This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them.
Resumen. En el presente artículo analizamos la aplicación de los conocimientos adquiridos en las diferentes asignaturas relacionadas con la Ingeniería de Computadores que componen el Grado en Ingeniería Informática con el uso de metaheurísticas que se imparten en la especialidad de Computación y Sistemas Inteligentes, haciendo especial enfoque en los Algoritmos Evolutivos. Nos centraremos en los Algoritmos Meméticos, comparando su rendimiento frente a los Algoritmos Genéticos. Finalmente, se comprobará la mejora del rendimiento de una metaheurística cuando se aplica Programación Paralela.
Fungal morphogenesis is an exciting field of cell biology and several mathematical models have been developed to describe it. These models require experimental evidences to be corroborated and, therefore, there is a continuous search for new microscopy and image analysis techniques. In this work, we have used a Canny-edge-detector based technique to automate the generation of hyphal profiles and calculation of morphogenetic parameters such as diameter, elongation rates and hyphoid fitness. The results show that the data obtained with this technique are similar to published data generated with manualbased tracing techniques and that have been carried out on the same species or genus. Thus, we show that application of edge detector-based technique to hyphal growth represents an efficient and accurate method to study hyphal morphogenesis. This represents the first step towards an automated analysis of videomicroscopy images of fungal morphogenesis.
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