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	Author	Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone
	Title	Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework			Type	Journal Article
	Year	2009	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	31	Issue	9	Pages	1630–1644
	Keywords
	Abstract	The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes.
	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	0162-8828	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ RVT2009			Serial	1220
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	Author	Lu Yu; Xialei Liu; Joost Van de Weijer
	Title	Self-Training for Class-Incremental Semantic Segmentation			Type	Journal Article
	Year	2022	Publication	IEEE Transactions on Neural Networks and Learning Systems	Abbreviated Journal	TNNLS
	Volume		Issue		Pages
	Keywords	Class-incremental learning; Self-training; Semantic segmentation.
	Abstract	In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art 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	LAMP; 600.147; 611.008;			Approved	no
	Call Number	Admin @ si @ YLW2022			Serial	3745
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	Author	Anjan Dutta; Hichem Sahbi
	Title	Stochastic Graphlet Embedding			Type	Journal Article
	Year	2018	Publication	IEEE Transactions on Neural Networks and Learning Systems	Abbreviated Journal	TNNLS
	Volume		Issue		Pages	1-14
	Keywords	Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality
	Abstract	Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
	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; 602.167; 602.168; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ DuS2018			Serial	3225
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	Author	Katerine Diaz; Francesc J. Ferri; W. Diaz
	Title	Incremental Generalized Discriminative Common Vectors for Image Classification			Type	Journal Article
	Year	2015	Publication	IEEE Transactions on Neural Networks and Learning Systems	Abbreviated Journal	TNNLS
	Volume	26	Issue	8	Pages	1761 - 1775
	Keywords
	Abstract	Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.
	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	2162-237X	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ DFD2015			Serial	2547
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	Author	Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez
	Title	Pay attention to the activations: a modular attention mechanism for fine-grained image recognition			Type	Journal Article
	Year	2020	Publication	IEEE Transactions on Multimedia	Abbreviated Journal	TMM
	Volume	22	Issue	2	Pages	502-514
	Keywords
	Abstract	Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%.
	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	ISE; 600.119; 600.098			Approved	no
	Call Number	Admin @ si @ RVC2020a			Serial	3417
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	Author	Weiqing Min; Shuqiang Jiang; Jitao Sang; Huayang Wang; Xinda Liu; Luis Herranz
	Title	Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration			Type	Journal Article
	Year	2017	Publication	IEEE Transactions on Multimedia	Abbreviated Journal	TMM
	Volume	19	Issue	5	Pages	1100 - 1113
	Keywords
	Abstract	This paper considers the problem of recipe-oriented image-ingredient correlation learning with multi-attributes for recipe retrieval and exploration. Existing methods mainly focus on food visual information for recognition while we model visual information, textual content (e.g., ingredients), and attributes (e.g., cuisine and course) together to solve extended recipe-oriented problems, such as multimodal cuisine classification and attribute-enhanced food image retrieval. As a solution, we propose a multimodal multitask deep belief network (M3TDBN) to learn joint image-ingredient representation regularized by different attributes. By grouping ingredients into visible ingredients (which are visible in the food image, e.g., “chicken” and “mushroom”) and nonvisible ingredients (e.g., “salt” and “oil”), M3TDBN is capable of learning both midlevel visual representation between images and visible ingredients and nonvisual representation. Furthermore, in order to utilize different attributes to improve the intermodality correlation, M3TDBN incorporates multitask learning to make different attributes collaborate each other. Based on the proposed M3TDBN, we exploit the derived deep features and the discovered correlations for three extended novel applications: 1) multimodal cuisine classification; 2) attribute-augmented cross-modal recipe image retrieval; and 3) ingredient and attribute inference from food images. The proposed approach is evaluated on the constructed Yummly dataset and the evaluation results have validated the effectiveness of the proposed approach.
	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; 600.120			Approved	no
	Call Number	Admin @ si @ MJS2017			Serial	2964
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	Author	Luis Herranz; Shuqiang Jiang; Ruihan Xu
	Title	Modeling Restaurant Context for Food Recognition			Type	Journal Article
	Year	2017	Publication	IEEE Transactions on Multimedia	Abbreviated Journal	TMM
	Volume	19	Issue	2	Pages	430 - 440
	Keywords
	Abstract	Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks.
	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; 600.120			Approved	no
	Call Number	Admin @ si @ HJX2017			Serial	2965
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	Author	Ferran Diego; Joan Serrat; Antonio Lopez
	Title	Joint spatio-temporal alignment of sequences			Type	Journal Article
	Year	2013	Publication	IEEE Transactions on Multimedia	Abbreviated Journal	TMM
	Volume	15	Issue	6	Pages	1377-1387
	Keywords	video alignment
	Abstract	Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times.
