1 - Image Acquisition Systems

Abstract

This chapter discusses physical assessments of agricultural materials and foodstuffs using machine vision systems. Traditionally, quality inspection is performed by trained human inspectors, who approach the problem of quality assessment in two ways: seeing and feeling. In addition to being costly, this method is highly variable and decisions are not always consistent between inspectors or from day to day. This is, however, changing with the advent of electronic imaging systems and with the rapid decline in cost of computers, peripherals, and other digital devices. Moreover, the inspection of foodstuffs for various quality factors is a very repetitive task, which is also very subjective in nature. In this type of environment, machine vision systems are ideally suited for routine inspection and quality assurance tasks. Backed by powerful artificial intelligence systems and state-of-the-art electronic technologies, machine vision provides a mechanism in which the human thinking process is simulated artificially. To date, machine vision has extensively been applied to solve various food engineering problems, ranging from simple quality evaluation of food products to complicated robot guidance applications. CCD vision systems, covering both the visible and infrared regions, are suitable for surface imaging, while computed tomography (CT), magnetic resonance imaging, and emission tomography (ET) are oriented for imaging internal structures. Of the latter three, CT is suitable for imaging hard and solid objects, MRI for functional imaging, and ET for conductivity or permittivity mapping. Some of these technologies are already available commercially, while some are still in the development stage. Currently under development is a system that can combine results from various modalities to enhance and improve image quality further. With careful calibration, images from different modalities can be registered and superimposed, giving rise to what is presently known as multimodal imaging or the sensor fusion technique.