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Author |
Idoia Ruiz |
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Title |
Deep Metric Learning for re-identification, tracking and hierarchical novelty detection |
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Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Metric learning refers to the problem in machine learning of learning a distance or similarity measurement to compare data. In particular, deep metric learning involves learning a representation, also referred to as embedding, such that in the embedding space data samples can be compared based on the distance, directly providing a similarity measure. This step is necessary to perform several tasks in computer vision. It allows to perform the classification of images, regions or pixels, re-identification, out-of-distribution detection, object tracking in image sequences and any other task that requires computing a similarity score for their solution. This thesis addresses three specific problems that share this common requirement. The first one is person re-identification. Essentially, it is an image retrieval task that aims at finding instances of the same person according to a similarity measure. We first compare in terms of accuracy and efficiency, classical metric learning to basic deep learning based methods for this problem. In this context, we also study network distillation as a strategy to optimize the trade-off between accuracy and speed at inference time. The second problem we contribute to is novelty detection in image classification. It consists in detecting samples of novel classes, i.e. never seen during training. However, standard novelty detection does not provide any information about the novel samples besides they are unknown. Aiming at more informative outputs, we take advantage from the hierarchical taxonomies that are intrinsic to the classes. We propose a metric learning based approach that leverages the hierarchical relationships among classes during training, being able to predict the parent class for a novel sample in such hierarchical taxonomy. Our third contribution is in multi-object tracking and segmentation. This joint task comprises classification, detection, instance segmentation and tracking. Tracking can be formulated as a retrieval problem to be addressed with metric learning approaches. We tackle the existing difficulty in academic research that is the lack of annotated benchmarks for this task. To this matter, we introduce the problem of weakly supervised multi-object tracking and segmentation, facing the challenge of not having available ground truth for instance segmentation. We propose a synergistic training strategy that benefits from the knowledge of the supervised tasks that are being learnt simultaneously. |
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July, 2022 |
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Thesis |
Ph.D. thesis |
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Editor  |
Joan Serrat |
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978-84-124793-4-8 |
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ADAS |
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no |
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Call Number |
Admin @ si @ Rui2022 |
Serial |
3717 |
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Author |
Cristhian Aguilera; M.Ramos; Angel Sappa |
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Title |
Simulated Annealing: A Novel Application of Image Processing in the Wood Area |
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Book Chapter |
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Year |
2012 |
Publication |
Simulated Annealing – Advances, Applications and Hybridizations |
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Pages |
91-104 |
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Editor |
Marcos de Sales Guerra Tsuzuki |
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978-953-51-0710-1 |
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ADAS |
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no |
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Call Number |
Admin @ si @ ARS2012 |
Serial |
2156 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez |
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Title |
Moving object detection from mobile platforms using stereo data registration |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Computational Intelligence paradigms in advanced pattern classification |
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Volume |
386 |
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Pages |
25-37 |
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Keywords |
pedestrian detection |
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Abstract |
This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. |
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Springer Berlin Heidelberg |
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Editor |
Marek R. Ogiela; Lakhmi C. Jain |
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ISSN |
1860-949X |
ISBN |
978-3-642-24048-5 |
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ADAS |
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no |
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Call Number |
Admin @ si @ SGD2012 |
Serial |
2061 |
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Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
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Title |
Advances in Vision-Based Human Body Modeling |
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Book Chapter |
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Year |
2004 |
Publication |
3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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1-26 |
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Editor |
N. Sarris and M. Strintzis. |
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ISBN |
1-59140-299-9 |
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Notes |
ADAS |
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no |
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Call Number |
ADAS @ adas @ SAG2004a |
Serial |
458 |
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Author |
Ricardo Toledo |
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Title |
Cardiac workstation and dynamic model to assist in coronary tree analysis. |
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Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Thesis |
Ph.D. thesis |
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Editor |
Petia Radeva;JuanJose Villanueva |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ Tol2001 |
Serial |
166 |
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Author |
Monica Piñol |
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Title |
Reinforcement Learning of Visual Descriptors for Object Recognition |
Type |
Book Whole |
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Year |
2014 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
The human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Furthermore the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval. |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Ricardo Toledo;Angel Sappa |
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ISBN |
978-84-940902-5-7 |
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Notes |
ADAS; 600.076 |
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no |
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Call Number |
Admin @ si @ Piñ2014 |
Serial |
2464 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Title |
Photometric Invariance by Machine Learning |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Color in Computer Vision: Fundamentals and Applications |
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7 |
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Pages |
113-134 |
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Keywords |
road detection |
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Publisher |
iConcept Press Ltd |
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Editor |
Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek |
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ISBN |
978-0-470-89084-4 |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ AlL2012 |
Serial |
2186 |
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Author |
Aura Hernandez-Sabate; Debora Gil |
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Title |
The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Intravascular Ultrasound |
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Volume |
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Pages |
185-206 |
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Publisher |
Intech |
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Editor |
Yasuhiro Honda |
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Language |
English |
Summary Language |
english |
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978-953-307-900-4 |
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Notes |
IAM; ADAS |
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no |
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Call Number |
IAM @ iam @ HeG2012 |
Serial |
1684 |
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