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Ignasi Rius. (2010). Motion Priors for Efficient Bayesian Tracking in Human Sequence Evaluation (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Recovering human motion by visual analysis is a challenging computer vision research
area with a lot of potential applications. Model-based tracking approaches, and in
particular particle lters, formulate the problem as a Bayesian inference task whose
aim is to sequentially estimate the distribution of the parameters of a human body
model over time. These approaches strongly rely on good dynamical and observation
models to predict and update con gurations of the human body according to measurements from the image data. However, it is very di cult to design observation
models which extract useful and reliable information from image sequences robustly.
This results specially challenging in monocular tracking given that only one viewpoint
from the scene is available. Therefore, to overcome these limitations strong motion
priors are needed to guide the exploration of the state space.
The work presented in this Thesis is aimed to retrieve the 3D motion parameters
of a human body model from incomplete and noisy measurements of a monocular
image sequence. These measurements consist of the 2D positions of a reduced set of
joints in the image plane. Towards this end, we present a novel action-speci c model
of human motion which is trained from several databases of real motion-captured
performances of an action, and is used as a priori knowledge within a particle ltering
scheme.
Body postures are represented by means of a simple and compact stick gure
model which uses direction cosines to represent the direction of body limbs in the 3D
Cartesian space. Then, for a given action, Principal Component Analysis is applied to
the training data to perform dimensionality reduction over the highly correlated input
data. Before the learning stage of the action model, the input motion performances
are synchronized by means of a novel dense matching algorithm based on Dynamic
Programming. The algorithm synchronizes all the motion sequences of the same
action class, nding an optimal solution in real-time.
Then, a probabilistic action model is learnt, based on the synchronized motion
examples, which captures the variability and temporal evolution of full-body motion
within a speci c action. In particular, for each action, the parameters learnt are: a
representative manifold for the action consisting of its mean performance, the standard deviation from the mean performance, the mean observed direction vectors from
each motion subsequence of a given length and the expected error at a given time
instant.
Subsequently, the action-speci c model is used as a priori knowledge on human
motion which improves the e ciency and robustness of the overall particle filtering tracking framework. First, the dynamic model guides the particles according to similar
situations previously learnt. Then, the state space is constrained so only feasible
human postures are accepted as valid solutions at each time step. As a result, the
state space is explored more e ciently as the particle set covers the most probable
body postures.
Finally, experiments are carried out using test sequences from several motion
databases. Results point out that our tracker scheme is able to estimate the rough
3D con guration of a full-body model providing only the 2D positions of a reduced
set of joints. Separate tests on the sequence synchronization method and the subsequence probabilistic matching technique are also provided.
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Ivan Huerta. (2010). Foreground Object Segmentation and Shadow Detection for Video Sequences in Uncontrolled Environments (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
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.
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Carles Fernandez. (2010). Understanding Image Sequences: the Role of Ontologies in Cognitive Vision (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: The increasing ubiquitousness of digital information in our daily lives has positioned
video as a favored information vehicle, and given rise to an astonishing generation of
social media and surveillance footage. This raises a series of technological demands
for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research
community to guide its steps towards a better attainment of such capabilities. As
a result, current trends on cognitive vision promise to recognize complex events and
self-adapt to different environments, while managing and integrating several types of
knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users.
In this thesis we tackle the problem of recognizing and describing meaningful
events in video sequences from different domains, and communicating the resulting
knowledge to end-users by means of advanced interfaces for human–computer interaction. This problem is addressed by designing the high-level modules of a cognitive
vision framework exploiting ontological knowledge. Ontologies allow us to define the
relevant concepts in a domain and the relationships among them; we prove that the
use of ontologies to organize, centralize, link, and reuse different types of knowledge
is a key factor in the materialization of our objectives.
The proposed framework contributes to: (i) automatically learn the characteristics
of different scenarios in a domain; (ii) reason about uncertain, incomplete, or vague
information from visual –camera’s– or linguistic –end-user’s– inputs; (iii) derive plausible interpretations of complex events from basic spatiotemporal developments; (iv)
facilitate natural interfaces that adapt to the needs of end-users, and allow them to
communicate efficiently with the system at different levels of interaction; and finally,
(v) find mechanisms to guide modeling processes, maintain and extend the resulting
models, and to exploit multimodal resources synergically to enhance the former tasks.
We describe a holistic methodology to achieve these goals. First, the use of prior
taxonomical knowledge is proved useful to guide MAP-MRF inference processes in
the automatic identification of semantic regions, with independence of a particular scenario. Towards the recognition of complex video events, we combine fuzzy
metric-temporal reasoning with SGTs, thus assessing high-level interpretations from
spatiotemporal data. Here, ontological resources like T–Boxes, onomasticons, or factual databases become useful to derive video indexing and retrieval capabilities, and
also to forward highlighted content to smart user interfaces. There, we explore the
application of ontologies to discourse analysis and cognitive linguistic principles, or scene augmentation techniques towards advanced communication by means of natural language dialogs and synthetic visualizations. Ontologies become fundamental to
coordinate, adapt, and reuse the different modules in the system.
