Fundamentals of digital image processing
Fundamentals of digital image processing
Analog VLSI and neural systems
Analog VLSI and neural systems
The dynamic retina: contrast and motion detection for active vision
International Journal of Computer Vision
Compact Integrated Motion Sensor With Three-Pixel Interaction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Perception Using Analog VLSI
Analog Integrated Circuits and Signal Processing
Vision Chips
Visual Sensor with Resolution Enhancement by Mechanical Vibrations
Autonomous Robots
Visual Sensor with Resolution Enhancement by Mechanical Vibrations
ARVLSI '01 Proceedings of the 2001 Conference on Advanced Research in VLSI
The Resonant Retina: Exploiting Vibration Noise to Optimally Detect Edges in an Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
A CMOS Imager with On-Chip Temporal Filtering for Motion Pre-Processing
Analog Integrated Circuits and Signal Processing
Reverse Engineering the Human Vision System: A Possible Explanation for the Role of Microsaccades
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Hexagonal Image Processing: A Practical Approach (Advances in Pattern Recognition)
Hexagonal Image Processing: A Practical Approach (Advances in Pattern Recognition)
A low-power integrated smart sensor with on-chip real-time image processing capabilities
EURASIP Journal on Applied Signal Processing
Silicon retina with correlation-based, velocity-tuned pixels
IEEE Transactions on Neural Networks
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We present a new biologically inspired imaging framework which uses sensor motion in a constructive way, contrary to most current imaging techniques. In addition to measuring light intensity, the spatial derivatives of the scene are estimated by measuring the temporal derivatives of the output of the vibrating photoreceptors. This is inspired from the fixational eye movements of the human visual system. When motion is known, these measured spatial derivatives can be used to improve the quality of the intensity image, as well as for object and pattern recognition tasks. This image reconstruction is based on integrating the derivatives while using the measured intensities as integration constants. The results of the imaging framework and of the reconstruction step are high quality images of the light intensities and of its spatial derivatives, which is relevant for further scene understanding. The advantages and the potential of this new imaging framework are shown in many simulations in this paper.