303 research outputs found
Low-Power Tracking Image Sensor Based on Biological Models of Attention
This paper presents implementation of a low-power tracking CMOS image sensor based on biological
models of attention. The presented imager allows tracking of up to N salient targets in the field of view. Employing
"smart" image sensor architecture, where all image processing is implemented on the sensor focal plane, the
proposed imager allows reduction of the amount of data transmitted from the sensor array to external processing
units and thus provides real time operation. The imager operation and architecture are based on the models
taken from biological systems, where data sensed by many millions of receptors should be transmitted and
processed in real time. The imager architecture is optimized to achieve low-power dissipation both in acquisition
and tracking modes of operation. The tracking concept is presented, the system architecture is shown and the
circuits description is discussed
Selective Change Driven Imaging: A Biomimetic Visual Sensing Strategy
Selective Change Driven (SCD) Vision is a biologically inspired strategy for acquiring, transmitting and processing images that significantly speeds up image sensing. SCD vision is based on a new CMOS image sensor which delivers, ordered by the absolute magnitude of its change, the pixels that have changed after the last time they were read out. Moreover, the traditional full frame processing hardware and programming methodology has to be changed, as a part of this biomimetic approach, to a new processing paradigm based on pixel processing in a data flow manner, instead of full frame image processing
Image Sensors in Security and Medical Applications
This paper briefly reviews CMOS image sensor technology and its utilization in security and medical
applications. The role and future trends of image sensors in each of the applications are discussed. To provide
the reader deeper understanding of the technology aspects the paper concentrates on the selected applications
such as surveillance, biometrics, capsule endoscopy and artificial retina. The reasons for concentrating on these
applications are due to their importance in our daily life and because they present leading-edge applications for
imaging systems research and development. In addition, review of image sensors implementation in these
applications allows the reader to investigate image sensor technology from the technical and from other views
as well
Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors
Edge devices equipped with computer vision must deal with vast amounts of
sensory data with limited computing resources. Hence, researchers have been
exploring different energy-efficient solutions such as near-sensor processing,
in-sensor processing, and in-pixel processing, bringing the computation closer
to the sensor. In particular, in-pixel processing embeds the computation
capabilities inside the pixel array and achieves high energy efficiency by
generating low-level features instead of the raw data stream from CMOS image
sensors. Many different in-pixel processing techniques and approaches have been
demonstrated on conventional frame-based CMOS imagers, however, the
processing-in-pixel approach for neuromorphic vision sensors has not been
explored so far. In this work, we for the first time, propose an asynchronous
non-von-Neumann analog processing-in-pixel paradigm to perform convolution
operations by integrating in-situ multi-bit multi-channel convolution inside
the pixel array performing analog multiply and accumulate (MAC) operations that
consume significantly less energy than their digital MAC alternative. To make
this approach viable, we incorporate the circuit's non-ideality, leakage, and
process variations into a novel hardware-algorithm co-design framework that
leverages extensive HSpice simulations of our proposed circuit using the GF22nm
FD-SOI technology node. We verified our framework on state-of-the-art
neuromorphic vision sensor datasets and show that our solution consumes ~2x
lower backend-processor energy while maintaining almost similar front-end
(sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art
while maintaining a high test accuracy of 88.36%.Comment: 17 pages, 11 figures, 2 table
Neural Network Methods for Radiation Detectors and Imaging
Recent advances in image data processing through machine learning and
especially deep neural networks (DNNs) allow for new optimization and
performance-enhancement schemes for radiation detectors and imaging hardware
through data-endowed artificial intelligence. We give an overview of data
generation at photon sources, deep learning-based methods for image processing
tasks, and hardware solutions for deep learning acceleration. Most existing
deep learning approaches are trained offline, typically using large amounts of
computational resources. However, once trained, DNNs can achieve fast inference
speeds and can be deployed to edge devices. A new trend is edge computing with
less energy consumption (hundreds of watts or less) and real-time analysis
potential. While popularly used for edge computing, electronic-based hardware
accelerators ranging from general purpose processors such as central processing
units (CPUs) to application-specific integrated circuits (ASICs) are constantly
reaching performance limits in latency, energy consumption, and other physical
constraints. These limits give rise to next-generation analog neuromorhpic
hardware platforms, such as optical neural networks (ONNs), for high parallel,
low latency, and low energy computing to boost deep learning acceleration
Bottleneck Problem Solution using Biological Models of Attention in High Resolution Tracking Sensors
Every high resolution imaging system suffers from the bottleneck problem. This problem relates to the
huge amount of data transmission from the sensor array to a digital signal processing (DSP) and to bottleneck in
performance, caused by the requirement to process a large amount of information in parallel. The same problem
exists in biological vision systems, where the information, sensed by many millions of receptors should be
transmitted and processed in real time. Models, describing the bottleneck problem solutions in biological systems
fall in the field of visual attention. This paper presents the bottleneck problem existing in imagers used for real
time salient target tracking and proposes a simple solution by employing models of attention, found in biological
systems. The bottleneck problem in imaging systems is presented, the existing models of visual attention are
discussed and the architecture of the proposed imager is shown
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