98 research outputs found
Retinal ganglion cell software and FPGA model implementation for object detection and tracking
This paper describes the software and FPGA
implementation of a Retinal Ganglion Cell model which detects
moving objects. It is shown how this processing, in conjunction
with a Dynamic Vision Sensor as its input, can be used to
extrapolate information about object position. Software-wise, a
system based on an array of these of RGCs has been developed in
order to obtain up to two trackers. These can track objects in a
scene, from a still observer, and get inhibited when saccadic
camera motion happens. The entire processing takes on average
1000 ns/event. A simplified version of this mechanism, with a mean
latency of 330 ns/event, at 50 MHz, has also been implemented in
a Spartan6 FPGA.European Commission FP7-ICT-600954Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
Neuromorphic Approach Sensitivity Cell Modeling and FPGA Implementation
Neuromorphic engineering takes inspiration from biology to
solve engineering problems using the organizing principles of biological
neural computation. This field has demonstrated success in sensor based
applications (vision and audition) as well in cognition and actuators.
This paper is focused on mimicking an interesting functionality of the
retina that is computed by one type of Retinal Ganglion Cell (RGC).
It is the early detection of approaching (expanding) dark objects. This
paper presents the software and hardware logic FPGA implementation
of this approach sensitivity cell. It can be used in later cognition layers as
an attention mechanism. The input of this hardware modeled cell comes
from an asynchronous spiking Dynamic Vision Sensor, which leads to an
end-to-end event based processing system. The software model has been
developed in Java, and computed with an average processing time per
event of 370 ns on a NUC embedded computer. The output firing rate
for an approaching object depends on the cell parameters that represent
the needed number of input events to reach the firing threshold. For the
hardware implementation on a Spartan6 FPGA, the processing time is
reduced to 160 ns/event with the clock running at 50 MHz.Ministerio de Economía y Competitividad TEC2016-77785-PUnión Europea FP7-ICT-60095
Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics
Taking inspiration from biology to solve engineering problems using the organizing
principles of biological neural computation is the aim of the field of neuromorphic engineering.
This field has demonstrated success in sensor based applications (vision and audition) as well as in
cognition and actuators. This paper is focused on mimicking the approaching detection functionality
of the retina that is computed by one type of Retinal Ganglion Cell (RGC) and its application to
robotics. These RGCs transmit action potentials when an expanding object is detected. In this work
we compare the software and hardware logic FPGA implementations of this approaching function
and the hardware latency when applied to robots, as an attention/reaction mechanism. The visual
input for these cells comes from an asynchronous event-driven Dynamic Vision Sensor, which leads
to an end-to-end event based processing system. The software model has been developed in Java,
and computed with an average processing time per event of 370 ns on a NUC embedded computer.
The output firing rate for an approaching object depends on the cell parameters that represent the
needed number of input events to reach the firing threshold. For the hardware implementation, on a
Spartan 6 FPGA, the processing time is reduced to 160 ns/event with the clock running at 50 MHz.
The entropy has been calculated to demonstrate that the system is not totally deterministic in response
to approaching objects because of several bioinspired characteristics. It has been measured that a
Summit XL mobile robot can react to an approaching object in 90 ms, which can be used as an
attentional mechanism. This is faster than similar event-based approaches in robotics and equivalent
to human reaction latencies to visual stimulus.Ministerio de Economía y Competitividad TEC2016-77785-PComisión Europea FP7-ICT-60095
Low Latency Event-Based Filtering and Feature Extraction for Dynamic Vision Sensors in Real-Time FPGA Applications
Dynamic Vision Sensor (DVS) pixels produce an asynchronous variable-rate address-event
output that represents brightness changes at the pixel. Since these sensors produce frame-free output, they
are ideal for real-time dynamic vision applications with real-time latency and power system constraints.
Event-based ltering algorithms have been proposed to post-process the asynchronous event output to
reduce sensor noise, extract low level features, and track objects, among others. These postprocessing
algorithms help to increase the performance and accuracy of further processing for tasks such as classi cation
using spike-based learning (ie. ConvNets), stereo vision, and visually-servoed robots, etc. This paper
presents an FPGA-based library of these postprocessing event-based algorithms with implementation details;
speci cally background activity (noise) ltering, pixel masking, object motion detection and object tracking.
The latencies of these lters on the Field Programmable Gate Array (FPGA) platform are below 300ns with
an average latency reduction of 188% (maximum of 570%) over the software versions running on a desktop
PC CPU. This open-source event-based lter IP library for FPGA has been tested on two different platforms
and scenarios using different synthesis and implementation tools for Lattice and Xilinx vendors
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Computational models of object motion detectors accelerated using FPGA technology
The detection of moving objects is a trivial task when performed by vertebrate retinas, yet a complex computer vision task. This PhD research programme has made three key contributions, namely: 1) a multi-hierarchical spiking neural network (MHSNN) architecture for detecting horizontal and vertical movements, 2) a Hybrid Sensitive Motion Detector (HSMD) algorithm for detecting object motion and 3) the Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD) , a real-time neuromorphic implementation of the HSMD algorithm.
