Pedro Bastos Costa

Determining camera parameters for round glassware measurements

Nowadays there are many types of accessible cameras, including digital single lens reflex ones. Although these cameras are not usually employed in machine vision applications, they can be an interesting choice. However, these cameras have many available parameters to be chosen by the user and it may be difficult to select the best of these in order to acquire images with the needed metrological quality. This paper proposes a methodology to select a set of parameters that will supply a machine vision system with the needed quality image, considering the measurement required of a laboratory glassware.

Computer Vision Analysis of a Melting Interface Problem with Natural Convection

This study presents some Computer Vision techniques to analyze a phase change problem with natural convection. The analysis and interpretation of images are important to understand the phenomenon under study. Methods of image processing and analysis are used to validate the mathematical model and to automate the process of extracting information from the experimental model. The images produced by the experiment show the melting of a vertical ice layer into a heated rectangular cavity in the presence of natural convection and maximum density.

Important Parameters for Image Color Analysis: An Overview

In recent years it is noteworthy how the use of Computational Vision techniques in processing and quality control of products has advanced. The available resources in both electronic and computing were important factors in the automation development, allowing constant monitoring during the process. Such techniques have systematically evolved in the international commerce. However, there is a lack of standardization on quality control of products using image analysis. Measurements using digital image should consider important aspects, such as the effects of lighting, characteristics of the environment, the types of illuminants, the observers, to name a few, all that beyond the traceability of the system and the definition of standards. With this in mind, the aim of this chapter is to discuss the relevance of the main variables that influence the color measurement of images using computer vision techniques, in order to promote some thought about the needs of standardization.

Metrology by Image: Discussing the Accuracy of the Results

The development of new inspection techniques have been studied due to their important role in ensuring the quality of products and services. Achieving technical specifications of products considering acceptance criteria requires knowledge of assured inspection methods. Inspection techniques have evolved expressively, lacking, however, in some cases, of certified standardization. The past ten years have experienced considerable development of inspection methods. In part, this is due to the need to meet the security and reliability requirements for consumer protection. Moreover, the extraordinary advances in electronics and computers were also important factors for this advance. In this context visual inspection by images is being applied in many applications, allowing the identification of innumerous product features, like cracks, corrosion, distortion, misalignments, porosity, among others. But, inspections and measurements require calibration of instruments, devices and systems. In order to obtain confident measurements it is important to study and specify the suitable lighting system, camera, lens and principally the best computer measurement algorithms. Thus the aim of this paper is to discuss the relevance of the main parameters that influence the measurement of images using computer vision techniques.

New Approach for Non-Contact Measurement Using Vision Probe

This paper has the purpose of presenting a study to determine the accuracy of a method for image measurement called Null Detector. In this new method, instead of determining the distance between two borders by counting pixels or by using subpixel approximations, the image itself is shifted and the border of the object to be measured is acquired in different points of the image. By using numerical adjustments for a specific point of the image, the real location for the border is found. The advantage of this method is that the pixel length does not need to be calibrated since it does not involve counting pixels.