Environmental and physiological parameters measurement in images and video

Abstract

Measurement in images and video is a new challenging research direction. Up to now, cameras are mostly used as interaction devices. Computer vision technologies, however, can turn an ordinary video camera to a powerful tool for counting, measuring and inspecting. Using the camera as a measuring sensor is very interesting as allows creating a ”universal” measurement instrument, where new type of measurements can be added just by changing the software. Appearance of smartphones brings measurements in image and video to the new level, introducing a small, portable, autonomous measurement device. A lot of efforts have been made to convert smartphones to mobile tools for measuring the object length, width, size, angles, area, dimensions etc. This Ph.D. thesis investigates novel image and video processing techniques and shows how they can be used for non-invasive measurement of various environmental and physiological parameters. The three logical steps describe the possible types of measurements: in static image, in video and using smartphones. First, the case with a single image affected by a motion blur is considered and appropriate techniques for locating the regions with motion blur and parameters extraction are presented. A new method to detect the locally motion blurred regions from the image with complex still background is introduced. Analysis in the frequency domain, statistical analysis and windowing techniques are used to find blurred object, and the Fourier and Radon transformations are used to compute its motion characteristics. Analysis of video allows measuring additional characteristics of the objects that change over time. Monitoring of the human fatigue level is done by eyelid blinks detection and analysis. Two solutions are proposed: the non-invasive blink detection system based on infrared camera and webcam. The usage of infrared camera with switching light is used for fast and easy pupil detection in each frame, while the webcam is used to create a very cheap but still effective system. The problem of eyes detection is solved by using a cascade of boosted classifiers based on Haar-like features. The algorithm is proposed to detect closure and opening of the eyes and to distinguish voluntary blinks from the involuntary ones. Finally, the smartphone is used for photoplethysmogram acquisition and measurement of vital parameters. The proposed approach utilizes a concept of image acquisition similar to the one of a pulse oximeter. The problem of finger detection in video as well as verification of the proper usage of the system is solved by using colour segmentation in each colour channel. Then, the pulse rate is evaluated based on adaptive and statistical analysis. Moreover, the blood pressure is estimated by means of artificial neural network. A set of parameters are proposed to be extracted from the photoplethysmographic signal and used as the input of the neural network. For wide representation of training data the Multiparameter Intelligent Monitoring in Intensive Care waveform dataset is used.