Two Decades of Colorization and Decolorization for Images and Videos

Colorization is a computer-aided process, which aims to give color to a gray image or video. It can be used to enhance black-and-white images, including black-and-white photos, old-fashioned films, and scientific imaging results. On the contrary, decolorization is to convert a color image or video into a grayscale one. A grayscale image or video refers to an image or video with only brightness information without color information. It is the basis of some downstream image processing applications such as pattern recognition, image segmentation, and image enhancement. Different from image decolorization, video decolorization should not only consider the image contrast preservation in each video frame, but also respect the temporal and spatial consistency between video frames. Researchers were devoted to develop decolorization methods by balancing spatial-temporal consistency and algorithm efficiency. With the prevalance of the digital cameras and mobile phones, image and video colorization and decolorization have been paid more and more attention by researchers. This paper gives an overview of the progress of image and video colorization and decolorization methods in the last two decades.Comment: 12 pages, 19 figure

Micorrizas arbusculares y las técnicas de visión artificial para su identificación

This article aims to analyze the leading computer vision techniques and strategies used in designed systems to automatically identify arbuscular mycorrhizal fungi, addressing general aspects of mycorrhizae and their taxonomic classification. Mycorrhizae are symbiotic associations between plants' roots and certain fungi groups. They are characterized by great benefits to the surrounding soil, the plants, and the derived productive processes. The work was developed with a specialized information collection methodology based on specific search criteria, selecting relevant publications, in a time range between 2014 and 2021, in the Scopus, Scielo, Dialnet, and Google Scholar databases. The study's results revealed that fuzzy mathematical morphology is an essential technique in the segmentation of fungal spores. In general, the studies developed are based on a binary identification of the spores, where the Hough transform, and artificial neural networks are the combined techniques that report better results. This study concludes that it is possible to assist the identification process of mycorrhizal fungi from artificial vision techniques. It contributes by indicating a lack of information regarding non-binary classification systems, which are important and must be considered to support advanced classification processes, according to the number of families and genera reported in the literature.El objetivo de este artículo fue analizar las principales técnicas y estrategias de visión artificial utilizadas en sistemas diseñados para la identificación automática de hongos formadores de micorrizas arbusculares, abordando aspectos generales de las micorrizas y su clasificación taxonómica. Las micorrizas son asociaciones simbióticas entre las raíces de las plantas y determinados grupos de hongos, se caracterizan por generar grandes beneficios al suelo circundante, a las plantas y a los procesos productivos derivados. El trabajo se desarrolló con una metodología de recolección de información especializada a partir de criterios de búsqueda específicos, seleccionando publicaciones relevantes, en un rango de tiempo entre el año 2014 y 2021, en las bases de datos de Scopus, Scielo, Dialnet y Google Académico. Los resultados del estudio revelaron que la morfología matemática difusa es una técnica importante en la segmentación de las esporas de hongos y, en general, los estudios desarrollados se basan en una identificación binaria de las esporas, donde la transformada de Hough y las redes neuronales artificiales son las técnicas combinadas que reportan mejores resultados. El presente estudio permitió concluir que es posible auxiliar el proceso de identificación de hongos formadores de micorrizas arbusculares a partir de técnicas de visión artificial, y contribuye indicando un vacío de información respecto de sistemas de clasificación no binaria, los cuales son importantes y se deben tener en cuenta para apoyar procesos de clasificación avanzados, de acuerdo con la cantidad de familias y géneros reportados en la literatura