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Paquete En Python Para La Reducción De Dimensionalidad En Imágenes a Color

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Paquete En Python Para La Reducción De Dimensionalidad En Imágenes a Color Departamento De Computación Título: Paquete en Python para la reducción de dimensionalidad en imágenes a color. Autora: Liliam Fernández Cabrera. Cabrera. Tutor: M.Sc. Roberto Díaz Amador. Santa Clara, Cuba, 2018 Este documento es Propiedad Patrimonial de la Universidad Central “Marta Abreu” de Las Villas, y se encuentra depositado en los fondos de la Biblioteca Universitaria “Chiqui Gómez Lubian” subordinada a la Dirección de Información Científico Técnica de la mencionada casa de altos estudios. Se autoriza su utilización bajo la licencia siguiente: Atribución- No Comercial- Compartir Igual Para cualquier información contacte con: Dirección de Información Científico Técnica. Universidad Central “Marta Abreu” de Las Villas. Carretera a Camajuaní. Km 5½. Santa Clara. Villa Clara. Cuba. CP. 54 830 Teléfonos.: +53 01 42281503-1419 i La que suscribe Liliam Fernández Cabrera, hago constar que el trabajo titulado Paquete en Python para la redimensionalidad de imágenes a color fue realizado en la Universidad Central “Marta Abreu” de Las Villas como parte de la culminación de los estudios de la especialidad de Licenciatura en Ciencias de la Computación, autorizando a que el mismo sea utilizado por la institución, para los fines que estime conveniente, tanto de forma parcial como total y que además no podrá ser presentado en eventos ni publicado sin la autorización de la Universidad. ______________________ Firma del Autor Los abajo firmantes, certificamos que el presente trabajo ha sido realizado según acuerdos de la dirección de nuestro centro y el mismo cumple con los requisitos que debe tener un trabajo de esta envergadura referido a la temática señalada. ____________________________ ___________________________ Firma del Tutor Firma del Jefe del Laboratorio ii RESUMEN Un enfoque eficiente de las técnicas del procesamiento digital de imágenes permite dar solución a muchas problemáticas en los campos investigativos que precisan del uso de imágenes para búsquedas de información. Estas imágenes después de ser procesadas tienen una amplia gama de usos en diferentes campos tales como la medicina, en diagnósticos en tomografías digitalizadas de pacientes enfermos; la astrofísica, al determinar estados de la materia en cuerpos celestes muy lejanos, así como la robótica en detección de objetos en tiempo real, entre otros. La reducción de dimensionalidad en imágenes de color a escala de grises como parte del procesamiento digital de imágenes es vital en la percepción humana para observadores con una visión de color deficiente y en el análisis de estas imágenes por los métodos de búsqueda de información. La eficiencia de los algoritmos que utilizan la reducción de dimensionalidad de imágenes a color es vital debido a la importancia de los campos en que se utiliza y a los grandes volúmenes de información que se procesan en sus aplicaciones, por lo que surge la necesidad de un paquete de algoritmos que realice una eficiente reducción de dimensionalidad de imágenes a color. En este trabajo de diploma se evaluarán las posibilidades de implementación de herramientas de reducción de dimensionalidad en imágenes en un paquete en el lenguaje Python que sea no propietario, fácil de utilizar y disponible. En la implementación del paquete se aprovecha la ventaja que ofrece contar con un compendio de los métodos de conversión a escala de grises más importantes desarrollados en este siglo de acuerdo con las publicaciones recientes, al ser esta un área activa de investigación. iii ABSTRACT An efficient approach to digital processing technics allows achieving solutions to problems in diverse investigation field that use digital images for information search. These images after being processed have a wide range of uses in fields like medicine, diagnosing sick patients by digitalized tomographies; astrophysics, detecting matter status in faraway celestial bodies, and robotics, real time object detection, among others. The reduction of color images dimensionality as part of the digital image processing is vital for human perception of images in grayscale to vision deficient human observers and analysis of these grayscale images by knowledge search methods. The efficiency of the algorithms that use dimensionality reduction of color images is fundamental because the huge information volume that needs to be processed; this is why the necessity of an algorithms package that reduces the dimensionality of color images in an efficient and robust way. In this basic degree thesis there will be evaluated the implementation of dimensionality reduction methods in python programming language, which is free software, easy to use, and available. In the package implementation we take advantage of a compendium of state of the art grayscale conversion methods developed in this century, related to recent publications. iv TABLA DE CONTENIDOS INTRODUCCIÓN..................................................................................................................... 1 CAPÍTULO 1. GENERALIDADES .................................................................................... 4 1.1 La Imagen Digital a Color ............................................................................................ 4 1.1.1 Imagen Digital ............................................................................................................. 5 1.1.2 Imagen a Color. Espacio de Color .............................................................................. 6 1.1.3 Estándares de Colores ................................................................................................. 9 1.2 Técnicas de Reducción de los Espacios de Color ............................................................ 10 1.2.1 Métodos clásicos de conversión de color a escala de grises. .................................... 11 1.2.2 El Problema de la conversión de color a escala de grises. ........................................ 14 1.2.3 Categorías de los métodos en estado-del-arte de C2G. ............................................. 16 1.3 Conclusiones del capítulo ................................................................................................ 26 CAPÍTULO 2. Materiales y Métodos ............................................................................ 27 2.1 El Lenguaje Python ..................................................................................................... 27 2.2 Diagrama de las Bibliotecas de Reducción de Dimensionalidad creadas................... 28 2.2.1 Decolorize (Decolor_linear2006) ........................................................................ 28 2.2.2 GcsDecolor2 ........................................................................................................ 29 2.2.3 SPDecolor ............................................................................................................ 31 2.2.4 Kim09 (Decolor_nonlinear2009) ........................................................................ 33 2.3 Procedimiento de evaluación utilizado. ...................................................................... 34 2.3.1 Diseño del experimento ....................................................................................... 35 2.3.2 Bases de casos utilizadas ..................................................................................... 35 2.3.3 Medidas Objetivas ............................................................................................... 35 2.3.4 Medidas Subjetivas .............................................................................................. 38 2.4 Análisis Estadístico de los Resultados ........................................................................ 38 2.5 Conclusiones del Capítulo .......................................................................................... 39 v CAPÍTULO 3. Resultados y Discusión ........................................................................ 40 3.1 Evaluación comparativa de los métodos implementados. .......................................... 40 3.1.1 Objetiva. .............................................................................................................. 40 3.1.2 Subjetiva. ............................................................................................................. 41 3.2 Diseño e implementación del paquete de conversión de imágenes a escala de grises: C2G y la herramienta asociada: C2GApp. ............................................................................. 43 3.2.1 Implementación del paquete de algoritmos C2G ................................................ 44 3.2.2 Implementación de la aplicación visual C2G_app .............................................. 45 3.3 Conclusiones del capítulo ........................................................................................... 47 CONCLUSIONES .................................................................................................................. 48 RECOMENDACIONES ........................................................................................................ 49 BIBLIOGRAFÍA ..................................................................................................................... 50 ANEXOS .................................................................................................................................. 53 vi LISTA DE FIGURAS Figura 1 Ejemplo de separación de canales RGB........................................................................ 4 Figura 2 Comparación de cromaticidades encerradas en un espacio
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