Publicacions CVC -- Query Results

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	Login Quick Search: Field: contains: ...
	3106–3120 of 3413 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

[191–200] << 201 202 203 204 205 206 207 208 209 210 >> [211–220]

List View

Citations

Details

	Records
	Author	Javier Vazquez; Maria Vanrell; Ramon Baldrich
	Title	Towards a Psychophysical Evaluation of Colour Constancy Algorithms			Type	Conference Article
	Year	2008	Publication	4th European Conference on Colour in Graphics, Imaging and Vision Proceedings	Abbreviated Journal
	Volume		Issue		Pages	372–377
	Keywords
	Abstract
	Address	Terrassa (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	CGIV08
	Notes	CAT;CIC			Approved	no
	Call Number	CAT @ cat @ VVB2008a			Serial	968
Permanent link to this record



	Author	Anna Salvatella; Maria Vanrell
	Title	Towards a texture representation database			Type	Report
	Year	2002	Publication	CVC Technical Report #60	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	CIC			Approved	no
	Call Number	CAT @ cat @ SaV2002			Serial	526
Permanent link to this record



	Author	Xavier Otazu; C. Alejandro Parraga; Maria Vanrell
	Title	Towards a unified chromatic inducction model			Type	Journal Article
	Year	2010	Publication	Journal of Vision	Abbreviated Journal	VSS
	Volume	10	Issue	12:5	Pages	1-24
	Keywords	Visual system; Color induction; Wavelet transform
	Abstract	In a previous work (X. Otazu, M. Vanrell, & C. A. Párraga, 2008b), we showed how several brightness induction effects can be predicted using a simple multiresolution wavelet model (BIWaM). Here we present a new model for chromatic induction processes (termed Chromatic Induction Wavelet Model or CIWaM), which is also implemented on a multiresolution framework and based on similar assumptions related to the spatial frequency and the contrast surround energy of the stimulus. The CIWaM can be interpreted as a very simple extension of the BIWaM to the chromatic channels, which in our case are defined in the MacLeod-Boynton (lsY) color space. This new model allows us to unify both chromatic assimilation and chromatic contrast effects in a single mathematical formulation. The predictions of the CIWaM were tested by means of several color and brightness induction experiments, which showed an acceptable agreement between model predictions and psychophysical 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	CIC			Approved	no
	Call Number	CAT @ cat @ OPV2010			Serial	1450
Permanent link to this record



	Author	Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
	Title	Towards a Videobronchoscopy Localization System from Airway Centre Tracking			Type	Conference Article
	Year	2017	Publication	12th International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	352-359
	Keywords	Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation
	Abstract	Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations.
	Address	Porto; Portugal; February 2017
	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	VISAPP
	Notes	IAM; 600.096; 600.075; 600.145			Approved	no
	Call Number	Admin @ si @ SEB2017			Serial	2943
Permanent link to this record



	Author	Zhengying Liu; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu
	Title	Towards automated computer vision: analysis of the AutoCV challenges 2019			Type	Journal Article
	Year	2020	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	135	Issue		Pages	196-203
	Keywords	Computer vision; AutoML; Deep learning
	Abstract	We present the results of recent challenges in Automated Computer Vision (AutoCV, renamed here for clarity AutoCV1 and AutoCV2, 2019), which are part of a series of challenge on Automated Deep Learning (AutoDL). These two competitions aim at searching for fully automated solutions for classification tasks in computer vision, with an emphasis on any-time performance. The first competition was limited to image classification while the second one included both images and videos. Our design imposed to the participants to submit their code on a challenge platform for blind testing on five datasets, both for training and testing, without any human intervention whatsoever. Winning solutions adopted deep learning techniques based on already published architectures, such as AutoAugment, MobileNet and ResNet, to reach state-of-the-art performance in the time budget of the challenge (only 20 minutes of GPU time). The novel contributions include strategies to deliver good preliminary results at any time during the learning process, such that a method can be stopped early and still deliver good performance. This feature is key for the adoption of such techniques by data analysts desiring to obtain rapidly preliminary results on large datasets and to speed up the development process. The soundness of our design was verified in several aspects: (1) Little overfitting of the on-line leaderboard providing feedback on 5 development datasets was observed, compared to the final blind testing on the 5 (separate) final test datasets, suggesting that winning solutions might generalize to other computer vision classification tasks; (2) Error bars on the winners’ performance allow us to say with confident that they performed significantly better than the baseline solutions we provided; (3) The ranking of participants according to the any-time metric we designed, namely the Area under the Learning Curve, was different from that of the fixed-time metric, i.e. AUC at the end of the fixed time budget. We released all winning solutions under open-source licenses. At the end of the AutoDL challenge series, all data of the challenge will be made publicly available, thus providing a collection of uniformly formatted datasets, which can serve to conduct further research, particularly on meta-learning.
	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	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ LXE2020			Serial	3427
Permanent link to this record



