Intelligent Image Processing and Signal Processing for wearable computing and AR (augmediated reality)

点击翻译按钮获取中文摘要

With ubiquitous mobile/portable imaging and computing now a reality, and wearable computing (e.g. Digital Eye Glass) just becoming a reality, we are entering an era in which our everyday lives are being or will soon be mediated by vision-based computers.**Many of us already carry a camera and computing device (smartphone or cameraphone) most of the time. These devices will soon take the form of Digital Eye Glass to help many of us see better, and improve the quality of our lives. This technology will be especially welcome to the growing population of older individuals with failing eyesight.**Moreover, as technologies become more personal, the Digital Eye Glass will help us remember names and faces (e.g. the wearable face recognizer), find our way (digital maps overlayed with Augmediated Reality), and provide us with improved safety (e.g. personal security), health (sensing, etc.) and well-being.**From my long-standing personal experience (more than 35 years of inventing, designing, building, and experimenting with wearable computing and Digital Eye Glass since my childhood -- first as an amateur scientist in my youth, and later in my professional life), I have helped this industry evolve.**Much of the industry is addressing immediate business interests, but there is a need for research on the foundational aspects of Intelligent Image Processing specifically related to personal imaging devices (small portable or wearable camera systems).**In particular, a core issue that remains is getting cameras to "see" as well as the human eye does. Already the spatial resolution of cameras has increased from thousands of pixels to megapixels, but image quality (e.g. dynamic range) at each pixel has not kept pace.**The first digital camera was invented (by Steven Sasson of Kodak) in 1975, and had 10,000 pixels (based on a Fairchild 100*100 pixel sensor), and 4 bits-per pixel per channel (1 channel, i.e. greyscale).**Today, mobile and portable camera phones are up to 41 Megapixels (e.g. Nokia PureView 808), and some cameras are in the gigapixel range, i.e. spatial resolutions that are more than 100,000 times what they were in 1975. Thus many cameras can "see" the detail (e.g. "read" small print on a newspaper) thousands of times better now than then.**But they still cannot match the human eye for dynamic range (i.e. being able to simultaneously sense in low-light and really bright light).**Remarkably, the bit depth of modern cameras is typically only twice what the world's first digital camera was 38 years ago (8 bits compared to 4 bits).**Recently I have addressed this problem by inventing and steadily improving something called HDR (High Dynamic Range) Imaging.**Robertson et al. write:**"The first report of digitally combining multiple pictures of the same scene to improve dynamic range appears to be Mann [Mann 1993]"*-- [Estimation-theoretic approach to dynamic range enhancement using multiple exposures, JEI 12(2)].***HDR is now used by many commercially manufactured cameras, including Apple's iPhone (which implements HDR internally), as well as many surveillance cameras and other specialized imaging devices.**My work will involve basic research in creation of an Intelligent Image Procesing chip for widespread use. The work involves development of new branches of mathematics and engineering, combining comparametric equations, superposimetric equations, functional equations, and quantum field theory, with FPGA (Field Programmable Gate Array) architectures. Another goal is to create Actional Systems (a new kind of kinematics) for circuit modeling. This fundamental work will be of great use to other scientists and engineers who work with image processing, Digital Eye Glass, and AR (Augmediated Reality).