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Author |
Gabriel Villalonga |
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Title |
Leveraging Synthetic Data to Create Autonomous Driving Perception Systems |
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Book Whole |
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Year |
2021 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
Manually annotating images to develop vision models has been a major bottleneck
since computer vision and machine learning started to walk together. This has
been more evident since computer vision falls on the shoulders of data-hungry
deep learning techniques. When addressing on-board perception for autonomous
driving, the curse of data annotation is exacerbated due to the use of additional
sensors such as LiDAR. Therefore, any approach aiming at reducing such a timeconsuming and costly work is of high interest for addressing autonomous driving
and, in fact, for any application requiring some sort of artificial perception. In the
last decade, it has been shown that leveraging from synthetic data is a paradigm
worth to pursue in order to minimizing manual data annotation. The reason is
that the automatic process of generating synthetic data can also produce different
types of associated annotations (e.g. object bounding boxes for synthetic images
and LiDAR pointclouds, pixel/point-wise semantic information, etc.). Directly
using synthetic data for training deep perception models may not be the definitive
solution in all circumstances since it can appear a synth-to-real domain shift. In
this context, this work focuses on leveraging synthetic data to alleviate manual
annotation for three perception tasks related to driving assistance and autonomous
driving. In all cases, we assume the use of deep convolutional neural networks
(CNNs) to develop our perception models.
The first task addresses traffic sign recognition (TSR), a kind of multi-class
classification problem. We assume that the number of sign classes to be recognized
must be suddenly increased without having annotated samples to perform the
corresponding TSR CNN re-training. We show that leveraging synthetic samples of
such new classes and transforming them by a generative adversarial network (GAN)
trained on the known classes (i.e. without using samples from the new classes), it is
possible to re-train the TSR CNN to properly classify all the signs for a ∼ 1/4 ratio of
new/known sign classes. The second task addresses on-board 2D object detection,
focusing on vehicles and pedestrians. In this case, we assume that we receive a set
of images without the annotations required to train an object detector, i.e. without
object bounding boxes. Therefore, our goal is to self-annotate these images so
that they can later be used to train the desired object detector. In order to reach
this goal, we leverage from synthetic data and propose a semi-supervised learning
approach based on the co-training idea. In fact, we use a GAN to reduce the synthto-real domain shift before applying co-training. Our quantitative results show
that co-training and GAN-based image-to-image translation complement each
other up to allow the training of object detectors without manual annotation, and still almost reaching the upper-bound performances of the detectors trained from
human annotations. While in previous tasks we focus on vision-based perception,
the third task we address focuses on LiDAR pointclouds. Our initial goal was to
develop a 3D object detector trained on synthetic LiDAR-style pointclouds. While
for images we may expect synth/real-to-real domain shift due to differences in
their appearance (e.g. when source and target images come from different camera
sensors), we did not expect so for LiDAR pointclouds since these active sensors
factor out appearance and provide sampled shapes. However, in practice, we have
seen that it can be domain shift even among real-world LiDAR pointclouds. Factors
such as the sampling parameters of the LiDARs, the sensor suite configuration onboard the ego-vehicle, and the human annotation of 3D bounding boxes, do induce
a domain shift. We show it through comprehensive experiments with different
publicly available datasets and 3D detectors. This redirected our goal towards the
design of a GAN for pointcloud-to-pointcloud translation, a relatively unexplored
topic.
Finally, it is worth to mention that all the synthetic datasets used for these three
tasks, have been designed and generated in the context of this PhD work and will
be publicly released. Overall, we think this PhD presents several steps forward to
encourage leveraging synthetic data for developing deep perception models in the
field of driving assistance and autonomous driving. |
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Address |
February 2021 |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Antonio Lopez;German Ros |
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ISBN |
978-84-122714-2-3 |
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Notes  |
ADAS; 600.118 |
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no |
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Call Number |
Admin @ si @ Vil2021 |
Serial |
3599 |
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Author |
Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil |
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Title |
Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging |
Type |
Book Chapter |
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Year |
2015 |
Publication |
Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 |
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Volume |
9534 |
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Pages |
69-79 |
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Abstract |
Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm. |
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Munich; Germany; January 2015 |
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Publisher |
Springer International Publishing |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-319-28711-9 |
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Conference |
STACOM |
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Notes |
ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
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no |
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Call Number |
Admin @ si @ KHM2015 |
Serial |
2734 |
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Author |
Angel Sappa; Jordi Vitria |
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Title |
Multimodal Interaction in Image and Video Applications |
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Book Whole |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
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48 |
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Abstract |
Book Series Intelligent Systems Reference Library |
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Publisher |
Springer Berlin Heidelberg |
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ISSN |
1868-4394 |
ISBN |
978-3-642-35931-6 |
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Notes |
ADAS; OR;MV |
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no |
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Call Number |
Admin @ si @ SaV2013 |
Serial |
2199 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution texture classification of ceramic tiles. |
Type |
Book Chapter |
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Year |
1999 |
Publication |
Recent Research developments in optical engineering, Research Signpost, 2: 213–228 |
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Address |
India |
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Notes |
ADAS;CIC |
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no |
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Call Number |
ADAS @ adas @ LBV1999b |
Serial |
45 |
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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
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Title |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
8746 |
Issue |
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Pages |
135-146 |
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Keywords |
Graphics recognition; Graphics retrieval; Image classification |
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Abstract |
This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-662-44853-3 |
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Notes |
DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 |
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no |
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Call Number |
Admin @ si @ HFF2014 |
Serial |
2536 |
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Author |
Alicia Fornes; Gemma Sanchez |
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Title |
Analysis and Recognition of Music Scores |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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Volume |
E |
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Pages |
749-774 |
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Abstract |
The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented. |
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Springer London |
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Editor |
D. Doermann; K. Tombre |
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978-0-85729-860-7 |
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Notes |
DAG; ADAS; 600.076; 600.077 |
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no |
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Call Number |
Admin @ si @ FoS2014 |
Serial |
2484 |
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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Title |
Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
8746 |
Issue |
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Pages |
109-121 |
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Keywords |
Graphics recognition; Floor plan analysis; Object segmentation |
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Abstract |
In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions. |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Series Volume |
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ISSN |
0302-9743 |
ISBN |
978-3-662-44853-3 |
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Conference |
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Notes |
DAG; ADAS; 600.076; 600.077 |
Approved |
no |
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Call Number |
Admin @ si @ HVS2014 |
Serial |
2535 |
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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 |
Place of Publication |
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Editor |
Yasuhiro Honda |
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Language |
English |
Summary Language |
english |
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ISBN |
978-953-307-900-4 |
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Notes |
IAM; ADAS |
Approved |
no |
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Call Number |
IAM @ iam @ HeG2012 |
Serial |
1684 |
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