453 research outputs found
Parallel software implementation of recursive multidimensional digital filters for point-target detection in cluttered infrared scenes
A technique for the enhancement of point targets in clutter is described. The
local 3-D spectrum at each pixel is estimated recursively. An optical
flow-field for the textured background is then generated using the 3-D
autocorrelation function and the local velocity estimates are used to apply
high-pass velocity-selective spatiotemporal filters, with finite impulse
responses (FIRs), to subtract the background clutter signal, leaving the
foreground target signal, plus noise. Parallel software implementations using a
multicore central processing unit (CPU) and a graphical processing unit (GPU)
are investigated.Comment: To appear in Proc. 2015 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP). Added header and DO
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Methods and Approaches for Real-Time Hierarchical Motion Detection
The recent work on perception and measurement of visual motion has consistently advocated the use of a hierarchical representation and analysis. In most of the practical applications of motion perception it is absolutely necessary to be able to construct and process these hierarchical image representations in real-time. First, we discuss a simple scheme for coarse motion detection that highlights the capabilities of the PIPE image processor, showing its ability to work in both the spatial and temporal dimensions in real-time. Secondly, we show how this architecture can be used to build pyramid structures useful for motion detection, again emphasizing the real-time nature of the computations. Using the PIPE architecture, we have constructed a Pyramid of Oriented Edges (POE) which is a logical extension of Burt's pyramid and also a version of Mallat's pyramid. The results of these algorithms are available on a video tape to highlight their real-time performance on moving images. Third, we propose a new method using PIPE that will allow dense optic flow computation and which relates the intensity-correlation and spatio-temporal frequency based methods of determining optic flow
Time-causal and time-recursive spatio-temporal receptive fields
We present an improved model and theory for time-causal and time-recursive
spatio-temporal receptive fields, based on a combination of Gaussian receptive
fields over the spatial domain and first-order integrators or equivalently
truncated exponential filters coupled in cascade over the temporal domain.
Compared to previous spatio-temporal scale-space formulations in terms of
non-enhancement of local extrema or scale invariance, these receptive fields
are based on different scale-space axiomatics over time by ensuring
non-creation of new local extrema or zero-crossings with increasing temporal
scale. Specifically, extensions are presented about (i) parameterizing the
intermediate temporal scale levels, (ii) analysing the resulting temporal
dynamics, (iii) transferring the theory to a discrete implementation, (iv)
computing scale-normalized spatio-temporal derivative expressions for
spatio-temporal feature detection and (v) computational modelling of receptive
fields in the lateral geniculate nucleus (LGN) and the primary visual cortex
(V1) in biological vision.
We show that by distributing the intermediate temporal scale levels according
to a logarithmic distribution, we obtain much faster temporal response
properties (shorter temporal delays) compared to a uniform distribution.
Specifically, these kernels converge very rapidly to a limit kernel possessing
true self-similar scale-invariant properties over temporal scales, thereby
allowing for true scale invariance over variations in the temporal scale,
although the underlying temporal scale-space representation is based on a
discretized temporal scale parameter.
We show how scale-normalized temporal derivatives can be defined for these
time-causal scale-space kernels and how the composed theory can be used for
computing basic types of scale-normalized spatio-temporal derivative
expressions in a computationally efficient manner.Comment: 39 pages, 12 figures, 5 tables in Journal of Mathematical Imaging and
Vision, published online Dec 201
A polar prediction model for learning to represent visual transformations
All organisms make temporal predictions, and their evolutionary fitness level
depends on the accuracy of these predictions. In the context of visual
perception, the motions of both the observer and objects in the scene structure
the dynamics of sensory signals, allowing for partial prediction of future
signals based on past ones. Here, we propose a self-supervised
representation-learning framework that extracts and exploits the regularities
of natural videos to compute accurate predictions. We motivate the polar
architecture by appealing to the Fourier shift theorem and its group-theoretic
generalization, and we optimize its parameters on next-frame prediction.
Through controlled experiments, we demonstrate that this approach can discover
the representation of simple transformation groups acting in data. When trained
on natural video datasets, our framework achieves better prediction performance
than traditional motion compensation and rivals conventional deep networks,
while maintaining interpretability and speed. Furthermore, the polar
computations can be restructured into components resembling normalized simple
and direction-selective complex cell models of primate V1 neurons. Thus, polar
prediction offers a principled framework for understanding how the visual
system represents sensory inputs in a form that simplifies temporal prediction
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
The Role of Early Recurrence in Improving Visual Representations
This dissertation proposes a computational model of early vision with recurrence, termed as early recurrence. The idea is motivated from the research of the primate vision. Specifically, the proposed model relies on the following four observations. 1) The primate visual system includes two main visual pathways: the dorsal pathway and the ventral pathway; 2) The two pathways respond to different visual features; 3) The neurons of the dorsal pathway conduct visual information faster than that of the neurons of the ventral pathway; 4) There are lower-level feedback connections from the dorsal pathway to the ventral pathway. As such, the primate visual system may implement a recurrent mechanism to improve visual representations of the ventral pathway.
Our work starts from a comprehensive review of the literature, based on which a conceptualization of early recurrence is proposed. Early recurrence manifests itself as a form of surround suppression. We propose that early recurrence is capable of refining the ventral processing using results of the dorsal processing.
Our work further defines a set of computational components to formalize early recurrence. Although we do not intend to model the true nature of biology, to verify that the proposed computation is biologically consistent, we have applied the model to simulate a neurophysiological experiment of a bar-and-checkerboard and a psychological experiment involving a moving contour illusion. Simulation results indicated that the proposed computation behaviourally reproduces the original observations.
The ultimate goal of this work is to investigate whether the proposal is capable of improving computer vision applications. To do this, we have applied the model to a variety of applications, including visual saliency and contour detection. Based on comparisons against the state-of-the-art, we conclude that the proposed model of early recurrence sheds light on a generally applicable yet lightweight approach to boost real-life application performance
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