Sensors I: Color Imaging and Basics of Image Processing

Abstract

This chapter provides an overview of the basic concepts of color imaging and image processing techniques applied to sensing, monitoring and robotic operations in agriculture. To obtain good results with a vision system, it is very important to acquire high-quality images, particularly when captured with moving platform in a natural environment, by selecting a proper camera, acquisition settings, and lighting conditions. Image acquisition using CMOS and CCD sensors are explained along with proper adjustment of various imaging parameters such as aperture and shutter speed. Various color models that are relevant to image processing are described including RGB, HSV, HLS, CIELAB, and CIELUV as well as conversions between different color models. Following the introduction of color models, some basic image preprocessing techniques including image enhancement using histograms, morphological operations, and lowpass filtering are described. Also, various segmentation methods are discussed such as pixelwise or region–based techniques and classifiers. In addition, the chapter describes different object detection methods (with examples) that utilize various features such as colors, shapes, and textures. Hough transform and pattern matching are also commonly used techniques to detect various objects, and example applications based on these techniques are discussed. Finally, some of the crucial challenges for outdoor imaging such as varying illumination, occlusion, clustering, and movement of either the object or the camera when it is installed on a ground robotic system are discussed and a brief thought on future direction around these topics is presented.