Preface

*Vision Sensors - Recent Advances* reflects a selection of recent developments within the field of vision sensors and their associated algorithms. Vision sensors use captured images to localize the target object's presence and orientation to the background. As the objects can appear in any area, the accuracy achieved with a single model is minimal, but overall accuracy can be improved using the combined predictions of multiple models. A bounding box is used for training these models so that the sensor can learn different object locations. Among the image processing techniques that involve the use of vision sensors are image measurement, image analysis, image transformation, image filtering, segmentation algorithms, supervised and unsupervised algorithms for classification, image recognition and parallel computing, and real-time algorithms. Recent literature reviews vision sensors with applications for localization of robot end effectors, panoramic vision sensors, wireless vision sensors, automated vision sensor inspection, multi-sensor imaging for medical data, and object recognition.

This book contains seven chapters by researchers and professionals in the field of vision sensors with applications in image processing. Chapter 1 discusses medical applications for an RGB-D multi-sensor imaging system, with particular reference to a 3D optical (RGB-D) craniofacial scanning system using multiple depth camera sensors. The system is designed to obtain an extensive dataset of scans (head and face) from various views as an alternative pre-diagnostic method for obstructive sleep apnea.

Chapter 2 considers a multi-object recognition system, using a feature descriptor and neural classifier, and based on a histogram of oriented gradients and multilayer perceptron (ORS HOG-MLP). The proposed improvement in the calculus of the HOG algorithm accurately represents different objects in multiclass applications, reducing the rate of false positives.

Chapter 3 introduces a new method for the diagnosis of Alzheimer's disease combining electroencephalogram (EEG) signals and magnetic resonance imaging (MRI). Support-vector machine and Elman neural network classifiers are used, with the optimal combined features extracted by analysis of variance to provide a good classification of the disease.

Chapter 4 proposes a saliency subitizing process (SSP) model which generates an initial saliency map using subitizing information without any seeds from unsupervised methods. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range, so that the subitizing information will tell us the number of featured objects in a given image.

Chapter 5 analyzes the therapeutic effects of using a leap motion controller (LMC) sensor to assess hand fine motor dexterity, with particular reference to the box and blocks test (BBT). The LMC device and its forms of interaction in virtual reality environments (immersive and non-immersive) are examined, together with manual function assessment, traditional BBT and its virtual version (VBBT), and VBBT integrated with the LMC in physical therapy practice.

Chapter 6 introduces a method for detecting the precise 6-degrees-of-freedom (6DOF) localization of robotic arms' end effectors without referencing any additional feature target during in-line robot operation. This method facilitates precise robot positioning and monitoring.

Chapter 7 discusses a new hybrid digital-optical architecture that combines an optical correlator with a supervised and/or unsupervised classifier learning algorithm to create a fast SDF (synthetic discriminant function) k-means classifier.

I thank the researchers and professionals whose chapters have contributed to the publication of this book, and I hope that readers will find it interesting.

> **Francisco Javier Gallegos-Funes** Instituto Politécnico Nacional de México, Mexico City, Mexico

**Chapter 1**

**Abstract**

Applications

*Libor Hargaš and Dušan Koniar*

the same with the price and accessibility.

**1. Introduction**

the health status of patients.

Usage of RGB-D Multi-Sensor

This chapter presents an inclusion of 3D optical (RGB-D) sensors into medical

methods, which are expensive in many aspects. It focuses on obstructive sleep apnea, the respiratory syndrome that occurs in an increasing proportion of the population, including children. We introduce the novel application, a response to the request for an alternative pre-diagnostic method for obstructive sleep apnea in the region of Slovakia. The main objective of the proposed system is to obtain an extensive dataset of scans (head and face) from various views and add detailed information about patient. The application consists of the 3D craniofacial scanning system using multiple depth camera sensors. Several technologies are presented with the proposed methodology for their comprehensive comparison based on depth sensing and evaluation of their suitability for parallel multi-view scanning (mutual interference, noise parameters). The application also includes the assistance algorithm guaranteeing the patient's head positioning, graphical interface for scanning management, and standardized EU medical sleep questionnaire. Compared to polysomnography, which is the golden standard for this diagnostics, the needed data acquisition time is reduced significantly,

**Keywords:** RGB-D sensors, multi-sensor system, 3D imaging, medical applications, obstructive sleep apnea, time of flight sensor, structured light sensor, stereo vision

Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder with arising prevalence. The course of the disease is followed by repeating breath interruptions during sleep. The reason is the collapse of soft upper airway tissue. This restriction of ventilation results in several breathing difficulties, such as snoring during sleep and hypoxemia. OSAS also leads to long-term changes in autonomous functions, hypertension, or reduced left ventricular function. As a result of the propagation and activation of inflammatory pathways, immune regulation is often disrupted in pediatric patients. Early diagnostics and relevant treatment are the keys for improve

clinical practice, as an alternative to the conventional imaging and diagnostic

Imaging System for Medical
