A fast approach for perceptually-based fitting strokes into elliptical arcs

Fitting elliptical arcs to strokes of an input sketch is discussed. We describe an approach which automatically combines existing algorithms to get a balance of speed and precision. For measuring precision, we introduce fast metrics which are based on perceptual criteria and are tolerant of sketching imperfections. We return a likelihood estimate based on these metrics rather than deterministic yes/no result, in order that the approach can be used in higher-level collaborative-decision recognition flows.1) Ramon y Cajal Scholarship Programme 2) "Pla de Promoció de la Investigació de la Universitat Jaume I", project P1 1B2010-0

On the Geometries of Conic Section Representation of Noisy Object Boundaries

This paper studies some geometrical properties of conic sections and the utilization of these properties for the generation of conic section representations of object boundaries in digital images. Several geometrical features of the conic sections, such as the chord, the characteristic point, the guiding triangles, and their appearances under the tessellation and noise corruption of the digital images are discussed. The study leads to a noniterative algorithm that takes advantage of these features in the process of formulating the conic section parameters and generating the approximations of object boundaries from the given sequences of edge pixels in the images. The results can be optimized with respect to certain different criteria of the fittings

Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel Density

Diabetic retinopathy is the leading cause of blindness in the Western world. The World Health Organisation estimates that 135 million people have diabetes mellitus worldwide and that the number of people with diabetes will increase to 300 million by the year 2025 (Amos et al., 1997). Timely detection and treatment for DR prevents severe visual loss in more than 50% of the patients (ETDRS, 1991). Through computer simulations is possible to demonstrate that prevention and treatment are relatively inexpensive if compared to the health care and rehabilitation costs incurred by visual loss or blindness (Javitt et al., 1994). The shortage of ophthalmologists and the continuous increase of the diabetic population limits the screening capability for effective timing of sight-saving treatment of typical manual methods. Therefore, an automatic or semi-automatic system able to detect various type of retinopathy is a vital necessity to save many sight-years in the population. According to Luzio et al. (2004) the preferred way to detect diseases such as diabetic retinopathy is digital fundus camera imaging. This allows the image to be enhanced, stored and retrieved more easily than film. In addition, images may be transferred electronically to other sites where a retinal specialist or an automated system can detect or diagnose disease while the patient remains at a remote location. Various systems for automatic or semi-automatic detection of retinopathy with fundus images have been developed. The results obtained are promising but the initial image quality is a limiting factor (Patton et al., 2006); this is especially true if the machine operator is not a trained photographer. Algorithms to correct the illumination or increase the vessel contrast exist (Chen & Tian, 2008; Foracchia et al., 2005; Grisan et al., 2006;Wang et al., 2001), however they cannot restore an image beyond a certain level of quality degradation. On the other hand, an accurate quality assessment algorithm can allow operators to avoid poor images by simply re-taking the fundus image, eliminating the need for correction algorithms. In addition, a quality metric would permit the automatic submission of only the best images if many are available. The measurement of a precise image quality index is not a straightforward task, mainly because quality is a subjective concept which varies even between experts, especially for images that are in the middle of the quality scale. In addition, image quality is dependent upon the type of diagnosis being made. For example, an image with dark regions might be considered of good quality for detecting glaucoma but of bad quality for detecting diabetic retinopathy. For this reason, we decided to define quality as the 'characteristics of an image that allow the retinopathy diagnosis by a human or software expert'. Fig. 1 shows some examples of macula centred fundus images whose quality is very likely to be judged as poor by many ophthalmologists. The reasons for this vary. They can be related to the camera settings like exposure or focal plane error (Fig. 1.(a,e,f)), the camera condition like a dirty or shuttered lens (Fig. 1.(d,h)), the movements of the patient which might blur the image (Fig. 1.(c)) or if the patient is not in the field of view of the camera (Fig. 1.(g)). We define an outlier as any image that is not a retina image which could be submitted to the screening system by mistake. Existing algorithms to estimate the image quality are based on the length of visible vessels in the macula region (Fleming et al., 2006), or edges and luminosity with respect to a reference image (Lalonde et al., 2001; Lee & Wang, 1999). Another method uses an unsupervised classifier that employs multi-scale filterbanks responses (Niemeijer et al., 2006). The shortcomings of these methods are either the fact that they do not take into account the natural variance encountered in retinal images or that they require a considerable time to produce a result. Additionally, none of the algorithms in the literature that we surveyed generate a 'quality measure'. Authors tend to split the quality levels into distinct classes and to classify images in particular ones. This approach is not really flexible and is error prone. In fact human experts are likely to disagree if many categories of image quality are used. Therefore, we think that a normalized 'quality measure' from 0 to 1 is the ideal way to approach the classification problem. Processing speed is another aspect to be taken into consideration. While algorithms to assess the disease state of the retina do not need to be particularly fast (within reason), the time response of the quality evaluation method is key towards the development of an automatic retinopathy screening system

