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Author Sergio Escalera
Title (up) Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A Type Book Whole
Year 2008 Publication PhD Thesis, Universitat de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However,
the extension of many 2-class classifiers to the multi-class case is a hard task. In
this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful
tool to combine any number of binary classifiers to model multi-class problems. But
there are still many open issues about the capabilities of the ECOC framework. In
this thesis, the two main stages of an ECOC design are analyzed: the coding and
the decoding steps. We present different problem-dependent designs. These designs
take advantage of the knowledge of the problem domain to minimize the number
of classifiers, obtaining a high classification performance. On the other hand, we
analyze the ECOC codification in order to define new decoding rules that take full
benefit from the information provided at the coding step. Moreover, as a successful
classification requires a rich feature set, new feature detection/extraction techniques
are presented and evaluated on the new ECOC designs. The evaluation of the new
methodology is performed on different real and synthetic data sets: UCI Machine
Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease
data set. The results of this thesis show that significant performance improvements
are obtained on both traditional coding and decoding ECOC designs when the new
coding and decoding rules are taken into account.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Petia Radeva;Oriol Pujol
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; HuPBA Approved no
Call Number Admin @ si @ Esc2008b Serial 2217
Permanent link to this record
 
 
Author Muhammad Anwer Rao
Title (up) Color for Object Detection and Action Recognition Type Book Whole
Year 2013 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Recognizing object categories in real world images is a challenging problem in computer vision. The deformable part based framework is currently the most successful approach for object detection. Generally, HOG are used for image representation within the part-based framework. For action recognition, the bag-of-word framework has shown to provide promising results. Within the bag-of-words framework, local image patches are described by SIFT descriptor. Contrary to object detection and action recognition, combining color and shape has shown to provide the best performance for object and scene recognition.

In the first part of this thesis, we analyze the problem of person detection in still images. Standard person detection approaches rely on intensity based features for image representation while ignoring the color. Channel based descriptors is one of the most commonly used approaches in object recognition. This inspires us to evaluate incorporating color information using the channel based fusion approach for the task of person detection.

In the second part of the thesis, we investigate the problem of object detection in still images. Due to high dimensionality, channel based fusion increases the computational cost. Moreover, channel based fusion has been found to obtain inferior results for object category where one of the visual varies significantly. On the other hand, late fusion is known to provide improved results for a wide range of object categories. A consequence of late fusion strategy is the need of a pure color descriptor. Therefore, we propose to use Color attributes as an explicit color representation for object detection. Color attributes are compact and computationally efficient. Consequently color attributes are combined with traditional shape features providing excellent results for object detection task.

Finally, we focus on the problem of action detection and classification in still images. We investigate the potential of color for action classification and detection in still images. We also evaluate different fusion approaches for combining color and shape information for action recognition. Additionally, an analysis is performed to validate the contribution of color for action recognition. Our results clearly demonstrate that combining color and shape information significantly improve the performance of both action classification and detection in still images.
Address Barcelona
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;Joost Van de Weijer
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 @ Rao2013 Serial 2281
Permanent link to this record
 
 
Author Theo Gevers; Arjan Gijsenij; Joost Van de Weijer; J.M. Geusebroek
Title (up) Color in Computer Vision: Fundamentals and Applications Type Book Whole
Year 2012 Publication Color in Computer Vision: Fundamentals and Applications Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher The Wiley-IS&T Series in Imaging Science and Technology Place of Publication Editor
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 ALTRES;ISE Approved no
Call Number Admin @ si @ GGG2012a Serial 2068
Permanent link to this record
 
 
Author Ivet Rafegas
Title (up) Color in Visual Recognition: from flat to deep representations and some biological parallelisms Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.

Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations.

Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias.
Address November 2017
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Maria Vanrell
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-7-0 Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ Raf2017 Serial 3100
Permanent link to this record
 
 
Author Fahad Shahbaz Khan
Title (up) Coloring bag-of-words based image representations Type Book Whole
Year 2011 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Put succinctly, the bag-of-words based image representation is the most successful approach for object and scene recognition. Within the bag-of-words framework the optimal fusion of multiple cues, such as shape, texture and color, still remains an active research domain. There exist two main approaches to combine color and shape information within the bag-of-words framework. The first approach called, early fusion, fuses color and shape at the feature level as a result of which a joint colorshape vocabulary is produced. The second approach, called late fusion, concatenates histogram representation of both color and shape, obtained independently. In the first part of this thesis, we analyze the theoretical implications of both early and late feature fusion. We demonstrate that both these approaches are suboptimal for a subset of object categories. Consequently, we propose a novel method for recognizing object categories when using multiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom-up and top-down attention maps. Subsequently, the color attention maps are used to modulate the weights of the shape features. Shape features are given more weight in regions with higher attention and vice versa. The approach is tested on several benchmark object recognition data sets and the results clearly demonstrate the effectiveness of our proposed method. In the second part of the thesis, we investigate the problem of obtaining compact spatial pyramid representations for object and scene recognition. Spatial pyramids have been successfully applied to incorporate spatial information into bag-of-words based image representation. However, a major drawback of spatial pyramids is that it leads to high dimensional image representations. We present a novel framework for obtaining compact pyramid representation. The approach reduces the size of a high dimensional pyramid representation upto an order of magnitude without any significant reduction in accuracy. Moreover, we also investigate the optimal combination of multiple features such as color and shape within the context of our compact pyramid representation. Finally, we describe a novel technique to build discriminative visual words from multiple cues learned independently from training images. To this end, we use an information theoretic vocabulary compression technique to find discriminative combinations of visual cues and the resulting visual vocabulary is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. The approach is tested on standard object recognition data sets. The results obtained clearly demonstrate the effectiveness of our approach.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Joost Van de Weijer;Maria Vanrell
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 Admin @ si @ Kha2011 Serial 1838
Permanent link to this record
 