	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	1520-9210	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ DSL2013; ADAS @ adas @			Serial	2228
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	Author	Jorge Bernal; Nima Tajkbaksh; F. Javier Sanchez; Bogdan J. Matuszewski; Hao Chen; Lequan Yu; Quentin Angermann; Olivier Romain; Bjorn Rustad; Ilangko Balasingham; Konstantin Pogorelov; Sungbin Choi; Quentin Debard; Lena Maier Hein; Stefanie Speidel; Danail Stoyanov; Patrick Brandao; Henry Cordova; Cristina Sanchez Montes; Suryakanth R. Gurudu; Gloria Fernandez Esparrach; Xavier Dray; Jianming Liang; Aymeric Histace
	Title	Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge			Type	Journal Article
	Year	2017	Publication	IEEE Transactions on Medical Imaging	Abbreviated Journal	TMI
	Volume	36	Issue	6	Pages	1231 - 1249
	Keywords	Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework
	Abstract	Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.
	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	MV; 600.096; 600.075			Approved	no
	Call Number	Admin @ si @ BTS2017			Serial	2949
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	Author	Jaume Garcia; Debora Gil; Luis Badiella; Aura Hernandez-Sabate; Francesc Carreras; Sandra Pujades; Enric Marti
	Title	A Normalized Framework for the Design of Feature Spaces Assessing the Left Ventricular Function			Type	Journal Article
	Year	2010	Publication	IEEE Transactions on Medical Imaging	Abbreviated Journal	TMI
	Volume	29	Issue	3	Pages	733-745
	Keywords
	Abstract	A through description of the left ventricle functionality requires combining complementary regional scores. A main limitation is the lack of multiparametric normality models oriented to the assessment of regional wall motion abnormalities (RWMA). This paper covers two main topics involved in RWMA assessment. We propose a general framework allowing the fusion and comparison across subjects of different regional scores. Our framework is used to explore which combination of regional scores (including 2-D motion and strains) is better suited for RWMA detection. Our statistical analysis indicates that for a proper (within interobserver variability) identification of RWMA, models should consider motion and extreme strains.
	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	0278-0062	ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM			Approved	no
	Call Number	IAM @ iam @ GGH2010b			Serial	1507
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	Author	Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva
	Title	Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions			Type	Journal Article
	Year	2010	Publication	IEEE Transactions on Medical Imaging	Abbreviated Journal	TMI
	Volume	29	Issue	2	Pages	246-259
	Keywords
	Abstract	Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions.
	Address
	Corporate Author	IEEE			Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0278-0062	ISBN		Medium
	Area	800	Expedition		Conference
	Notes	MILAB;MV;OR;SIAI			Approved	no
	Call Number	BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010			Serial	1281
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	Author	Aura Hernandez-Sabate; Debora Gil;Eduard Fernandez-Nofrerias;Petia Radeva; Enric Marti
	Title	Approaching Artery Rigid Dynamics in IVUS			Type	Journal Article
	Year	2009	Publication	IEEE Transactions on Medical Imaging	Abbreviated Journal	TMI
	Volume	28	Issue	11	Pages	1670-1680
	Keywords	Fourier analysis; intravascular ultrasound (IVUS) dynamics; longitudinal motion; quality measures; tissue deformation.
	Abstract	Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel-catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical 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	0278-0062	ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM; MILAB			Approved	no
	Call Number	IAM @ iam @ HGF2009			Serial	1545
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	Author	Debora Gil; Oriol Rodriguez-Leon; Petia Radeva; Josepa Mauri
	Title	Myocardial Perfusion Characterization From Contrast Angiography Spectral Distribution			Type	Journal Article
	Year	2008	Publication	IEEE Transactions on Medical Imaging	Abbreviated Journal
	Volume	27	Issue	5	Pages	641-649
	Keywords	Contrast angiography; myocardial perfusion; spectral analysis.
	Abstract	Despite recovering a normal coronary flow after acute myocardial infarction, percutaneous coronary intervention does not guarantee a proper perfusion (irrigation) of the infarcted area. This damage in microcirculation integrity may detrimentally affect the patient survival. Visual assessment of the myocardium opacification in contrast angiography serves to define a subjective score of the microcirculation integrity myocardial blush analysis (MBA). Although MBA correlates with patient prognosis its visual assessment is a very difficult task that requires of a highly expertise training in order to achieve a good intraobserver and interobserver agreement. In this paper, we provide objective descriptors of the myocardium staining pattern by analyzing the spectrum of the image local statistics. The descriptors proposed discriminate among the different phenomena observed in the angiographic sequence and allow defining an objective score of the myocardial perfusion.
	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	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ GRR2008			Serial	1541
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	Author	Debora Gil; Aura Hernandez-Sabate; Oriol Rodriguez; Josepa Mauri; Petia Radeva
	Title	Statistical Strategy for Anisotropic Adventitia Modelling in IVUS			Type	Journal Article
	Year	2006	Publication	IEEE Transactions on Medical Imaging	Abbreviated Journal
	Volume	25	Issue	6	Pages	768-778
	Keywords	Corners; T-junctions; Wavelets
	Abstract	Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and mediaadventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders. Index Terms–-Anisotropic processing, intravascular ultrasound (IVUS), vessel border segmentation, vessel structure classification.
	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	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ GHR2006			Serial	1525
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	Author	Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez
	Title	Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision			Type	Journal Article
	Year	2021	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	23	Issue	8	Pages	12738-12751
	Keywords
	Abstract	Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.
	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	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ GTS2021			Serial	3598
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