About the book
Automatic Systems with Vision Sensors apply novel algorithms in the acquired digital images to solve problems in different research fields such as medical imaging, remote sensing, and other real-world problems.
Digital images can be easily distorted by noise during the acquisition, processing, and transmission. Noise level is an important parameter to consider in image processing algorithms, including denoising, compression, feature extraction, motion estimation, optical flow, segmentation, super-resolution, and image quality assessment. Their performance depends on the accuracy of the noise level estimate.
Image denoising is an important stage to improve the accuracy of many image processing techniques, such as image segmentation and recognition. Image segmentation is another important stage in computer vision applications. Many methodologies utilize both stages in a unique algorithm to solve the problem of the segmentation of noisy images to provide better classification and recognition compared to algorithms that independently use these two stages.
The goal of this book will be to collect original research chapters that develop or apply new theories and/or hardware or software to process the acquired noisy images to solve the problem of Segmentation of noisy images in the field of medical imaging, remote sensing, engineering, and other research applications.