Abstract
In this paper, we propose a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) that has built-in mask circuits to selectively capture either edge-detection images or normal 8-bit images for low-power computer vision applications. To detect the edges of images in the CIS, neighboring column data are compared in in-column memories after column-parallel analog-to-digital conversion with the proposed mask. The proposed built-in mask circuits are implemented in the CIS without a complex image signal processer to obtain edge images with high speed and low power consumption. According to the measurement results, edge images were successfully obtained with a maximum frame rate of 60 fps. A prototype sensor with 1920 × 1440 resolution was fabricated with a 90-nm 1-poly 5-metal CIS process. The area of the 4-shared 4T-active pixel sensor was 1.4 × 1.4 µm2, and the chip size was 5.15 × 5.15 mm2. The total power consumption was 9.4 mW at 60 fps with supply voltages of 3.3 V (analog), 2.8 V (pixel), and 1.2 V (digital).
Highlights
In recent years, complementary metal-oxide-semiconductor (CMOS) image sensors (CIS) with computing functions have received considerable attention for use in a wide variety of applications, such as medical imaging, automotive safety, surveillance, and face detection for auto-focus in digital cameras [1,2,3,4,5,6,7]
The full chip was measured by using a field-programmable gate array (FPGA) board to produce
The full chip wassignals measured by operation using a field-programmable gate array (FPGA)block, boardand to produce the required control for the of the clocked comparator, memory so on
Summary
Complementary metal-oxide-semiconductor (CMOS) image sensors (CIS) with computing functions (called computer vision sensors) have received considerable attention for use in a wide variety of applications, such as medical imaging, automotive safety, surveillance, and face detection for auto-focus in digital cameras [1,2,3,4,5,6,7]. To detect objects using CV computation, two types of approaches have been developed: (1) asynchronous and event-based sensors, such as dynamic vision sensors (DVS), and (2) frame-based CIS with CV computation hardware. Event-based sensors, such as DVS [12], asynchronous time-based image sensors [10], and dynamic and active-pixel vision sensors (DAVIS) [11], have been developed for low-latency CV computation. Owing to the mature development of APS and ADC, high-performance and high-resolution frame-based CIS can be used for further CV applications, such as long-distance object detection for surveillance sensing systems. For power-efficient CV operations, by taking advantage of mature APS and ADC, simple CV computation hardware for edge detection can be implemented in column-parallel peripheral circuitry based on a frame-based CIS structure.
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