Ellipse fitting by spatial averaging of random ensembles

Earlier ellipse fitting methods often consider the algebraic and geometric forms of the ellipse. The work presented here makes use of an ensemble to provide better results. The method proposes a new ellipse parametrization based on the coordinates of both foci, and the distance between them and each point of the ellipse where the Euclidean norm is applied. Besides, a certain number of subsets are uniformly drawn without replacement from the overall training set which allows estimating the center of the distribution robustly by employing the L1 median of each estimated focus. An additional postprocessing stage is proposed to filter out the effect of bad fits. In order to evaluate the performance of this method, four different error measures were considered. Results show that our proposal outperforms all its competitors, especially when higher levels of outliers are presented. Several synthetic and real data tests were developed and confirmed such finding.This work is partially supported by the Ministry of Economy and Compet- itiveness of Spain [grant numbers TIN2016-75097-P and PPIT.UMA.B1.2017]. It is also partially supported by the Ministry of Science, Innovation and Univer- sities of Spain [grant number RTI2018-094645-B-I00], project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent sys- tems. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinfor- matics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs used for this research. The authors acknowledge the funding from the Universi- dad de Málaga. Karl Thurnhofer-Hemsi is funded by a Ph.D. scholarship from the Spanish Ministry of Education, Culture and Sport under the FPU program [grant number FPU15/06512

O desenvolvimento de competências enquanto determinante negativo do entrincheiramento na carreira:o efeito mediador da autonomia e da insegurança

Dissertação de Mestrado em Politicas de Desenvolvimento dos Recursos HumanosO Entrincheiramento na Carreira é um processo de estagnação da vida profissional, assente em três fatores que impedem a mudança: Investimentos na Carreira, Custos Emocionais e Falta de Alternativas. Permanecer insatisfeito na mesma profissão/carreira acarreta custos para as pessoas, organizações e países, traduzindo-se em fraca motivação e baixa produtividade. O objetivo proposto foi avaliar se as Competências de Carreira teriam um efeito preditor na saída do Entrincheiramento, considerando os efeitos de mediação da Autonomia e da Insegurança. A investigação é quantitativa e foi utilizada uma amostra de conveniência constituída por 265 sujeitos. Os resultados sugeriram, que o Entrincheiramento na Carreira se deva essencialmente à perceção de Falta de Alternativas, as Competências de Carreira mais importantes para lidar com a mudança, são a Saber-porquê as questões de identidade pessoal e os motivos subjacentes que fundamentam as decisões relacionadas com a carreira e a competência Saber-com-quem as capacidades relacionais e a rede de contactos. O modelo final propôs a mediação da Autonomia na relação de influência entre a competência Saber-com-quem e a Falta de Alternativas, a mediação da Insegurança não foi proposta. No final, foram discutidos os resultados e as implicações para a definição de políticas de Gestão de Recursos HumanosCareer Entrenchment is a process of stagnation in professional life, based on three factors hindering the change: Career Investments, Emotional Costs and Limitedness of Career Alternatives. Staying unsatisfied in the some career reveals costs for people, organizations and countries, resulting in poor motivation and low productivity. The aim of the study was to evaluate if Career Competencies have a predictive effect on the Entrenchment Career considering the mediating effects of Autonomy and Insecurity. Research is quantitative and a convenience sample of 265 participants was used. The results suggested that the Career Entrenchment is given essentially by the perception of Limitedness of Career Alternatives and the most important Career Competencies to deal with change, are Knowing-why personal identity issues and the underlying grounds for the decisions related to career and the competence Knowing-whom, relational skills and networks of contacts. The final model, proposed the mediation of Autonomy in the relation of influence between variables Knowing-whom and Limitedness of Career Alternatives, the mediation of Insecurity was not proposed in the model. In the end, the results and implications were discussed for the design of Human Resource Management policies