The MHSNN is a customised 4 layers Spiking Neural Network (SNN) architecture designed to reflect the basic connectivity, similar to canonical behaviours found in the majority of vertebrate retinas (including human retinas). The architecture, was trained using images from a custom dataset generated in laboratory settings. Simulation results revealed that each cell model is sensitive to vertical and horizontal movements, with a detection error of 6.75% contrasted against the teaching signals (expected output signals) used to train the MHSNN. The experimental evaluation of the methodology shows that the MH SNN was not scalable because of the overall number of neurons and synapses which lead to the development of the HSMD.
The HSMD algorithm enhanced an existing Dynamic Background subtraction (DBS) algorithm using a customised 3-layer SNN. The customised 3-layer SNN was used to stabilise the foreground information of moving objects in the scene, which improves the object motion detection. The algorithm was compared against existing background subtraction approaches, available on the Open Computer Vision (OpenCV) library, specifically on the 2012 Change Detection (CDnet2012) and the 2014 Change Detection (CDnet2014) benchmark datasets. The accuracy results show that the HSMD was ranked overall first and performed better than all the other benchmarked algorithms on four of the categories, across all eight test metrics. Furthermore, the HSMD is the first to use an SNN to enhance the existing dynamic background subtraction algorithm without a substantial degradation of the frame rate, being capable of processing images 720 × 480 at 13.82 Frames Per Second (fps) (CDnet2014) and 720 × 480 at 13.92 fps (CDnet2012) on a High Performance computer (96 cores and 756 GB of RAM). Although the HSMD analysis shows good Percentage of Correct Classifications (PCC) on the CDnet2012 and CDnet2014, it was identified that the 3-layer customised SNN was the bottleneck, in terms of speed, and could be improved using dedicated hardware.
The NeuroHSMD is thus an adaptation of the HSMD algorithm whereby the SNN component has been fully implemented on dedicated hardware [Terasic DE10-pro Field-Programmable Gate Array (FPGA) board]. Open Computer Language (OpenCL) was used to simplify the FPGA design flow and allow the code portability to other devices such as FPGA and Graphical Processing Unit (GPU). The NeuroHSMD was also tested against the CDnet2012 and CDnet2014 datasets with an acceleration of 82% over the HSMD algorithm, being capable of processing 720 × 480 images at 28.06 fps (CDnet2012) and 28.71 fps (CDnet2014)
The role of direction-selective visual interneurons T4 and T5 in Drosophila orientation behavior
In order to safely move through the environment, visually-guided animals
use several types of visual cues for orientation. Optic flow provides faithful
information about ego-motion and can thus be used to maintain a straight
course. Additionally, local motion cues or landmarks indicate potentially
interesting targets or signal danger, triggering approach or avoidance, respectively.
The visual system must reliably and quickly evaluate these cues
and integrate this information in order to orchestrate behavior. The underlying
neuronal computations for this remain largely inaccessible in higher
organisms, such as in humans, but can be studied experimentally in more
simple model species. The fly Drosophila, for example, heavily relies on
such visual cues during its impressive flight maneuvers. Additionally, it is
genetically and physiologically accessible. Hence, it can be regarded as an
ideal model organism for exploring neuronal computations during visual
processing.
In my PhD studies, I have designed and built several autonomous virtual
reality setups to precisely measure visual behavior of walking flies. The
setups run in open-loop and in closed-loop configuration. In an open-loop
experiment, the visual stimulus is clearly defined and does not depend on
the behavioral response. Hence, it allows mapping of how specific features
of simple visual stimuli are translated into behavioral output, which can
guide the creation of computational models of visual processing. In closedloop
experiments, the behavioral response is fed back onto the visual stimulus,
which permits characterization of the behavior under more realistic
conditions and, thus, allows for testing of the predictive power of the computational
models.
In addition, Drosophila’s genetic toolbox provides various strategies for
targeting and silencing specific neuron types, which helps identify which
cells are needed for a specific behavior. We have focused on visual interneuron
types T4 and T5 and assessed their role in visual orientation behavior.
These neurons build up a retinotopic array and cover the whole visual field
of the fly. They constitute major output elements from the medulla and have
long been speculated to be involved in motion processing.
