Abstract: The specific problem of symbol recognition in graphical documents requires additional techniques to those developed for character recognition. The most well-known obstacle is the so-called Sayre paradox: Correct recognition requires good segmentation, yet improvement in segmentation is achieved using information provided by the recognition process. This dilemma can be avoided by techniques that identify sets of regions containing useful information. Such symbol-spotting methods allow the detection of symbols in maps or technical drawings without having to fully segment or fully recognize the entire content.
This unique text/reference provides a complete, integrated and large-scale solution to the challenge of designing a robust symbol-spotting method for collections of graphic-rich documents. The book examines a number of features and descriptors, from basic photometric descriptors commonly used in computer vision techniques to those specific to graphical shapes, presenting a methodology which can be used in a wide variety of applications. Additionally, readers are supplied with an insight into the problem of performance evaluation of spotting methods. Some very basic knowledge of pattern recognition, document image analysis and graphics recognition is assumed.

Abstract: Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known.

Abstract: Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect.

Abstract: PURPOSE:
In this article, the authors studied the feasibility of estimating regional mechanical properties in cerebral aneurysms, integrating information extracted from imaging and physiological data with generic computational models of the arterial wall behavior.
METHODS:
A data assimilation framework was developed to incorporate patient-specific geometries into a given biomechanical model, whereas wall motion estimates were obtained from applying registration techniques to a pair of simulated MR images and guided the mechanical parameter estimation. A simple incompressible linear and isotropic Hookean model coupled with computational fluid-dynamics was employed as a first approximation for computational purposes. Additionally, an automatic clustering technique was developed to reduce the number of parameters to assimilate at the optimization stage and it considerably accelerated the convergence of the simulations. Several in silico experiments were designed to assess the influence of aneurysm geometrical characteristics and the accuracy of wall motion estimates on the mechanical property estimates. Hence, the proposed methodology was applied to six real cerebral aneurysms and tested against a varying number of regions with different elasticity, different mesh discretization, imaging resolution, and registration configurations.
RESULTS:
Several in silico experiments were conducted to investigate the feasibility of the proposed workflow, results found suggesting that the estimation of the mechanical properties was mainly influenced by the image spatial resolution and the chosen registration configuration. According to the in silico experiments, the minimal spatial resolution needed to extract wall pulsation measurements with enough accuracy to guide the proposed data assimilation framework was of 0.1 mm.
CONCLUSIONS:
Current routine imaging modalities do not have such a high spatial resolution and therefore the proposed data assimilation framework cannot currently be used on in vivo data to reliably estimate regional properties in cerebral aneurysms. Besides, it was observed that the incorporation of fluid-structure interaction in a biomechanical model with linear and isotropic material properties did not have a substantial influence in the final results.

Abstract: Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers' opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.

Abstract: In two previous experiments [Parraga et al, 2009 J. of Im. Sci. and Tech 53(3) 031106; Benavente et al,2009 Perception 38 ECVP Supplement, 36] the boundaries of basic colour categories were measured.
In the first experiment, samples were presented in isolation (ie on a dark background) and boundaries were measured using a yes/no paradigm. In the second, subjects adjusted the chromaticity of a sample presented on a random Mondrian background to find the boundary between pairs of adjacent colours.
Results from these experiments showed significant dierences but it was not possible to conclude whether this discrepancy was due to the absence/presence of a colourful background or to the dierences in the paradigms used. In this work, we settle this question by repeating the first experiment (ie samples presented on a dark background) using the second paradigm. A comparison of results shows that
although boundary locations are very similar, boundaries measured in context are significantly dierent(more diuse) than those measured in isolation (confirmed by a Student’s t-test analysis on the subject’s answers statistical distributions). In addition, we completed the mapping of colour name space by measuring the boundaries between chromatic colours and the achromatic centre. With these results we
completed our parametric fuzzy-sets model of colour naming space.

