Evaluación de descriptores para la detección automática de fallas en fabricación utilizando máquinas de soporte vectorial

Este documento presenta la evaluación de un método de clasificación de fallas en productos terminados utilizando la combinación de descriptores de color, forma y textura. Se utiliza una Máquina de Vectores de Soporte multiclase (SVM-Support Vector Machine) y se construye una base de datos anotada capturando botellas de plástico con 11 situaciones de fabricación entre botellas en buen estado y botellas con imperfectos como rasgaduras, golpes, hendiduras, etc; bajo diferentes condiciones no controladas (ruido, iluminación, escala, entre otras). La etapa de fusión propone una combinación lineal de características y para calcular el desempeño de descriptores y fusión de datos, se utilizó una metodología de validación cruzada aplicando el método de Montecarlo. La configuración de SVM utiliza la metodología multiclase “One-vs-All” con Kernel Radial Gaussiano. La detección se realiza inicialmente aplicando descriptores individuales y combinados

Natural scene classification, annotation and retrieval. Developing different approaches for semantic scene modelling based on Bag of Visual Words.

With the availability of inexpensive hardware and software, digital imaging has become an important medium of communication in our daily lives. A huge amount of digital images are being collected and become available through the internet and stored in various fields such as personal image collections, medical imaging, digital arts etc. Therefore, it is important to make sure that images are stored, searched and accessed in an efficient manner. The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a standard choice for many computer vision tasks. Based on this promising model, this thesis investigates three main problems: natural scene classification, annotation and retrieval. Given an image, the task is to design a system that can determine to which class that image belongs to (classification), what semantic concepts it contain (annotation) and what images are most similar to (retrieval). This thesis contributes to scene classification by proposing a weighting approach, named keypoints density-based weighting method (KDW), to control the fusion of colour information and bag of visual words on spatial pyramid layout in a unified framework. Different configurations of BOW, integrated visual vocabularies and multiple image descriptors are investigated and analyzed. The proposed approaches are extensively evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories using 10-fold cross validation. The second contribution in this thesis, the scene annotation task, is to explore whether the integrated visual vocabularies generated for scene classification can be used to model the local semantic information of natural scenes. In this direction, image annotation is considered as a classification problem where images are partitioned into 10x10 fixed grid and each block, represented by BOW and different image descriptors, is classified into one of predefined semantic classes. An image is then represented by counting the percentage of every semantic concept detected in the image. Experimental results on 6 scene categories demonstrate the effectiveness of the proposed approach. Finally, this thesis further explores, with an extensive experimental work, the use of different configurations of the BOW for natural scene retrieval.Applied Science University in Jorda

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform