Contents


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

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

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 applica-

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

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

imaging for medical data, and object recognition.

tions, reducing the rate of false positives.

good classification of the disease.

sleep apnea.
