371 research outputs found
ARG based on arcs and segments to improve the symbol recognition by genetic algorithm
URL : http://www.buyans.com/pol/UploadedFile/147_4410.pdfInternational audienceIn this paper a genetic matching scheme is extended to take into account primitive arcs and complex description in the pattern recognition process. Classical ways only focus on segments and are sensitive to over segmentation effect. Our approach allows to improve the recognition by handling more accurate description and also to decrease processing time by limiting the number of vertices to match. Experimental studies using real data attest the robustness of our approach
Structural matching by discrete relaxation
This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we locus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations ai the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter
An Approach to Pattern Recognition by Evolutionary Computation
Evolutionary Computation has been inspired by the natural phenomena of evolution. It provides a quite general heuristic, exploiting few basic concepts: reproduction of individuals, variation phenomena that affect the likelihood of survival of individuals, inheritance of parents features by offspring. EC has been widely used in the last years to effectively solve hard, non linear and very complex problems.
Among the others, EC–based algorithms have also been used to tackle
classification problems. Classification is a process according to which an object is attributed to one of a finite set of classes or, in other words, it is recognized as belonging to a set of equal or similar entities, identified by a label. Most likely, the main aspect of classification concerns the generation of prototypes to be used to recognize unknown patterns. The role of prototypes is that of representing patterns belonging to the different classes defined within a given problem. For most of the problems of practical interest, the generation of such prototypes is a very hard problem, since a prototype must be able to represent patterns belonging to the same class, which may be significantly dissimilar each other. They must also be able to discriminate patterns belonging to classes different from the one that they represent. Moreover, a prototype should contain the minimum amount of information required to satisfy the requirements just mentioned. The research presented in this thesis, has led to the definition of an EC–based framework to be used for prototype generation. The defined framework does not provide for the use of any particular kind of prototypes. In fact, it can generate any kind of prototype once an encoding scheme for the used prototypes has been defined. The generality of the framework can be exploited to develop many applications. The framework has been employed to implement two specific applications for prototype generation.
The developed applications have been tested on several data sets and the results compared with those obtained by other approaches previously presented in the literature
Spatial features of reverberant speech: estimation and application to recognition and diarization
Distant talking scenarios, such as hands-free calling or teleconference meetings, are essential for natural and comfortable human-machine interaction and they are being increasingly used in multiple contexts. The acquired speech signal in such scenarios is reverberant and affected by additive noise. This signal distortion degrades the performance of speech recognition and diarization systems creating troublesome human-machine interactions.This thesis proposes a method to non-intrusively estimate room acoustic parameters, paying special attention to a room acoustic parameter highly correlated with speech recognition degradation: clarity index. In addition, a method to provide information regarding the estimation accuracy is proposed. An analysis of the phoneme recognition performance for multiple reverberant environments is presented, from which a confusability metric for each phoneme is derived. This confusability metric is then employed to improve reverberant speech recognition performance. Additionally, room acoustic parameters can as well be used in speech recognition to provide robustness against reverberation. A method to exploit clarity index estimates in order to perform reverberant speech recognition is introduced.
Finally, room acoustic parameters can also be used to diarize reverberant speech. A room acoustic parameter is proposed to be used as an additional source of information for single-channel diarization purposes in reverberant environments. In multi-channel environments, the time delay of arrival is a feature commonly used to diarize the input speech, however the computation of this feature is affected by reverberation. A method is presented to model the time delay of arrival in a robust manner so that speaker diarization is more accurately performed.Open Acces
New methods, techniques and applications for sketch recognition
2012-2013The use of diagrams is common in various disciplines. Typical examples
include maps, line graphs, bar charts, engineering blueprints, architects’
sketches, hand drawn schematics, etc.. In general, diagrams can be created
either by using pen and paper, or by using specific computer programs. These
programs provide functions to facilitate the creation of the diagram, such as
copy-and-paste, but the classic WIMP interfaces they use are unnatural when
compared to pen and paper. Indeed, it is not rare that a designer prefers
to use pen and paper at the beginning of the design, and then transfer the
diagram to the computer later.
To avoid this double step, a solution is to allow users to sketch directly on
the computer. This requires both specific hardware and sketch recognition
based software. As regards hardware, many pen/touch based devices such as
tablets, smartphones, interactive boards and tables, etc. are available today,
also at reasonable costs. Sketch recognition is needed when the sketch must
be processed and not considered as a simple image and it is crucial to the
success of this new modality of interaction. It is a difficult problem due to the
inherent imprecision and ambiguity of a freehand drawing and to the many
domains of applications. The aim of this thesis is to propose new methods
and applications regarding the sketch recognition. The presentation of the
results is divided into several contributions, facing problems such as corner
detection, sketched symbol recognition and autocompletion, graphical context
detection, sketched Euler diagram interpretation.
The first contribution regards the problem of detecting the corners present
in a stroke. Corner detection is often performed during preprocessing to
segment a stroke in single simple geometric primitives such as lines or curves.
The corner recognizer proposed in this thesis, RankFrag, is inspired by the
method proposed by Ouyang and Davis in 2011 and improves the accuracy
percentages compared to other methods recently proposed in the literature.
The second contribution is a new method to recognize multi-stroke hand
drawn symbols, which is invariant with respect to scaling and supports symbol
recognition independently from the number and order of strokes. The method
is an adaptation of the algorithm proposed by Belongie et al. in 2002 to the
case of sketched images. This is achieved by using stroke related information.
The method has been evaluated on a set of more than 100 symbols from
the Military Course of Action domain and the results show that the new
recognizer outperforms the original one.
The third contribution is a new method for recognizing multi-stroke partially
hand drawn symbols which is invariant with respect to scale, and
supports symbol recognition independently from the number and order of
strokes. The recognition technique is based on subgraph isomorphism and
exploits a novel spatial descriptor, based on polar histograms, to represent
relations between two stroke primitives. The tests show that the approach
gives a satisfactory recognition rate with partially drawn symbols, also with
a very low level of drawing completion, and outperforms the existing approaches
proposed in the literature. Furthermore, as an application, a system
presenting a user interface to draw symbols and implementing the proposed
autocompletion approach has been developed. Moreover a user study aimed
at evaluating the human performance in hand drawn symbol autocompletion
has been presented. Using the set of symbols from the Military Course of
Action domain, the user study evaluates the conditions under which the
users are willing to exploit the autocompletion functionality and those under
which they can use it efficiently. The results show that the autocompletion
functionality can be used in a profitable way, with a drawing time saving of
about 18%.
The fourth contribution regards the detection of the graphical context of
hand drawn symbols, and in particular, the development of an approach for
identifying attachment areas on sketched symbols. In the field of syntactic
recognition of hand drawn visual languages, the recognition of the relations
among graphical symbols is one of the first important tasks to be accomplished
and is usually reduced to recognize the attachment areas of each symbol and
the relations among them. The approach is independent from the method used
to recognize symbols and assumes that the symbol has already been recognized.
The approach is evaluated through a user study aimed at comparing the
attachment areas detected by the system to those devised by the users. The
results show that the system can identify attachment areas with a reasonable
accuracy.
The last contribution is EulerSketch, an interactive system for the sketching
and interpretation of Euler diagrams (EDs). The interpretation of a hand
drawn ED produces two types of text encodings of the ED topology called
static code and ordered Gauss paragraph (OGP) code, and a further encoding
of its regions. Given the topology of an ED expressed through static or OGP
code, EulerSketch automatically generates a new topologically equivalent ED
in its graphical representation. [edited by author]XII n.s
Hidden Markov Models
Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research
Advances in Character Recognition
This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject
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