The suitability of our ontological framework is demonstrated by a series of applications that especially benefit the field of smart video surveillance, viz. automatic generation of linguistic reports about the content of video sequences in multiple natural
languages; content-based filtering and summarization of these reports; dialogue-based
interfaces to query and browse video contents; automatic learning of semantic regions
in a scenario; and tools to evaluate the performance of components and models in the
system, via simulation and augmented reality.
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Joan Mas. (2010). A Syntactic Pattern Recognition Approach based on a Distribution Tolerant Adjacency Grammar and a Spatial Indexed Parser. Application to Sketched Document Recognition (Gemma Sanchez, & Josep Llados, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
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.
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Francisco Javier Orozco. (2010). Human Emotion Evaluation on Facial Image Sequences (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Psychological evidence has emphasized the importance of affective behaviour understanding due to its high impact in nowadays interaction humans and computers. All
type of affective and behavioural patterns such as gestures, emotions and mental
states are highly displayed through the face, head and body. Therefore, this thesis is
focused to analyse affective behaviours on head and face. To this end, head and facial
movements are encoded by using appearance based tracking methods. Specifically,
a wise combination of deformable models captures rigid and non-rigid movements of
different kinematics; 3D head pose, eyebrows, mouth, eyelids and irises are taken into
account as basis for extracting features from databases of video sequences. This approach combines the strengths of adaptive appearance models, optimization methods
and backtracking techniques.
For about thirty years, computer sciences have addressed the investigation on
human emotions to the automatic recognition of six prototypic emotions suggested
by Darwin and systematized by Paul Ekman in the seventies. The Facial Action
Coding System (FACS) which uses discrete movements of the face (called Action
units or AUs) to code the six facial emotions named anger, disgust, fear, happy-Joy,
sadness and surprise. However, human emotions are much complex patterns that
have not received the same attention from computer scientists.
Simon Baron-Cohen proposed a new taxonomy of emotions and mental states
without a system coding of the facial actions. These 426 affective behaviours are
more challenging for the understanding of human emotions. Beyond of classically
classifying the six basic facial expressions, more subtle gestures, facial actions and
spontaneous emotions are considered here. By assessing confidence on the recognition
results, exploring spatial and temporal relationships of the features, some methods are
combined and enhanced for developing new taxonomy of expressions and emotions.
The objective of this dissertation is to develop a computer vision system, including both facial feature extraction, expression recognition and emotion understanding
by building a bottom-up reasoning process. Building a detailed taxonomy of human
affective behaviours is an interesting challenge for head-face-based image analysis
methods. In this paper, we exploit the strengths of Canonical Correlation Analysis
(CCA) to enhance an on-line head-face tracker. A relationship between head pose and
local facial movements is studied according to their cognitive interpretation on affective expressions and emotions. Active Shape Models are synthesized for AAMs based
on CCA-regression. Head pose and facial actions are fused into a maximally correlated space in order to assess expressiveness, confidence and classification in a CBR system. The CBR solutions are also correlated to the cognitive features, which allow
avoiding exhaustive search when recognizing new head-face features. Subsequently,
Support Vector Machines (SVMs) and Bayesian Networks are applied for learning the
spatial relationships of facial expressions. Similarly, the temporal evolution of facial
expressions, emotion and mental states are analysed based on Factorized Dynamic
Bayesian Networks (FaDBN).
As results, the bottom-up system recognizes six facial expressions, six basic emotions and six mental states, plus enhancing this categorization with confidence assessment at each level, intensity of expressions and a complete taxonomy
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Marc Serra. (2010). Estimating Intrinsic Images from Physical and Categorical Color Cues (Vol. 151). Master's thesis, , .
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Ahmed Mounir Gad. (2010). Object Localization Enhancement by Multiple Segmentation Fusion (Vol. 152). Master's thesis, , .
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Antonio Hernandez. (2010). Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation (Vol. 153). Master's thesis, , .
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Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2010). Feature Detectors and Feature Descriptors: Where We Are Now (Vol. 154).
Abstract: Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.
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Lluis Pere de las Heras. (2010). Syntactic Model for Semantic Document Analysis (Vol. 158).
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Anjan Dutta. (2010). Symbol Spotting in Graphical Documents by Serialized Subgraph Matching (Vol. 159). Master's thesis, , .
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Ekain Artola. (2010). Human Attention Map Prediction Combining Visual Features (Vol. 160). Bachelor's thesis, , .
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David Fernandez. (2010). Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors (Vol. 161). Master's thesis, , .
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Jon Almazan. (2010). Deforming the Blurred Shape Model for Shape Description and Recognition (Vol. 163). Master's thesis, , .
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Nataliya Shapovalova. (2010). On Importance of Interaction and Context (Vol. 155). Master's thesis, , .
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