Abstract: This Thesis is mainly divided in two parts. The first one presents a study of motion
segmentation problems. Based on this study, a novel algorithm for mobile-object
segmentation from a static background scene is also presented. This approach is
demonstrated robust and accurate under most of the common problems in motion
segmentation. The second one tackles the problem of shadows in depth. Firstly, a
bottom-up approach based on a chromatic shadow detector is presented to deal with
umbra shadows. Secondly, a top-down approach based on a tracking system has been
developed in order to enhance the chromatic shadow detection.
In our first contribution, a case analysis of motion segmentation problems is presented by taking into account the problems associated with different cues, namely
colour, edge and intensity. Our second contribution is a hybrid architecture which
handles the main problems observed in such a case analysis, by fusing (i) the knowledge from these three cues and (ii) a temporal difference algorithm. On the one hand,
we enhance the colour and edge models to solve both global/local illumination changes
(shadows and highlights) and camouflage in intensity. In addition, local information is
exploited to cope with a very challenging problem such as the camouflage in chroma.
On the other hand, the intensity cue is also applied when colour and edge cues are not
available, such as when beyond the dynamic range. Additionally, temporal difference
is included to segment motion when these three cues are not available, such as that
background not visible during the training period. Lastly, the approach is enhanced
for allowing ghost detection. As a result, our approach obtains very accurate and robust motion segmentation in both indoor and outdoor scenarios, as quantitatively and
qualitatively demonstrated in the experimental results, by comparing our approach
with most best-known state-of-the-art approaches.
Motion Segmentation has to deal with shadows to avoid distortions when detecting
moving objects. Most segmentation approaches dealing with shadow detection are
typically restricted to penumbra shadows. Therefore, such techniques cannot cope
well with umbra shadows. Consequently, umbra shadows are usually detected as part
of moving objects.
Firstly, a bottom-up approach for detection and removal of chromatic moving
shadows in surveillance scenarios is proposed. Secondly, a top-down approach based
on kalman filters to detect and track shadows has been developed in order to enhance
the chromatic shadow detection. In the Bottom-up part, the shadow detection approach applies a novel technique based on gradient and colour models for separating
chromatic moving shadows from moving objects.
Well-known colour and gradient models are extended and improved into an invariant colour cone model and an invariant gradient model, respectively, to perform
automatic segmentation while detecting potential shadows. Hereafter, the regions corresponding to potential shadows are grouped by considering ”a bluish effect” and an
edge partitioning. Lastly, (i) temporal similarities between local gradient structures
and (ii) spatial similarities between chrominance angle and brightness distortions are
analysed for all potential shadow regions in order to finally identify umbra shadows.
In the top-down process, after detection of objects and shadows both are tracked
using Kalman filters, in order to enhance the chromatic shadow detection, when it
fails to detect a shadow. Firstly, this implies a data association between the blobs
(foreground and shadow) and Kalman filters. Secondly, an event analysis of the different data association cases is performed, and occlusion handling is managed by a
Probabilistic Appearance Model (PAM). Based on this association, temporal consistency is looked for the association between foregrounds and shadows and their
respective Kalman Filters. From this association several cases are studied, as a result
lost chromatic shadows are correctly detected. Finally, the tracking results are used
as feedback to improve the shadow and object detection.
Unlike other approaches, our method does not make any a-priori assumptions
about camera location, surface geometries, surface textures, shapes and types of
shadows, objects, and background. Experimental results show the performance and
accuracy of our approach in different shadowed materials and illumination conditions.

Abstract: Sketch recognition is a discipline which has gained an increasing interest in the last
20 years. This is due to the appearance of new devices such as PDA, Tablet PC’s
or digital pen & paper protocols. From the wide range of sketched documents we
focus on those that represent structured documents such as: architectural floor-plans,
engineering drawing, UML diagrams, etc. To recognize and understand these kinds
of documents, first we have to recognize the different compounding symbols and then
we have to identify the relations between these elements. From the way that a sketch
is captured, there are two categories: on-line and off-line. On-line input modes refer
to draw directly on a PDA or a Tablet PC’s while off-line input modes refer to scan
a previously drawn sketch.
This thesis is an overlapping of three different areas on Computer Science: Pattern
Recognition, Document Analysis and Human-Computer Interaction. The aim of this
thesis is to interpret sketched documents independently on whether they are captured
on-line or off-line. For this reason, the proposed approach should contain the following
features. First, as we are working with sketches the elements present in our input
contain distortions. Second, as we would work in on-line or off-line input modes, the
order in the input of the primitives is indifferent. Finally, the proposed method should
be applied in real scenarios, its response time must be slow.
To interpret a sketched document we propose a syntactic approach. A syntactic
approach is composed of two correlated components: a grammar and a parser. The
grammar allows describing the different elements on the document as well as their
relations. The parser, given a document checks whether it belongs to the language
generated by the grammar or not. Thus, the grammar should be able to cope with
the distortions appearing on the instances of the elements. Moreover, it would be
necessary to define a symbol independently of the order of their primitives. Concerning to the parser when analyzing 2D sentences, it does not assume an order in the
primitives. Then, at each new primitive in the input, the parser searches among the
previous analyzed symbols candidates to produce a valid reduction.
Taking into account these features, we have proposed a grammar based on Adjacency Grammars. This kind of grammars defines their productions as a multiset
of symbols rather than a list. This allows describing a symbol without an order in
their components. To cope with distortion we have proposed a distortion model.
This distortion model is an attributed estimated over the constraints of the grammar and passed through the productions. This measure gives an idea on how far is the
symbol from its ideal model. In addition to the distortion on the constraints other
distortions appear when working with sketches. These distortions are: overtracing,
overlapping, gaps or spurious strokes. Some grammatical productions have been defined to cope with these errors. Concerning the recognition, we have proposed an
incremental parser with an indexation mechanism. Incremental parsers analyze the
input symbol by symbol given a response to the user when a primitive is analyzed.
This makes incremental parser suitable to work in on-line as well as off-line input
modes. The parser has been adapted with an indexation mechanism based on a spatial division. This indexation mechanism allows setting the primitives in the space
and reducing the search to a neighbourhood.
A third contribution is a grammatical inference algorithm. This method given a
set of symbols captures the production describing it. In the field of formal languages,
different approaches has been proposed but in the graphical domain not so much work
is done in this field. The proposed method is able to capture the production from
a set of symbol although they are drawn in different order. A matching step based
on the Haussdorff distance and the Hungarian method has been proposed to match
the primitives of the different symbols. In addition the proposed approach is able to
capture the variability in the parameters of the constraints.
From the experimental results, we may conclude that we have proposed a robust
approach to describe and recognize sketches. Moreover, the addition of new symbols
to the alphabet is not restricted to an expert. Finally, the proposed approach has
been used in two real scenarios obtaining a good performance.