	Author	Zhengying Liu; Zhen Xu; Shangeth Rajaa; Meysam Madadi; Julio C. S. Jacques Junior; Sergio Escalera; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu; Isabelle Guyon
	Title	Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019			Type	Conference Article
	Year	2020	Publication	Proceedings of Machine Learning Research	Abbreviated Journal
	Volume	123	Issue		Pages	242-252
	Keywords
	Abstract	We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at {autodl.chalearn.org}).
	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	NEURIPS
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ LXR2020			Serial	3500
Permanent link to this record



	Author	Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin
	Title	Towards automatic and flexible concept transfer			Type	Journal Article
	Year	2012	Publication	Computers and Graphics	Abbreviated Journal	CG
	Volume	36	Issue	6	Pages	622–634
	Keywords
	Abstract	This paper introduces a novel approach to automatic, yet flexible, image concepttransfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The presented method modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This method is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. Our framework is flexible for two reasons. First, the user may select one of two modalities to map input image chromaticities to target concept chromaticities depending on the level of photo-realism required. Second, the user may adjust the intensity level of the concepttransfer to his/her liking with a single parameter. The proposed method uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. Results show that our approach yields transferred images which effectively represent concepts as confirmed by a user study.
	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	0097-8493	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ MSM2012			Serial	2002
Permanent link to this record



	Author	Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin
	Title	Towards Automatic Concept Transfer			Type	Conference Article
	Year	2011	Publication	Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering	Abbreviated Journal
	Volume		Issue		Pages	167.176
	Keywords	chromatic modeling, color concepts, color transfer, concept transfer
	Abstract	This paper introduces a novel approach to automatic concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The approach modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This approach is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study.
	Address
	Corporate Author				Thesis
	Publisher	ACM Press	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4503-0907-3	Medium
	Area		Expedition		Conference	NPAR
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ MSM2011			Serial	1866
Permanent link to this record



	Author	Pau Cano; Debora Gil; Eva Musulen
	Title	Towards automatic detection of helicobacter pylori in histological samples of gastric tissue			Type	Conference Article
	Year	2023	Publication	IEEE International Symposium on Biomedical Imaging	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Cartagena de Indias; Colombia; April 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	ISBI
	Notes	IAM			Approved	no
	Call Number	Admin @ si @ CGM2023			Serial	3953
Permanent link to this record



	Author	Mirko Arnold; Anarta Ghosh; Glen Doherty; Hugh Mulcahy; Stephen Patchett; Gerard Lacey
	Title	Towards Automatic Direct Observation of Procedure and Skill (DOPS) in Colonoscopy			Type	Conference Article
	Year	2013	Publication	Proceedings of the International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	48-53
	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	800	Expedition		Conference	VISIGRAPP
	Notes	MV			Approved	no
	Call Number	fernando @ fernando @			Serial	2427
Permanent link to this record



	Author	Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
	Title	Towards Automatic Polyp Detection with a Polyp Appearance Model			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	9	Pages	3166-3182
	Keywords	Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot
	Abstract	This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0031-3203	ISBN		Medium
	Area	800	Expedition		Conference	IbPRIA
	Notes	MV;SIAI			Approved	no
	Call Number	Admin @ si @ BSV2012; IAM @ iam			Serial	1997
Permanent link to this record



