created using an unsupervised clustering algorithm and, they require to test multiple parameters to determine which configuration is best for a certain document collection. We propose a semantic clustering algorithm which allows to estimate the best parameter from data. Since gather annotated data is costly, we use synthetically generated word images. The resulting codebook is database agnostic, i. e. a codebook that yields a good performance on document collections that use the same script. We also propose the use of an additional codebook to approximate descriptors and reduce the descriptor encoding complexity to sub-linear. Finally, we focus on the problem of signatures dimensionality. We propose a new symbol probability signature where each bin represents the probability that a certain symbol is present a certain location of the word image. This signature is extremely compact and combined with compression techniques can represent word images with just a few bytes per signature.
	Address	April 2021
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Marçal Rusiñol;Josep Llados
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-122714-5-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Ald2021			Serial	3601
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	Author	David Aldavert
	Title	Visual Simultaneous Localization and Mapping			Type	Report
	Year	2006	Publication	CVC Technical Report #98	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	CVC (UAB)
	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	ADAS			Approved	no
	Call Number	Admin @ si @ Ald2006			Serial	736
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	Author	David Alcalde
	Title	Image classification in terms of rotation-invariant pattern matching			Type	Report
	Year	2003	Publication	CVC Technical Report #73	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	CVC (UAB)
	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	Admin @ si @ Alc2003			Serial	518
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	Author	Marina Alberti
	Title	Detection and Alignment of Vascular Structures in Intravascular Ultrasound using Pattern Recognition Techniques			Type	Book Whole
	Year	2013	Publication	PhD Thesis, Universitat de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this thesis, several methods for the automatic analysis of Intravascular Ultrasound (IVUS) sequences are presented, aimed at assisting physicians in the diagnosis, the assessment of the intervention and the monitoring of the patients with coronary disease. The basis for the developed frameworks are machine learning, pattern recognition and image processing techniques. First, a novel approach for the automatic detection of vascular bifurcations in IVUS is presented. The task is addressed as a binary classication problem (identifying bifurcation and non-bifurcation angular sectors in the sequence images). The multiscale stacked sequential learning algorithm is applied, to take into account the spatial and temporal context in IVUS sequences, and the results are rened using a-priori information about branching dimensions and geometry. The achieved performance is comparable to intra- and inter-observer variability. Then, we propose a novel method for the automatic non-rigid alignment of IVUS sequences of the same patient, acquired at dierent moments (before and after percutaneous coronary intervention, or at baseline and follow-up examinations). The method is based on the description of the morphological content of the vessel, obtained by extracting temporal morphological proles from the IVUS acquisitions, by means of methods for segmentation, characterization and detection in IVUS. A technique for non-rigid sequence alignment – the Dynamic Time Warping algorithm - is applied to the proles and adapted to the specic clinical problem. Two dierent robust strategies are proposed to address the partial overlapping between frames of corresponding sequences, and a regularization term is introduced to compensate for possible errors in the prole extraction. The benets of the proposed strategy are demonstrated by extensive validation on synthetic and in-vivo data. The results show the interest of the proposed non-linear alignment and the clinical value of the method. Finally, a novel automatic approach for the extraction of the luminal border in IVUS images is presented. The method applies the multiscale stacked sequential learning algorithm and extends it to 2-D+T, in a rst classication phase (the identi- cation of lumen and non-lumen regions of the images), while an active contour model is used in a second phase, to identify the lumen contour. The method is extended to the longitudinal dimension of the sequences and it is validated on a challenging data-set.
	Address	Barcelona
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Simone Balocco;Petia Radeva
	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 @ Alb2013			Serial	2215
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	Author	Arash Akbarinia; C. Alejandro Parraga
	Title	Feedback and Surround Modulated Boundary Detection			Type	Journal Article
	Year	2018	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	126	Issue	12	Pages	1367–1380
	Keywords	Boundary detection; Surround modulation; Biologically-inspired vision
	Abstract	Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.
	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	NEUROBIT; 600.068; 600.072			Approved	no
	Call Number	Admin @ si @ AkP2018b			Serial	2991
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	Author	Arash Akbarinia; C. Alejandro Parraga
	Title	Colour Constancy Beyond the Classical Receptive Field			Type	Journal Article
	Year	2018	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	40	Issue	9	Pages	2081 - 2094
	Keywords
	Abstract	The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us.
	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	NEUROBIT; 600.068; 600.072			Approved	no
	Call Number	Admin @ si @ AkP2018a			Serial	2990
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	Author	Arash Akbarinia; C. Alejandro Parraga
	Title	Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception			Type	Conference Article
	Year	2016	Publication	European Conference on Visual Perception	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Barcelona; Spain; August 2016
	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	ECVP
	Notes	NEUROBIT			Approved	no
	Call Number	Admin @ si @ AkP2016b			Serial	2900
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	Author	Arash Akbarinia; C. Alejandro Parraga
	Title	Biologically plausible boundary detection			Type	Conference Article
	Year	2016	Publication	27th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.
	Address	York; UK; September 2016
	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	BMVC
	Notes	NEUROBIT; 600.068; 600.072			Approved	no
	Call Number	Admin @ si @ AkP2016a			Serial	2867
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