 
Author Javier Vazquez
Title (up) Colour Constancy in Natural Through Colour Naming and Sensor Sharpening Type Book Whole
Year 2011 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Colour is derived from three physical properties: incident light, object reflectance and sensor sensitivities. Incident light varies under natural conditions; hence, recovering scene illuminant is an important issue in computational colour. One way to deal with this problem under calibrated conditions is by following three steps, 1) building a narrow-band sensor basis to accomplish the diagonal model, 2) building a feasible set of illuminants, and 3) defining criteria to select the best illuminant. In this work we focus on colour constancy for natural images by introducing perceptual criteria in the first and third stages.
To deal with the illuminant selection step, we hypothesise that basic colour categories can be used as anchor categories to recover the best illuminant. These colour names are related to the way that the human visual system has evolved to encode relevant natural colour statistics. Therefore the recovered image provides the best representation of the scene labelled with the basic colour terms. We demonstrate with several experiments how this selection criterion achieves current state-of-art results in computational colour constancy. In addition to this result, we psychophysically prove that usual angular error used in colour constancy does not correlate with human preferences, and we propose a new perceptual colour constancy evaluation.
The implementation of this selection criterion strongly relies on the use of a diagonal
model for illuminant change. Consequently, the second contribution focuses on building an appropriate narrow-band sensor basis to represent natural images. We propose to use the spectral sharpening technique to compute a unique narrow-band basis optimised to represent a large set of natural reflectances under natural illuminants and given in the basis of human cones. The proposed sensors allow predicting unique hues and the World colour Survey data independently of the illuminant by using a compact singularity function. Additionally, we studied different families of sharp sensors to minimise different perceptual measures. This study brought us to extend the spherical sampling procedure from 3D to 6D.
Several research lines still remain open. One natural extension would be to measure the
effects of using the computed sharp sensors on the category hypothesis, while another might be to insert spatial contextual information to improve category hypothesis. Finally, much work still needs to be done to explore how individual sensors can be adjusted to the colours in a scene.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Maria Vanrell;Graham D. Finlayson
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 Admin @ si @ Vaz2011a Serial 1785
Permanent link to this record
 
 
Author Jose Manuel Alvarez
Title (up) Combining Context and Appearance for Road Detection Type Book Whole
Year 2010 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Road traffic crashes have become a major cause of death and injury throughout the world.
Hence, in order to improve road safety, the automobile manufacture is moving towards the
development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit
color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene.
In this way, we present two different approaches to infer the geometry of the road ahead
the moving vehicle. Finally, we focus on combining these context and appearance (color)
approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;Theo Gevers
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-937261-8-8 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Alv2010 Serial 1454
Permanent link to this record
 
 
Author Noha Elfiky
Title (up) Compact, Adaptive and Discriminative Spatial Pyramids for Improved Object and Scene Classification Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The release of challenging datasets with a vast number of images, requires the development of efficient image representations and algorithms which are able to manipulate these large-scale datasets efficiently. Nowadays the Bag-of-Words (BoW) is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based image representation. Observing the remarkable performance of spatial pyramids, their growing number of applications to a broad range of vision problems, and finally its geometry inclusion, a question can be asked what are the limits of spatial pyramids. Within the SP framework, the optimal way for obtaining an image spatial representation, which is able to cope with it’s most foremost shortcomings, concretely, it’s high dimensionality and the rigidity of the resulting image representation, still remains an active research domain. In summary, the main concern of this thesis is to search for the limits of spatial pyramids and try to figure out solutions for them.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Xavier Roca
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 @ Elf2012 Serial 2202
Permanent link to this record
 
