Francisco Cuevas

By L. E. Toledo, F. J. Cuevas, J.F. Jimenez Vielma and J. H. Sossa

Part of the book: Advanced Topics in Measurements

By Francisco Cuevas, Miriam Rocha Navarro, Manuel Garcia Salido and Manuel Rodríguez Villegas

Metrological techniques using digital image processing techniques have been extended into different fields of science such as optics, meteorology, mineralogy, agriculture, and medicine, among others. In the field of medicine, particularly in dentistry, it is important to perform different dental measurements to support the biometric work of specialists using panoramic radiographic images. Due to the poor capturing of these radiographic images, several problems, such as poor contrast and quality, are generally present. As the detection of the dental area must be done using these images, this chapter presents an algorithm that will assist in bettering image quality and make the dental measurements needed. This is done by binarizing using the histogram statistics of the image for the determination of threshold in order to establish sections of the teeth and the detection of the intramaxillary section by fitting a nonlinear function. The proposed method is applied to panoramic digital radiographs of subjects with permanent dentition (≥12 years and <30 years). The algorithm achieved an adjustment of 96% of the processed radiographs as a result from patients of the School of Dentistry of the Universidad de la Salle Bajío.

Part of the book: Digital Image Processing

By Juan Pablo Guerra and Francisco Cuevas

Agriculture plays a crucial role in human survival, necessitating the development of efficient methods for food production. This chapter reviews Digital Image Processing (DPI) methods that utilize various color models to segment elements like leaves, fruits, pests, and diseases, aiming to enhance agricultural crop production. Recent DPI research employs techniques such as image subtraction, binarization, color thresholding, statistics, and convolutional filtering to segment and identify crop elements with shared attributes. DPI algorithms have a broad impact on optimizing resources for increased food production through agriculture. This chapter provides an overview of DPI techniques and their applications in agricultural image segmentation, including methods for detecting fruit quality, pests, and plant nutritional status. The review’s contribution lies in the selection and analysis of highly cited articles, offering readers a current perspective on DPI’s application in agricultural processes.

Part of the book: Digital Image Processing