This cumulative thesis consists of three published studies: In the first
study, we silenced both T4 and T5 neurons together and found that such flies
were completely blind to any kind of motion. In particular, these flies could
not perform an optomotor response anymore, which means that they lost
their normally innate following responses to motion of large-field moving
patterns. This was an important finding as it ruled out the contribution
of another system for motion vision-based behaviors. However, these flies
were still able to fixate a black bar. We could show that this behavior is
mediated by a T4/T5-independent flicker detection circuitry which exists in
parallel to the motion system.
In the second study, T4 and T5 neurons were characterized via twophoton
imaging, revealing that these cells are directionally selective and
have very similar temporal and orientation tuning properties to directionselective
neurons in the lobula plate. T4 and T5 cells responded in a
contrast polarity-specific manner: T4 neurons responded selectively to ON
edge motion while T5 neurons responded only to OFF edge motion. When
we blocked T4 neurons, behavioral responses to moving ON edges were
more impaired than those to moving OFF edges and the opposite was true
for the T5 block. Hence, these findings confirmed that the contrast polarityspecific
visual motion pathways, which start at the level of L1 (ON) and L2
(OFF), are maintained within the medulla and that motion information is
computed twice independently within each of these pathways.
Finally, in the third study, we used the virtual reality setups to probe the
performance of an artificial microcircuit. The system was equipped with a
camera and spherical fisheye lens. Images were processed by an array of
Reichardt detectors whose outputs were integrated in a similar way to what
is found in the lobula plate of flies. We provided the system with several rotating
natural environments and found that the fly-inspired artificial system
could accurately predict the axes of rotation
FPGA design and implementation of a framework for optogenetic retinal prosthesis
PhD ThesisThere are 285 million people worldwide with a visual impairment, 39 million of whom are completely blind and 246 million partially blind, known as low vision patients. In the UK and other developed countries of the west, retinal dystrophy diseases represent the primary cause of blindness, especially Age Related Macular Degeneration (AMD), diabetic retinopathy and Retinitis Pigmentosa (RP).
There are various treatments and aids that can help these visual disorders, such as low vision aids, gene therapy and retinal prosthesis. Retinal prostheses consist of four main stages: the input stage (Image Acquisition), the high level processing stage (Image preparation and retinal encoding), low level processing stage (Stimulation controller) and the output stage (Image displaying on the opto-electronic micro-LEDs array). Up to now, a limited number of full hardware implementations have been available for retinal prosthesis.
In this work, a photonic stimulation controller was designed and implemented. The main rule of this controller is to enhance framework results in terms of power and time. It involves, first, an even power distributor, which was used to evenly distribute the power through image sub-frames, to avoid a large surge of power, especially with large arrays. Therefore, the overall framework power results are improved. Second, a pulse encoder was used to select different modes of operation for the opto-electronic micro-LEDs array, and as a result of this the overall time for the framework was improved. The implementation is completed using reconfigurable hardware devices, i.e. Field Programmable Gate Arrays (FPGAs), to achieve high performance at an economical price. Moreover, this FPGA-based framework for an optogenetic retinal prosthesis aims to control the opto-electronic micro-LED array in an efficient way, and to interface and link between the opto-electronic micro-LED array hardware architecture and the previously developed high level retinal prosthesis image processing algorithms.University of Jorda
Low-power dynamic object detection and classification with freely moving event cameras
We present the first purely event-based, energy-efficient approach for dynamic object detection and categorization with a freely moving event camera. Compared to traditional cameras, event-based object recognition systems are considerably behind in terms of accuracy and algorithmic maturity. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional object representation when hardware resources are limited to implement PCA. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance compared to state-of-the-art algorithms. Additionally, we verified the real-time FPGA performance of the proposed object detection method, trained with limited data as opposed to deep learning methods, under a closed-loop aerial vehicle flight mode. We also compare the proposed object categorization framework to pre-trained convolutional neural networks using transfer learning and highlight the drawbacks of using frame-based sensors under dynamic camera motion. Finally, we provide critical insights about the feature extraction method and the classification parameters on the system performance, which aids in understanding the framework to suit various low-power (less than a few watts) application scenarios
Topics in Adaptive Optics
Advances in adaptive optics technology and applications move forward at a rapid pace. The basic idea of wavefront compensation in real-time has been around since the mid 1970s. The first widely used application of adaptive optics was for compensating atmospheric turbulence effects in astronomical imaging and laser beam propagation. While some topics have been researched and reported for years, even decades, new applications and advances in the supporting technologies occur almost daily. This book brings together 11 original chapters related to adaptive optics, written by an international group of invited authors. Topics include atmospheric turbulence characterization, astronomy with large telescopes, image post-processing, high power laser distortion compensation, adaptive optics and the human eye, wavefront sensors, and deformable mirrors
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