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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	Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo
	Title	Multispectral Stereo Image Correspondence			Type	Conference Article
	Year	2013	Publication	15th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	8048	Issue		Pages	217-224
	Keywords
	Abstract	This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.
	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			Approved	no
	Call Number	Admin @ si @ PST2013			Serial	2561
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	Author	Gioacchino Vino; Angel Sappa
	Title	Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach			Type	Conference Article
	Year	2013	Publication	10th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	7950	Issue		Pages	354-363
	Keywords
	Abstract	This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
	Address	Póvoa de Varzim; Portugal; June 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	978-3-642-39093-7	Medium
	Area		Expedition		Conference	ICIAR
	Notes	ADAS; 600.055			Approved	no
	Call Number	Admin @ si @ ViS2013			Serial	2562
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	Author	Jose Carlos Rubio; Joan Serrat; Antonio Lopez
	Title	Video Co-segmentation			Type	Conference Article
	Year	2012	Publication	11th Asian Conference on Computer Vision	Abbreviated Journal
	Volume	7725	Issue		Pages	13-24
	Keywords
	Abstract	Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos.
	Address	Daejeon, Korea
	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	978-3-642-37443-2	Medium
	Area		Expedition		Conference	ACCV
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ RSL2012d			Serial	2153
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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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	Author	Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez
	Title	Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features			Type	Conference Article
	Year	2012	Publication	12th European Conference on Computer Vision – Workshops and Demonstrations	Abbreviated Journal
	Volume	7584	Issue		Pages	586-595
	Keywords	road detection
	Abstract	Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process. Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo.
	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	978-3-642-33867-0	Medium
	Area		Expedition		Conference	ECCVW
	Notes	ADAS;ISE			Approved	no
	Call Number	Admin @ si @ ALG2012; ADAS @ adas			Serial	2187
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	Author	Patricia Marquez;Debora Gil;Aura Hernandez-Sabate
	Title	A Complete Confidence Framework for Optical Flow			Type	Conference Article
	Year	2012	Publication	12th European Conference on Computer Vision – Workshops and Demonstrations	Abbreviated Journal
	Volume	7584	Issue	2	Pages	124-133
	Keywords	Optical flow, confidence measures, sparsification plots, error prediction plots
	Abstract	Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.
	Address
	Corporate Author				Thesis
	Publisher	Springer-Verlag	Place of Publication	Florence, Italy, October 7-13, 2012	Editor	Andrea Fusiello, Vittorio Murino ,Rita Cucchiara
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-33867-0	Medium
	Area		Expedition		Conference	ECCVW
	Notes	IAM;ADAS;			Approved	no
	Call Number	IAM @ iam @ MGH2012b			Serial	1991
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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	Mohammad Rouhani; Angel Sappa
	Title	Non-Rigid Shape Registration: A Single Linear Least Squares Framework			Type	Conference Article
	Year	2012	Publication	12th European Conference on Computer Vision	Abbreviated Journal
	Volume	7578	Issue		Pages	264-277
	Keywords
	Abstract	This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided.
	Address	Florencia
	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			Approved	no
	Call Number	Admin @ si @ RoS2012a			Serial	2158
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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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