A computer vision system tries to mimic our primary sense (sight) in order to gather information without the need for physical interaction, in fact such systems are able to grade automatically, and extract useful information with a degree of sensitivity closer to that of a human, reducing considerably the margin of error. By performing digital image processing, defined as the acquisition and processing of visual information by computer, computer vision systems allow analyzing image data for specific applications in order to determine how images can be used to extract the required information. Among the most important features for accurate classification and sorting of products it can be mentioned the shape. The shape of objects or regions of interest are important features used for content representation, and require good segmentation to detect objects or regions. Basically, shape characterization is of two types: boundary-based and regionbased. Boundary-based shape features include rectilinear shapes, polygonal approximation, finite element models, and Fourier-based shape descriptors. Region-based features include statistical moments and grid-based approaches. Object shape detection using a technique based on Hough Transform for further segmentation is presented on this paper

Recent developments in Computer-aided Diagnosis (CAD) systems as a countermeasure to the increasing number of untreated cases of eye diseases related to visual impairment (such as diabetic retinopathy or age-related macular degeneration) have the potential to yield in low-to-mid income countries a comfortable and accessible alternative to obtaining a general ophthalmological study necessary for follow-up medical attention. In this work, a multi-stage ensemble-based system for the diagnosis of glaucomatous optic neuropathy (GON) is proposed. GON diagnosis is based on a binary classification procedure working in conjunction with a multi-stage block based on image preprocessing and feature extraction. Our preliminary data show similar results compared to current studies considering metrics such as Accuracy, Sensitivity, Specificity, AUC (AUROC), $F_1\mathrm{score}$, and the use of Matthews Correlation Coefficient (MCC) as an additional performance metric is proposed.

Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications.

Combination of digital X-ray with image processing techniques has the potential to extract useful information for healthcare professionals (physicians). From all the information that can be extracted from X-ray images, information concerning the human cervical vertebrae is relevant for the medical area. Therefore, in this work we present a simple enhanced region of interest (ROI) selection tool to select automatically the region that contains most of the information concerning to cervical vertebrae. The ROI-selection method reduces the size of a lateral or frontal digital X-ray by 30-60% without losing significance in the resulting image. This is achieved by an adjustment of dimensions in the image while the cervical area is preserved. Moreover, the visual quality is improved by performing a contrast enhancement in the region of interest. •Automatic threshold selection is computationally more efficient than traditional image segmentation techniques.•Reduce size in comparison with original image (enhancing ROI).•Independence of depth gray scale space.

Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications.

This article presents partial results of the development and evaluation of an architecture based on Wireless Sensor Network (WSN), which allows monitoring of physiological variables in patients who develop daily activities both at home as at a hospital. The variables measured are: temperature, oxygen saturation and heart rate, also includes the measurement of contextual variables (lighting, temperature and relative humidity). The architecture consists of sensor nodes wearable by the patient, an IEEE 802.15.4/802.11 bridge, a local server and a remote server (medical server).