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A biologically inspired spiking model of visual processing for image feature detection
To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images
Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision
To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
A geometric model of multi-scale orientation preference maps via Gabor functions
In this paper we present a new model for the generation of orientation
preference maps in the primary visual cortex (V1), considering both orientation
and scale features. First we undertake to model the functional architecture of
V1 by interpreting it as a principal fiber bundle over the 2-dimensional
retinal plane by introducing intrinsic variables orientation and scale. The
intrinsic variables constitute a fiber on each point of the retinal plane and
the set of receptive profiles of simple cells is located on the fiber. Each
receptive profile on the fiber is mathematically interpreted as a rotated Gabor
function derived from an uncertainty principle. The visual stimulus is lifted
in a 4-dimensional space, characterized by coordinate variables, position,
orientation and scale, through a linear filtering of the stimulus with Gabor
functions. Orientation preference maps are then obtained by mapping the
orientation value found from the lifting of a noise stimulus onto the
2-dimensional retinal plane. This corresponds to a Bargmann transform in the
reducible representation of the group. A
comparison will be provided with a previous model based on the Bargman
transform in the irreducible representation of the group,
outlining that the new model is more physiologically motivated. Then we present
simulation results related to the construction of the orientation preference
map by using Gabor filters with different scales and compare those results to
the relevant neurophysiological findings in the literature
A bio-inspired image coder with temporal scalability
We present a novel bio-inspired and dynamic coding scheme for static images.
Our coder aims at reproducing the main steps of the visual stimulus processing
in the mammalian retina taking into account its time behavior. The main novelty
of this work is to show how to exploit the time behavior of the retina cells to
ensure, in a simple way, scalability and bit allocation. To do so, our main
source of inspiration will be the biologically plausible retina model called
Virtual Retina. Following a similar structure, our model has two stages. The
first stage is an image transform which is performed by the outer layers in the
retina. Here it is modelled by filtering the image with a bank of difference of
Gaussians with time-delays. The second stage is a time-dependent
analog-to-digital conversion which is performed by the inner layers in the
retina. Thanks to its conception, our coder enables scalability and bit
allocation across time. Also, our decoded images do not show annoying artefacts
such as ringing and block effects. As a whole, this article shows how to
capture the main properties of a biological system, here the retina, in order
to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS
2011
Egocentric Perception using a Biologically Inspired Software Retina Integrated with a Deep CNN
We presented the concept of of a software retina, capable
of significant visual data reduction in combination with
scale and rotation invariance, for applications in egocentric
and robot vision at the first EPIC workshop in Amsterdam
[9]. Our method is based on the mammalian retino-cortical
transform: a mapping between a pseudo-randomly tessellated
retina model (used to sample an input image) and a
CNN. The aim of this first pilot study is to demonstrate a
functional retina-integrated CNN implementation and this
produced the following results: a network using the full
retino-cortical transform yielded an F1 score of 0.80 on a
test set during a 4-way classification task, while an identical
network not using the proposed method yielded an F1
score of 0.86 on the same task. On a 40K node retina the
method reduced the visual data byeĂ—7, the input data to the
CNN by 40% and the number of CNN training epochs by
36%. These results demonstrate the viability of our method
and hint at the potential of exploiting functional traits of
natural vision systems in CNNs. In addition, to the above
study, we present further recent developments in porting
the retina to an Apple iPhone, an implementation in CUDA
C for NVIDIA GPU platforms and extensions of the retina
model we have adopted
Handwritten digit recognition by bio-inspired hierarchical networks
The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%
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