2,598 research outputs found
Streaming an image through the eye: The retina seen as a dithered scalable image coder
We propose the design of an original scalable image coder/decoder that is
inspired from the mammalians retina. Our coder accounts for the time-dependent
and also nondeterministic behavior of the actual retina. The present work
brings two main contributions: As a first step, (i) we design a deterministic
image coder mimicking most of the retinal processing stages and then (ii) we
introduce a retinal noise in the coding process, that we model here as a dither
signal, to gain interesting perceptual features. Regarding our first
contribution, our main source of inspiration will be the biologically plausible
model of the retina called Virtual Retina. The main novelty of this coder is to
show that the time-dependent behavior of the retina cells could ensure, in an
implicit way, scalability and bit allocation. Regarding our second
contribution, we reconsider the inner layers of the retina. We emit a possible
interpretation for the non-determinism observed by neurophysiologists in their
output. For this sake, we model the retinal noise that occurs in these layers
by a dither signal. The dithering process that we propose adds several
interesting features to our image coder. The dither noise whitens the
reconstruction error and decorrelates it from the input stimuli. Furthermore,
integrating the dither noise in our coder allows a faster recognition of the
fine details of the image during the decoding process. Our present paper goal
is twofold. First, we aim at mimicking as closely as possible the retina for
the design of a novel image coder while keeping encouraging performances.
Second, we bring a new insight concerning the non-deterministic behavior of the
retina.Comment: arXiv admin note: substantial text overlap with arXiv:1104.155
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
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
Information recovery from rank-order encoded images
The time to detection of a visual stimulus by the primate eye is recorded at
100 ā 150ms. This near instantaneous recognition is in spite of the considerable
processing required by the several stages of the visual pathway to recognise and
react to a visual scene. How this is achieved is still a matter of speculation.
Rank-order codes have been proposed as a means of encoding by the primate
eye in the rapid transmission of the initial burst of information from the sensory
neurons to the brain. We study the efficiency of rank-order codes in encoding
perceptually-important information in an image. VanRullen and Thorpe built a
model of the ganglion cell layers of the retina to simulate and study the viability
of rank-order as a means of encoding by retinal neurons. We validate their model
and quantify the information retrieved from rank-order encoded images in terms
of the visually-important information recovered. Towards this goal, we apply
the āperceptual information preservation algorithmā, proposed by Petrovic and
Xydeas after slight modification. We observe a low information recovery due
to losses suffered during the rank-order encoding and decoding processes. We
propose to minimise these losses to recover maximum information in minimum
time from rank-order encoded images. We first maximise information recovery by
using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder
decoding. We then apply the biological principle of lateral inhibition to
minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap
Correction algorithm. To test the perfomance of rank-order codes in
a biologically realistic model, we design and simulate a model of the foveal-pit
ganglion cells of the retina keeping close to biological parameters. We use this
as a rank-order encoder and analyse its performance relative to VanRullen and
Thorpeās retinal model
Visual motion processing and human tracking behavior
The accurate visual tracking of a moving object is a human fundamental skill
that allows to reduce the relative slip and instability of the object's image
on the retina, thus granting a stable, high-quality vision. In order to
optimize tracking performance across time, a quick estimate of the object's
global motion properties needs to be fed to the oculomotor system and
dynamically updated. Concurrently, performance can be greatly improved in terms
of latency and accuracy by taking into account predictive cues, especially
under variable conditions of visibility and in presence of ambiguous retinal
information. Here, we review several recent studies focusing on the integration
of retinal and extra-retinal information for the control of human smooth
pursuit.By dynamically probing the tracking performance with well established
paradigms in the visual perception and oculomotor literature we provide the
basis to test theoretical hypotheses within the framework of dynamic
probabilistic inference. We will in particular present the applications of
these results in light of state-of-the-art computer vision algorithms
Bio-inspired vision-based leader-follower formation flying in the presence of delays
Flocking starlings at dusk are known for the mesmerizing and intricate shapes they generate, as well as how fluid these shapes change. They seem to do this effortlessly. Real-life vision-based flocking has not been achieved in micro-UAVs (micro Unmanned Aerial Vehicles) to date. Towards this goal, we make three contributions in this paper: (i) we used a computational approach to develop a bio-inspired architecture for vision-based Leader-Follower formation flying on two micro-UAVs. We believe that the minimal computational cost of the resulting algorithm makes it suitable for object detection and tracking during high-speed flocking; (ii) we show that provided delays in the control loop of a micro-UAV are below a critical value, Kalman filter-based estimation algorithms are not required to achieve Leader-Follower formation flying; (iii) unlike previous approaches, we do not use external observers, such as GPS signals or synchronized communication with flock members. These three contributions could be useful in achieving vision-based flocking in GPS-denied environments on computationally-limited agents
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
Second-order neural core for bioinspired focal-plane dynamic image processing in CMOS
Based on studies of the mammalian retina, a bioinspired model for mixed-signal array processing has been implemented on silicon. This model mimics the way in which images are processed at the front-end of natural visual pathways, by means of programmable complex spatio-temporal dynamic. When embedded into a focal-plane processing chip, such a model allows for online parallel filtering of the captured image; the outcome of such processing can be used to develop control feedback actions to adapt the response of photoreceptors to local image features. Beyond simple resistive grid filtering, it is possible to program other spatio-temporal processing operators into the model core, such as nonlinear and anisotropic diffusion, among others. This paper presents analog and mixed-signal very large-scale integration building blocks to implement this model, and illustrates their operation through experimental results taken from a prototype chip fabricated in a 0.5-Ī¼m CMOS technology.European Union IST 2001 38097Ministerio de Ciencia y TecnologĆa TIC 2003 09817 C02 01Office of Naval Research (USA) N00014021088
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