Nowadays, object recognition is widely studied under the paradigm of matching local features. This work describes a genetic programming methodology that synthesizes mathematical expressions that are used to improve a well known local descriptor algorithm. It follows the idea that object recognition in the cerebral cortex of primates makes use of features of intermediate complexity that are largely invariant to change in scale, location, and illumination. These local features have been previously designed by human experts using traditional representations that have a clear, preferably mathematically, well-founded definition. However, it is not clear that these same representations are implemented by the natural system with the same structure. Hence, the possibility to design novel operators through genetic programming represents an open research avenue where the combinatorial search of evolutionary algorithms can largely exceed the ability of human experts. This paper provides evidence that genetic programming is able to design new features that enhance the overall performance of the best available local descriptor. Experimental results confirm the validity of the proposed approach using a widely accept testbed and an object recognition application.

Se estudia las características principales del comportamiento del consumidor en plataformas de compra en línea en el estado sur de Sonora. Esto se hace mediante un estudio cualitativo basado en la metodología de la Teoría Fundamentada. La idea principal es identificar y categorizar variables sobre la compra en línea basándose en factores relevantes como la motivación de compra, preferencias, hábitos de consumo y patrones de compra. Para llevar a cabo el estudio, se realizaron entrevistas semi-estructuradas a consumidores del estado sur de Sonora, México, con experiencia en compras en línea. De esta manera, al concluir el análisis, se obtuvieron tres categorías principales: motivación de compra, experiencias del consumidor y comportamiento. También se identificaron una serie de variables que tienen mayor influencia en las preferencias, decisión de compra y el patrón de compra en los consumidores. Los resultados obtenidos proporcionan información de utilidad para la creación de estrategias que permitan tomar decisiones efectivas mediante su aplicación en el diseño de plataformas en línea ofreciendo un servicio personalizado a los consumidores.

Recognizing and localizing objects is a classical problem in computer vision

Nowadays, object recognition based on local invariant features is widely acknowledged as one of the best paradigms for object recognition due to its robustness for solving image matching across different views of a given scene. This paper proposes a new approach for learning invariant region descriptor operators through genetic programming and introduces another optimization method based on a hill-climbing algorithm with multiple re-starts. The approach relies on the synthesis of mathematical expressions that extract information derived from local image patches called local features. These local features have been previously designed by human experts using traditional representations that have a clear and, preferably mathematically, well-founded definition. We propose in this paper that the mathematical principles that are used in the description of such local features could be well optimized using a genetic programming paradigm. Experimental results confirm the validity of our approach using a widely accepted testbed that is used for testing local descriptor algorithms. In addition, we compare our results not only against three state-of-the-art algorithms designed by human experts, but also, against a simpler search method for automatically generating programs such as hill-climber. Furthermore, we provide results that illustrate the performance of our improved SIFT algorithms using an object recognition application for indoor and outdoor scenarios.

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.