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	Author	David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras
	Title	Visual Registration Method For A Low Cost Robot: Computer Vision Systems			Type	Conference Article
	Year	2009	Publication	7th International Conference on Computer Vision Systems	Abbreviated Journal
	Volume	5815	Issue		Pages	204–214
	Keywords
	Abstract	An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance.
	Address	Belgica
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-04666-7	Medium
	Area		Expedition		Conference	ICVS
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ ATR2009b			Serial	1247
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	Author	Naveen Onkarappa; Angel Sappa
	Title	On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow			Type	Conference Article
	Year	2010	Publication	7th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	6111	Issue		Pages	230-239
	Keywords
	Abstract	This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach.
	Address	Povoa de Varzim (Portugal)
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-13771-6	Medium
	Area		Expedition		Conference	ICIAR
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ OnS2010			Serial	1342
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	Author	Muhammad Anwer Rao; David Vazquez; Antonio Lopez
	Title	Color Contribution to Part-Based Person Detection in Different Types of Scenarios			Type	Conference Article
	Year	2011	Publication	14th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	6855	Issue	II	Pages	463-470
	Keywords	Pedestrian Detection; Color
	Abstract	Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution.
	Address	Seville, Spain
	Corporate Author				Thesis
	Publisher	Springer	Place of Publication	Berlin Heidelberg	Editor	P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch
	Language	English	Summary Language	english	Original Title	Color Contribution to Part-Based Person Detection in Different Types of Scenarios
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-23677-8	Medium
	Area		Expedition		Conference	CAIP
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ RVL2011b			Serial	1665
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	Author	Naveen Onkarappa; Angel Sappa
	Title	Space Variant Representations for Mobile Platform Vision Applications			Type	Conference Article
	Year	2011	Publication	14th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	6855	Issue	II	Pages	146-154
	Keywords
	Abstract	The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
	Address	Seville, Spain
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-23677-8	Medium
	Area		Expedition		Conference	CAIP
	Notes	ADAS			Approved	no
	Call Number	NaS2011; ADAS @ adas @			Serial	1686
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	Author	Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers
	Title	Improving HOG with Image Segmentation: Application to Human Detection			Type	Conference Article
	Year	2012	Publication	11th International Conference on Advanced Concepts for Intelligent Vision Systems	Abbreviated Journal
	Volume	7517	Issue		Pages	178-189
	Keywords	Segmentation; Pedestrian Detection
	Abstract	In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement. We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
	Address	Brno, Czech Republic
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	J. Blanc-Talon et al.
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-33139-8	Medium
	Area		Expedition		Conference	ACIVS
	Notes	ADAS;ISE			Approved	no
	Call Number	ADAS @ adas @ SLV2012			Serial	1980
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	Author	Fernando Barrera; Felipe Lumbreras; Angel Sappa
	Title	Evaluation of Similarity Functions in Multimodal Stereo			Type	Conference Article
	Year	2012	Publication	9th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	7324	Issue	I	Pages	320-329
	Keywords	Aveiro, Portugal
	Abstract	This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-31294-6	Medium
	Area		Expedition		Conference	ICIAR
	Notes	ADAS			Approved	no
	Call Number	BLS2012a			Serial	2014
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	Author	Miguel Oliveira; Angel Sappa; V. Santos
	Title	Color Correction using 3D Gaussian Mixture Models			Type	Conference Article
	Year	2012	Publication	9th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	7324	Issue	I	Pages	97-106
	Keywords
	Abstract	The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	10.1007/978-3-642-31295-3_12	Medium
	Area		Expedition		Conference	ICIAR
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ OSS2012a			Serial	2015
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	Author	Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez
	Title	Road Scene Segmentation from a Single Image			Type	Conference Article
	Year	2012	Publication	12th European Conference on Computer Vision	Abbreviated Journal
	Volume	7578	Issue	VII	Pages	376-389
	Keywords	road detection
	Abstract	Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding. In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images. From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined
	Address	Florence, Italy
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-33785-7	Medium
	Area		Expedition		Conference	ECCV
	Notes	ADAS;ISE			Approved	no
	Call Number	Admin @ si @ AGL2012; ADAS @ adas @ agl2012a			Serial	2022
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	Author	Naveen Onkarappa; Angel Sappa
	Title	Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario			Type	Conference Article
	Year	2013	Publication	15th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	8048	Issue		Pages	483-490
	Keywords	Optical flow; regularization; Driver Assistance Systems; Performance Evaluation
	Abstract	Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
	Address	York; UK; August 2013
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-40245-6	Medium
	Area		Expedition		Conference	CAIP
	Notes	ADAS; 600.055; 601.215			Approved	no
	Call Number	Admin @ si @ OnS2013b			Serial	2244
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	Author	Karel Paleček; David Geronimo; Frederic Lerasle
	Title	Pre-attention cues for person detection			Type	Conference Article
	Year	2012	Publication	Cognitive Behavioural Systems, COST 2102 International Training School	Abbreviated Journal
	Volume		Issue		Pages	225-235
	Keywords
	Abstract	Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.
	Address	Dresden, Germany
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-34583-8	Medium
	Area		Expedition		Conference	COST-TS
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ PGL2012			Serial	2148
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