 
Author Arash Akbarinia
Title (up) Computational Model of Visual Perception: From Colour to Form Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The original idea of this project was to study the role of colour in the challenging task of object recognition. We started by extending previous research on colour naming showing that it is feasible to capture colour terms through parsimonious ellipsoids. Although, the results of our model exceeded state-of-the-art in two benchmark datasets, we realised that the two phenomena of metameric lights and colour constancy must be addressed prior to any further colour processing. Our investigation of metameric pairs reached the conclusion that they are infrequent in real world scenarios. Contrary to that, the illumination of a scene often changes dramatically. We addressed this issue by proposing a colour constancy model inspired by the dynamical centre-surround adaptation of neurons in the visual cortex. This was implemented through two overlapping asymmetric Gaussians whose variances and heights are adjusted according to the local contrast of pixels. We complemented this model with a generic contrast-variant pooling mechanism that inversely connect the percentage of pooled signal to the local contrast of a region. The results of our experiments on four benchmark datasets were indeed promising: the proposed model, although simple, outperformed even learning-based approaches in many cases. Encouraged by the success of our contrast-variant surround modulation, we extended this approach to detect boundaries of objects. We proposed an edge detection model based on the first derivative of the Gaussian kernel. We incorporated four types of surround: full, far, iso- and orthogonal-orientation. Furthermore, we accounted for the pooling mechanism at higher cortical areas and the shape feedback sent to lower areas. Our results in three benchmark datasets showed significant improvement over non-learning algorithms.
To summarise, we demonstrated that biologically-inspired models offer promising solutions to computer vision problems, such as, colour naming, colour constancy and edge detection. We believe that the greatest contribution of this Ph.D dissertation is modelling the concept of dynamic surround modulation that shows the significance of contrast-variant surround integration. The models proposed here are grounded on only a portion of what we know about the human visual system. Therefore, it is only natural to complement them accordingly in future works.
Address October 2017
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor C. Alejandro Parraga
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-4-9 Medium
Area Expedition Conference
Notes NEUROBIT Approved no
Call Number Admin @ si @ Akb2017 Serial 3019
Permanent link to this record
 
 
Author Angel Sappa (ed)
Title (up) Computer Graphics and Imaging Type Book Whole
Year 2010 Publication Computer Graphics and Imaging Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978–0–88986–836–6 Medium
Area Expedition Conference CGIM
Notes ADAS Approved no
Call Number ADAS @ adas @ Sap2010 Serial 1468
Permanent link to this record
 
 
Author Angel Sappa; George A. Triantafyllid
Title (up) Computer Graphics and Imaging Type Book Whole
Year 2012 Publication Computer Graphics and Imaging Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Crete, Greece
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 978-0-88986-921-9 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Sap2012 Serial 2067
Permanent link to this record
 
 
Author Ferran Poveda
Title (up) Computer Graphics and Vision Techniques for the Study of the Muscular Fiber Architecture of the Myocardium Type Book Whole
Year 2013 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Debora Gil;Enric Marti
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 Approved no
Call Number Admin @ si @ Pov2013 Serial 2417
Permanent link to this record
 
 
Author Michael Teutsch; Angel Sappa; Riad I. Hammoud
Title (up) Computer Vision in the Infrared Spectrum: Challenges and Approaches Type Book Whole
Year 2021 Publication Synthesis Lectures on Computer Vision Abbreviated Journal
Volume 10 Issue 2 Pages 1-138
Keywords
Abstract Human visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can provably increase the robustness of many autonomous systems. Applications that can benefit from utilizing infrared imagery range from robotics to automotive and from biometrics to surveillance. In this book, we provide a brief yet concise introduction to the current state-of-the-art of computer vision and machine learning in the infrared spectrum. Based on various popular computer vision tasks such as image enhancement, object detection, or object tracking, we first motivate each task starting from established literature in the visual-optical spectrum. Then, we discuss the differences between processing images and videos in the visual-optical spectrum and the various infrared spectra. An overview of the current literature is provided together with an outlook for each task. Furthermore, available and annotated public datasets and common evaluation methods and metrics are presented. In a separate chapter, popular applications that can greatly benefit from the use of infrared imagery as a data source are presented and discussed. Among them are automatic target recognition, video surveillance, or biometrics including face recognition. Finally, we conclude with recommendations for well-fitting sensor setups and data processing algorithms for certain computer vision tasks. We address this book to prospective researchers and engineers new to the field but also to anyone who wants to get introduced to the challenges and the approaches of computer vision using infrared images or videos. Readers will be able to start their work directly after reading the book supported by a highly comprehensive backlog of recent and relevant literature as well as related infrared datasets including existing evaluation frameworks. Together with consistently decreasing costs for infrared cameras, new fields of application appear and make computer vision in the infrared spectrum a great opportunity to face nowadays scientific and engineering challenges.
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 978-1636392431 Medium
Area Expedition Conference
Notes MSIAU Approved no
Call Number Admin @ si @ TSH2021 Serial 3666
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Author Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez
Title (up) Computer Vision in Vehicle Technology: Land, Sea & Air Type Book Whole
Year 2017 Publication Abbreviated Journal
Volume Issue Pages 161-163
Keywords
Abstract Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition.
Address
Corporate Author Thesis
Publisher John Wiley & Sons, Ltd Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-118-86807-2 Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ LIP2017a Serial 2937
Permanent link to this record
 
 
Author Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez
Title (up) Computer Vision in Vehicle Technology: Land, Sea & Air Type Book Whole
Year Publication Computer Vision in Vehicle Technology: Land, Sea & Air Abbreviated Journal
Volume Issue Pages
Keywords
Abstract A unified view of the use of computer vision technology for different types of vehicles

Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).

The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed.
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 978-1-118-86807-2 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ LIP2017b Serial 3049
Permanent link to this record