	Author	Josep M. Gonfaus
	Title	Towards Deep Image Understanding: From pixels to semantics			Type	Book Whole
	Year	2012	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Understanding the content of the images is one of the greatest challenges of computer vision. Recognition of objects appearing in images, identifying and interpreting their actions are the main purposes of Image Understanding. This thesis seeks to identify what is present in a picture by categorizing and locating all the objects in the scene. Images are composed by pixels, and one possibility consists of assigning to each pixel an object category, which is commonly known as semantic segmentation. By incorporating information as a contextual cue, we are able to resolve the ambiguity within categories at the pixel-level. We propose three levels of scale in order to resolve such ambiguity. Another possibility to represent the objects is the object detection task. In this case, the aim is to recognize and localize the whole object by accurately placing a bounding box around it. We present two new approaches. The first one is focused on improving the object representation of deformable part models with the concept of factorized appearances. The second approach addresses the issue of reducing the computational cost for multi-class recognition. The results given have been validated on several commonly used datasets, reaching international recognition and state-of-the-art within the field
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Jordi Gonzalez;Theo Gevers
	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 @ Gon2012			Serial	2208
Permanent link to this record



	Author	Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera
	Title	Towards Eating Habits Discovery in Egocentric Photo-Streams			Type	Journal Article
	Year	2021	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	9	Issue		Pages	17495-17506
	Keywords
	Abstract	Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals.
	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	Admin @ si @ MGR2021			Serial	3637
Permanent link to this record



	Author	Parichehr Behjati Ardakani
	Title	Towards Efficient and Robust Convolutional Neural Networks for Single Image Super-Resolution			Type	Book Whole
	Year	2022	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Single image super-resolution (SISR) is an important task in image processing which aims to enhance the resolution of imaging systems. Recently, SISR has witnessed great strides with the rapid development of deep learning. Recent advances in SISR are mostly devoted to designing deeper and wider networks to enhance their representation learning capacity. However, as the depth of networks increases, deep learning-based methods are faced with the challenge of computational complexity in practice. Moreover, most existing methods rarely leverage the intermediate features and also do not discriminate the computation of features by their frequencial components, thereby achieving relatively low performance. Aside from the aforementioned problems, another desired ability is to upsample images to arbitrary scales using a single model. Most current SISR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. In this thesis, we address the aforementioned issues and propose solutions to them: i) We present a novel frequency-based enhancement block which treats different frequencies in a heterogeneous way and also models inter-channel dependencies, which consequently enrich the output feature. Thus it helps the network generate more discriminative representations by explicitly recovering finer details. ii) We introduce OverNet which contains two main parts: a lightweight feature extractor that follows a novel recursive framework of skip and dense connections to reduce low-level feature degradation, and an overscaling module that generates an accurate SR image by internally constructing an overscaled intermediate representation of the output features. Then, to solve the problem of reconstruction at arbitrary scale factors, we introduce a novel multi-scale loss, that allows the simultaneous training of all scale factors using a single model. iii) We propose a directional variance attention network which leverages a novel attention mechanism to enhance features in different channels and spatial regions. Moreover, we introduce a novel procedure for using attention mechanisms together with residual blocks to facilitate the preservation of finer details. Finally, we demonstrate that our approaches achieve considerably better performance than previous state-of-the-art methods, in terms of both quantitative and visual quality.
	Address	April, 2022
	Corporate Author				Thesis	Ph.D. thesis
	Publisher		Place of Publication		Editor	Jordi Gonzalez;Xavier Roca;Pau Rodriguez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-124793-1-7	Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ Beh2022			Serial	3713
Permanent link to this record



	Author	Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva
	Title	Towards Egocentric Person Re-identification and Social Pattern Analysis.			Type	Book Chapter
	Year	2019	Publication	Frontiers in Artificial Intelligence and Applications	Abbreviated Journal
	Volume	310	Issue		Pages	203 - 211
	Keywords
	Abstract	CoRR abs/1905.04073 Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation.
	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	Admin @ si @ TCP2019			Serial	3377
Permanent link to this record