Optimización Bio-Inspirada de un Sistema de Inferencia Difusa para la Generación de Efectos Psicodelicos en Imágenes Digitales

The generation of effects on digital images through computers has created a new age for different artistic trends. Particularly, the digital age has had a remarkable impact on psychedelic art. In this paper we propose a fuzzy inference system for color manipulation in digital images with the purpose of modifying their artistic style. The proposed system aims to create a combination of different colors to generate a new visual sensation, much like the current psychedelic art. The proposed system is divided into two parts.The first part of the method consists in the creation of a fuzzy logic system that, through fuzzy sets, and rules of change generates color images that produced this psychedelic effect. In the second part, the differential evolution algorithm optimizes parameters of the proposed inference system and adjusts the results to the psychedelic art style used.La generación de efectos en imágenes digitales a través de los computadores se ha convertido en una nueva era para distintas corrientes artísticas. En particular, la era digital ha tenido un impacto notable en el arte psicodélico. En este artículo proponemos un sistema de inferencia difusa para la manipulación del color en imágenes digitales con el propósito de modificar su estilo artístico. El sistema propuesto tiene como objetivo generar una combinación de colores diferente que genere una nueva sensación visual, como lo pretende la corriente de arte psicodélico. El sistema propuesto está divido en dos partes. La primera parte del método consiste en un sistema de lógica difusa que a través de conjuntos difusos y reglas la modificación de color genera como resultado imágenes cuyos colores producen este efecto psicodélico. En la segunda parte, se utiliza el algoritmo de evolución diferencial para optimizar parámetros del sistema de inferencia propuesto y ajustar los resultados al estilo de arte psicodélico

Extraction of Contextual Knowledge and Ambiguity Handling for Ontology in Virtual Environment

This dissertation investigates the extraction of knowledge from a known environment. Virtual ontology – the extracted knowledge – is defined as a structure of a virtual environment with semantics. While many existing 3D reconstruction approaches can generate virtual environments without structure and related knowledge, the use of Metaearth architecture is proposed as a more descriptive data structure for virtual ontology. Its architecture consists of four layers: interactions and relationships between virtual components can be represented in the virtual space layer; and the library layers contribute to the design of large-scale virtual environments with less redundancy; and the mapping layer links the library layer to the virtual space layer; and the ontology layer functions as a context for the extracted knowledge. The dissertation suggests two construction methodologies. The first method generates a scene structure from a 2D image. Unlike other scene understanding techniques, the suggested method generates scene ontology without prior knowledge and human intervention. As an intermediate process, a new and effective fuzzy color-based over-segmentation method is suggested. The second method generates virtual ontology with 3D information using multi-view scenes. The many ambiguities in extracting 3D information are resolved by employing a new fuzzy dynamic programming method (FDP). The hybrid approach of FDP and 3D reconstruction method generates more accurate virtual ontology with 3D information. A virtual model is equipped with virtual ontology whereby contextual knowledge can be mapped into the Metaearth architecture via the proposed isomorphic matching method. The suggested procedure guarantees the automatic and autonomous processing demanded in virtual interaction analysis with far less effort and computational time