El problema de Alhacén

El presente documento es un estudio detallado del problema conocido bajo el título de Problema de Alhacén. Este problema fue formulado en el siglo X por el filósofo y matemático árabe conocido en occidente bajo el nombre de Alhacén. El documento hace una breve presentación del filósofo y una breve reseña de su trascendental tratado de óptica Kitab al-Manazir. A continuación el documento se detiene a estudiar cuidadosamente los lemas requeridos para enfrentar el problema y se presentan las soluciones para el caso de los espejos esféricos (convexos y cóncavos), cilíndricos y cónicos. También se ofrece una conjetura que habría de explicar la lógica del descubrimiento implícita en la solución que ofreció Alhacén. Tanto los lemas como las soluciones se han modelado en los software de geometría dinámica Cabri II-Plus y Cabri 3-D. El lector interesado en seguir dichas modelaciones debe contar con los programas mencionados para adelantar la lectura de los archivos. En general, estas presentaciones constan de tres partes: (i) formulación del problema (se formula en forma concisa el problema); (ii) esquema general de la construcción (se presentan los pasos esenciales que conducen a la construcción solicitada y las construcciones auxiliares que demanda el problema), esta parte se puede seguir en los archivos de Cabri; y (iii) demostración (se ofrece la justificación detallada de la construcción requerida). Los archivos en Cabri II plus cuentan con botones numerados que pueden activarse haciendo “Click” sobre ellos. La numeración corresponde a la numeración presente en el documento. El lector puede desplazar a su antojo los puntos libres que pueden reconocerse porque ellos se distinguen con la siguiente marca (º). Los puntos restantes no pueden modificarse pues son el resultado de construcciones adelantadas y ajustadas a los protocolos recomendados en el esquema general.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Detecting air pockets for architectural concrete quality assessment using visual sensing

Air pockets, one kind of concrete surface defects, are often created on formed concrete surfaces during concrete construction. Their existence undermines the desired appearance and visual uniformity of architectural concrete. Therefore, measuring the impact of air pockets on the concrete surface in the form of air pockets is vital in assessing the quality of architectural concrete. Traditionally, such measurements are mainly based on in-situ manual inspections, the results of which are subjective and heavily dependent on the inspectors’ own criteria and experience. Often, inspectors may make different assessments even when inspecting the same concrete surface. In addition, the need for experienced inspectors costs owners or general contractors more in inspection fees. To alleviate these problems, this paper presents a methodology that can measure air pockets quantitatively and automatically. In order to achieve this goal, a high contrast, scaled image of a concrete surface is acquired from a fixed distance range and then a spot filter is used to accurately detect air pockets with the help of an image pyramid. The properties of air pockets (the number, the size, and the occupation area of air pockets) are subsequently calculated. These properties are used to quantify the impact of air pockets on the architectural concrete surface. The methodology is implemented in a C++ based prototype and tested on a database of concrete surface images. Comparisons with manual tests validated its measuring accuracy. As a result, the methodology presented in this paper can increase the reliability of concrete surface quality assessment

A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.