Parallelization for image processing algorithms based chain and mid-crack codes

Freeman chain code is a widely-used description for a contour image. Another mid-crack code algorithm was proposed as a more precise method for image representation. We have developed a coding algorithm which is suitable to generate either chain code description or mid-crack code description by switching between two different tables. Since there is a strong urge to use parallel processing in image related problems, a parallel coding algorithm is implemented. This algorithm is developed on a pyramid architecture and a N cube architecture. Using link-list data structure and neighbor identification, the algorithm gains efficiency because no sorting or neighborhood pairing is needed. In this dissertation, the local symmetry deficiency (LSD) computation to calculate the local k-symmetry is embedded in the coding algorithm. Therefore, we can finish the code extraction and the LSD computation in one pass. The embedding process is not limited to the k-symmetry algorithm and has the capability of parallelism. An adaptive quadtree to chain code conversion algorithm is also presented. This algorithm is designed for constructing the chain codes of the resulting quadtree from the boolean operation of two quadtrees by using the chain codes of the original one. The algorithm has the parallelism and is ready to be implemented on a pyramid architecture. Our parallel processing approach can be viewed as a parallelization paradigm - a template to embed image processing algorithms in the chain coding process and to implement them in a parallel approach

Clasificación automática de anomalías asociadas con ausencia de información en superficies tridimensionales de objetos de forma libre

En este trabajo se propone un método computacional para clasificar anomalías relacionadas con ausencia de información sobre modelos tridimensionales de forma libre. Para ello, se hizo una exploración descriptiva de las propiedades geométricas globales y locales de las anomalías y una evaluación posterior de distintos métodos de clasificación utilizados en visión artificial y aplicaciones de reconstrucción tridimensional. El método propuesto logra un nivel de clasificación cercano al 90% y un tiempo de ejecución de alrededor de 100 milisegundos. Restringir la clasificación de acuerdo a la aplicación en específico se propone como trabajo futuro./Abstract. In this work it is proposed a computational method to classify anomalies related with information absence over free-form tridimensional models. For that, it was made a descriptive exploration of global and local geometric properties of anomalies and a posterior evaluation of different classification methods widely used in artificial vision and tridimensional reconstruction applications. The proposed method achieved a classification level near to 90% and an execution time near to 100 miliseconds. Constrain classification according to the specific application is suggested as future work.Maestrí

An investigation into the use of genetic algorithms for shape recognition

The use of the genetic algorithm for shape recognition has been investigated in relation to features along a shape boundary contour. Various methods for encoding chromosomes were investigated, the most successful of which led to the development of a new technique to input normalised 'perceptually important point' features from the contour into a genetic algorithm. Chromosomes evolve with genes defining various ways of 'observing' different parts of the contour. The normalisation process provides the capability for multi-scale spatial frequency filtering and fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this investigation because its performance can be analysed by applying schema analysis to the genes. A new method for measurement of gene diversity has been developed. It is shown that this diversity measure can be used to direct the genetic algorithm parameters to evolve a number of 'good' chromosomes. In this way a variety of sections along the contour can be observed. A new and effective recognition technique has been developed which makes use of these 'good' chromosomes and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved by selecting chromosomes and adjusting two thresholds, the values of which are found not to be critical. Difficulties associated with the calculation of a shape's fitness were analysed and the structure of the genes in the chromosome investigated using schema and epistatic analysis. It was shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H. Holland. Reasons are given to explain the minimum value for the mutation probability that is required for the evolution of a number of' good' chromosomes. Suggestions for future research are made and, in particular, it is recommended that the convergence properties of the standard genetic algorithm be investigated

Rainfall estimation for hydrology using volumetric weather radar

This thesis focuses specifically on weather radar rainfall measurements in strati form precipitation. In North-Western Europe this type of precipitation is most dominant in winter and leads to the largest hydro logical response of catchments. Unfortunately, the quality of uncorrected radar rainfall estimates starts decreasing at relatively close range from the radar for this type of precipitation. Therefore, as a first approach, a number of previously proposed radar error correction algorithms were applied in this thesis. The implementation of these methods shows a positive impact on the quality of the obtained precipitation measurements as compared to rain gauges. However, the traditional approach of applying a uniform Eulerian based algorithm for the entire radar umbrella to correct for VPR, limits its impact to improve the corrected weather radar precipitation measurement