A Low-Power Integrated Smart Sensor with on-Chip Real-Time Image Processing Capabilities
- PDF / 1,086,499 Bytes
- 9 Pages / 600 x 792 pts Page_size
- 41 Downloads / 149 Views
A Low-Power Integrated Smart Sensor with on-Chip Real-Time Image Processing Capabilities Massimo Barbaro Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy Email: [email protected]
Luigi Raffo Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy Email: [email protected] Received 16 September 2003; Revised 13 May 2004 A low-power, CMOS retina with real-time, pixel-level processing capabilities is presented. Features extraction and edge enhancement are implemented with fully programmable 1D Gabor convolutions. An equivalent computation rate of 3 GOPs is obtained at the cost of very low-power consumption (1.5 µW per pixel), providing real-time performances (50 microseconds for overall computation, 0.5 GOPs/mW). Experimental results from the first realized prototype show a very good matching between measures and expected outputs. Keywords and phrases: smart sensors, bioinspired circuits, real-time image processing.
1.
INTRODUCTION
Real-time, low-power, low-cost, and portable vision systems apt to be adopted as an optical front end on mobile and autonomous systems are more and more demanded for by the consumer electronic market. Specific vision tasks, ranging from segmentation to recognition (characters, faces, postures, obstacles) and classification, are required in several different applications which are emerging from the needs of the automotive, mobile surveillance market. In the automotive field, for example, an increasing number of electronic devices are being introduced in the car to improve safety and driveability. Sensors will be needed for applications such as drivesupport and safety measures. In the mobile market, more and more capabilities (such as OCR, face recognition and so on) will be built in the 3G cell phones, which are already being equipped with digital cameras. Surveillance systems represent an exploding market with plenty of complex image processing applications, such as biometric identification in airports, to cite only one. Promising fields of application are also medical assistance and, of course, robotics. These applications (requiring estimation of motion-indepth, computation of time-to-contact, target tracking, object recognition, and other high-level image processing tasks) are examples of perceptive tasks, or problems conveying the necessity of taking a quick decision on the basis of a sensory input (visual, in this case). The traditional approach to image
processing, based on acquisition on a CCD camera and software processing on a digital platform (PC, DSP, or ASIC), has proven to be scarcely fit to accomplish perceptive tasks. In fact, even if a wide and reliable collection of software algorithms is available and computational capabilities of digital platforms are constantly evolving and improving, nevertheless, it seems that the constraints of real time, low cost, low power and portability can be hardly contemporaneously met with the classic approach. Nee
Data Loading...
