97 research outputs found
A Comparative Experiment of Several Shape Methods in Recognizing Plants
Shape is an important aspects in recognizing plants. Several approaches have
been introduced to identify objects, including plants. Combination of geometric
features such as aspect ratio, compactness, and dispersion, or moments such as
moment invariants were usually used toidentify plants. In this research, a
comparative experiment of 4 methods to identify plants using shape features was
accomplished. Two approaches have never been used in plants identification yet,
Zernike moments and Polar Fourier Transform (PFT), were incorporated. The
experimental comparison was done on 52 kinds of plants with various shapes. The
result, PFT gave best performance with 64% in accuracy and outperformed the
other methods.Comment: 8 pages; International Journal of Computer Science & Information
Technology (IJCSIT), Vol 3, No 3, June 201
A survey of face detection, extraction and recognition
The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important
Maximum likelihood Linear Programming Data Fusion for Speaker Recognition
Biometric system performance can be improved by means of data fusion. Several kinds of
information can be fused in order to obtain a more accurate classification (identification or
verification) of an input sample. In this paper we present a method for computing the
weights in a weighted sum fusion for score combinations, by means of a likelihood model.
The maximum likelihood estimation is set as a linear programming problem. The scores are
derived from a GMM classifier working on a different feature extractor. Our experimental
results assesed the robustness of the system in front a changes on time (different sessions)
and robustness in front a change of microphone. The improvements obtained were
significantly better (error bars of two standard deviations) than a uniform weighted sum or a
uniform weighted product or the best single classifier. The proposed method scales
computationaly with the number of scores to be fussioned as the simplex method for linear
programming
Face Detection & Recognition based on Fusion of Omnidirectional & PTZ Vision Sensors and Heteregenous Database
International audienceLarge field of view with high resolution has always been sought-after for Mobile Robotic Authentication. So the Vision System proposed here is composed of a catadioptric sensor for full range monitoring and a Pan Tilt Zoom (PTZ) camera together forming an innovative sensor, able to detect and track any moving objects at a higher zoom level. In our application, the catadioptric sensor is calibrated and used to detect and track Regions Of Iinterest (ROIs) within its 360 degree Field Of View (FOV), especially face regions. Using a joint calibration strategy, the PTZ camera parameters are automatically adjusted by the system in order to detect and track the face ROI within a higher resolution and project the same in faces-pace for recognition via Eigenface algorithm. Face recognition is one important task in Nomad Biometric Authentication (NOBA 1) project. However, as many other face databases, it will easily produce the Small Sample Size (SSS) problem in some applications with NOBA data. Thus this journal uses the compressed sensing (CS) algorithm to solve the SSS problem in NOBA face database. Some experiments can prove the feasibility and validity of this solution. The whole development has been partially validated by application to the Face recognition using our own database NOBA
Biometric Recognition of 3D Faces
Diplomová práce byla vypracována na studijnĂm pobytu na "Gjovik University College" v Norsku, a je zpracována v angliÄŤtinÄ›. Tato práce se zabĂ˝vá rozpoznávánĂm 3D obliÄŤejĹŻ. Je zde popsán obecnĂ˝ biometrickĂ˝ systĂ©m a takĂ© konkrĂ©tnĂ postupy pouĹľĂvanĂ© pĹ™i rozpoznávánĂ 2D i 3D obliÄŤejĹŻ. NáslednÄ› je navĹľena metoda pro rozpoznávánĂ 3D obliÄŤejĹŻ. Algoritmus je vyvĂjen a testován pomocĂ databáze Face Recognition Grand Challenge (FRGC). BÄ›hem pĹ™edzpracovánĂ jsou nalezeny vĂ˝znaÄŤnĂ© body v obliÄŤeji a následnÄ› je trojrozmÄ›rnĂ˝ model zarovnán do referenÄŤnĂ polohy. Dále jsou vstupnĂ data porovnávána s biometrickĂ˝mi šablonami uloĹľenĂ˝mi v databázi, a to je zajištÄ›no vyuĹľitĂm třà základnĂch technik pro rozpoznávánĂ obliÄŤejĹŻ -- metoda eigenface (PCA), rozpoznávánĂ zaloĹľenĂ© na histogramu obliÄŤeje a rozpoznávánĂ zaloĹľenĂ© na anatomickĂ˝ch rysech. Nakonec jsou jednotlivĂ© metody spojeny do jednoho systĂ©mu, jehoĹľ celková vĂ˝sledná vĂ˝konnost pĹ™evyšuje vĂ˝konnost jednotlivĂ˝ch pouĹľitĂ˝ch technik.This Master's Thesis was performed during a study stay at the Gjovik University College, Norway. This Thesis is about biometric 3D face recognition. A general biometric system as well as specific techniques used in 2D and 3D face recognition are described. An automatic modular 3D face recognition method will be proposed. The algorithm is developed, tested and evaluated on the Face Recognition Grand Challenge (FRGC) database. During the preprocessing part, facial landmarks are located on the face surface and the three dimensional model is aligned to a predefined position. In the comparison module, the input probe scan is compared to the gallery template. There are three fundamental face recognition algorithms employed during the recognition pipeline -- the eigenface method (PCA), the recognition using histogram-based features, and the recognition based on the anatomical-Bertillon features of the face. Finally the decision module fuses the scores provided by the utilized recognition techniques. The resulting performance is better than any of utilized recognition algorithms.
On-Line Handwritten Formula Recognition using Hidden Markov Models and Context Dependent Graph Grammars
This paper presents an approach for the recognition of on-line handwritten mathematical expressions. The Hidden Markov Model (HMM) based system makes use of simultaneous segmentation and recognition capabilities, avoiding a crucial segmentation during pre-processing. With the segmentation and recognition results, obtained from the HMMrecognizer, it is possible to analyze and interpret the spatial two-dimensional arrangement of the symbols. We use a graph grammar approach for the structure recognition, also used in off-line recognition process, resulting in a general tree-structure of the underlying input-expression. The resulting constructed tree can be translated to any desired syntax (for example: Lisp, LaTeX, OpenMath . . . )
Computer Vision and Human-Computer Interaction: artificial vision techniques and use cases with creating interfaces and interaction models
Here is described how Computer Vision could give improvements to Human-Computer Interaction. Starting from a brief description of computers and human beings, follows a description of computer interfaces and Computer Vision. Then there's a description of how a human being can be recognized by Computer Vision and follow some cases in which these techniques are applied. The last part is about describing Computer Vision for Human-Computer Interaction models based on the use case
A step towards understanding paper documents
This report focuses on analysis steps necessary for a paper document processing. It is divided in three major parts: a document image preprocessing, a knowledge-based geometric classification of the image, and a expectation-driven text recognition. It first illustrates the several low level image processing procedures providing the physical document structure of a scanned document image. Furthermore, it describes a knowledge-based approach, developed for the identification of logical objects (e.g., sender or the footnote of a letter) in a document image. The logical identifiers provide a context-restricted consideration of the containing text. While using specific logical dictionaries, a expectation-driven text recognition is possible to identify text parts of specific interest. The system has been implemented for the analysis of single-sided business letters in Common Lisp on a SUN 3/60 Workstation. It is running for a large population of different letters. The report also illustrates and discusses examples of typical results obtained by the system
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