Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Motion cue analysis for parkinsonian gait recognition

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

March 4th, 1999 : the recognition-primed decision hypothesis

On March 4, 1999, the newly appointed President of the Brazilian Central Bank, Mr Armínio Fraga, raised interest rates to a staggering 45% per annum. The objective of that decision was to keep foreign investors assets in Brazil, and prevent the country from default. At the time, Brazil suffered frem an enormously intense crisis of confidence, and fears of such default were widespread. Mr Fraga was walking a very fine line when making that decision, for it could bring forth unintended effects: the market, already concerned about Brazil's sustainability, could perceive the increased rate as an irreversible step towards the abyss inevitable default. Economic theory postulates the rational actor model as the driving force behind economic decision-making. The objective of this thesis is to present and discuss the hypothesis that this particular decision, and by extension many others, are better explained threugh the recognition-primed decision mode!.

Opening ILT blackbox: Exploring recognition-based leadership perceptions with conjoint analysis

Although research on Implicit Leadership Theories (ILT) has put great effort on determining what attributes define a leader prototype, little attention has been given to understanding the relative importance of each of these attributes in the categorization process by followers. Knowing that recognition-based leadership perceptions are the result of the match between followers’ ILTs and the perceived attributes in their actual leaders, understanding how specific prototypical leader attributes impact this impression formation process is particularly relevant. In this study, we draw upon socio-cognitive theories to explore how followers cognitively process the information about a leader’s attributes. By using Conjoint Analysis (CA), a technique that allows us to measure an individual’s trade-offs when making choices about multi-attributed options, we conducted a series of 4 studies with a total of 879 participants. Our results demonstrate that attributes’ importance for individuals’ leadership perceptions formation is rather heterogeneous, and that some attributes can enhance or spoil the importance of other prototypical attributes. Finally, by manipulating the leadership domain, we show that the weighting pattern of attributes is context dependent, as suggested by the connectionist approach to leadership categorization. Our findings also demonstrate that Conjoint Analysis can be a valuable tool for ILT research.

Antibodies to yeast Sm motif 1 cross-react with human Sm core polypeptides

Two regions common to all UsnRNP core polypeptides have been described: Sm motif 1 and Sm motif 2. Rabbits were immunized with a 22 amino-acid peptide containing one segment of Sm motif 1 (YRGTLVSTDNYFNLQL-NEAEEF, corresponding to residues 11-32) from yeast F protein. After immunization, the rabbit sera contained antibodies that not only reacted specifically with the peptide from yeast F protein but also cross-reacted with Sm polypeptides from mammals; that is, with purified human U1snRNPs. The results suggest that the peptide used and human Sm polypeptides contain a common feature recognized by the polyclonal antibodies. A large collection of human systemic lupus erythematosus sera was assayed using the yeast peptide as an antigen source. Seventy per cent of systemic lupus erythematosus sera contain an antibody specificity that cross-reacts with the yeast peptide.

CENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION and MATCHING APPROACH FOR FACE RECOGNITION

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.

Factors affecting nucleolytic efficiency of some ternary metal complexes with DNA binding and recognition domains. Crystal and molecular structure of Zn(phen)(edda)

The binding selectivity of the M(phen)(edda) (M = Cu, Co, Ni, Zn; phen = 1,10-phenanthroline, edda = ethylenediaminediacetic acid) complexes towards ds(CG)(6), ds(AT)(6) and ds(CGCGAATTCGCG) B-form oligonucleotide duplexes were studied by CD spectroscopy and molecular modeling. The binding mode is intercalation and there is selectivity towards AT-sequence and stacking preference for A/A parallel or diagonal adjacent base steps in their intercalation. The nucleolytic properties of these complexes were investigated and the factors affecting the extent of cleavage were determined to be: concentration of complex, the nature of metal(11) ion, type of buffer, pH of buffer, incubation time, incubation temperature, and the presence of hydrogen peroxide or ascorbic acid as exogenous reagents. The fluorescence property of these complexes and its origin were also investigated. The crystal structure of the Zn(phen)(edda) complex is reported in which the zinc atom displays a distorted trans-N4O2 octahedral geometry; the crystal packing features double layers of complex molecules held together by extensive hydrogen bonding that inter-digitate with adjacent double layers via pi...pi interactions between 1,10-phenanthroline residues. The structure is compared with that of the recently described copper(II) analogue and, with the latter, included in molecular modeling. (C) 2008 Elsevier B.V. All rights reserved.