Fast space-variant elliptical filtering using box splines

The efficient realization of linear space-variant (non-convolution) filters is a challenging computational problem in image processing. In this paper, we demonstrate that it is possible to filter an image with a Gaussian-like elliptic window of varying size, elongation and orientation using a fixed number of computations per pixel. The associated algorithm, which is based on a family of smooth compactly supported piecewise polynomials, the radially-uniform box splines, is realized using pre-integration and local finite-differences. The radially-uniform box splines are constructed through the repeated convolution of a fixed number of box distributions, which have been suitably scaled and distributed radially in an uniform fashion. The attractive features of these box splines are their asymptotic behavior, their simple covariance structure, and their quasi-separability. They converge to Gaussians with the increase of their order, and are used to approximate anisotropic Gaussians of varying covariance simply by controlling the scales of the constituent box distributions. Based on the second feature, we develop a technique for continuously controlling the size, elongation and orientation of these Gaussian-like functions. Finally, the quasi-separable structure, along with a certain scaling property of box distributions, is used to efficiently realize the associated space-variant elliptical filtering, which requires O(1) computations per pixel irrespective of the shape and size of the filter.Comment: 12 figures; IEEE Transactions on Image Processing, vol. 19, 201

Virtual Retina : a biological retina model and simulator, with contrast gain control

A detailed retina model is proposed, that transforms a video sequence into a set of spike trains, as those emitted by retinal ganglion cells. It includes a linear model of filtering in the Outer Plexiform Layer (OPL), a contrast gain control mechanism modeling the non-linear feedback of some amacrine cells on bipolar cells, and a spike generation process modeling ganglion cells. A strength of the model is that each of its features can be associated to a precise physiological signification and location. The resulting retina model can simulate physiological recordings on mammalian retinas, including such non-linearities as cat Y cells, or contrast gain control. Furthermore, the model has been implemented on a large-scale simulator that can emulate the spikes of up to 100,000 neurons

Efficient scatter-based kernel superposition on GPU

This is the author accepted manuscript. The final version is available from ACM via https://dl.acm.org/doi/10.1016/j.jpdc.2015.07.003.Kernel superposition, where an image is convolved with a spatially varying kernel, is commonly used in optics, astronomy, medical imaging and radiotherapy. This operation is computationally expensive and generally cannot benefit from the mathematical simplifications available for true convolutions. We systematically evaluated the performance of a number of implementations of a 2D Gaussian kernel superposition on several graphics processing units of two recent architectures. The 2D Gaussian kernel was used because of its importance in real-life applications and representativeness of expensive-to-evaluate, separable kernels. The implementations were based both on the gather approach found in the literature and on the scatter approach presented here. Our results show that, over a range of kernel sizes, the scatter approach delivers speedups of 2.1-14.5 or 1.3-4.9 times, depending on the architecture. These numbers were further improved to 4.8-28.5 and 3.7-16.8 times, respectively, when only "exact" implementations were compared. Speedups similar to those presented are expected for other separable kernels and, we argue, will also remain applicable for problems of higher dimensionality. We investigate the performance of a 2D Gaussian variable kernel convolution on GPU.We produce several implementations, both conventional, gather-based, and new, scatter-based.The performance of the implementations is evaluated on several GPUs of different architecture.The scatter-based approach achieves considerably better performance for all GPUs and kernel sizes.The results should extend to any similar kernel and to problems of higher dimensionality

Performance of three recursive algorithms for fast space-variant Gaussian filtering

Animal visual systems have solved the problem of limited resources by allocating more processing power to central than peripheral vision. Foveation considerably reduces the amount of data per image by progressively decreasing the resolution at the periphery while retaining a sharp center of interest. This strategy has important applications in the design of autonomous systems for navigation, tracking and surveillance. Central to foveation is a space-variant Gaussian filtering scheme that gradually blurs out details as the distance to the image center increases. Unfortunately Gaussian convolution is a computationally expensive operation, which can severely limit the real-time applicability of foveation. In the space-variant case, the problem is even more difficult as traditional techniques such as the fast Fourier transform cannot be employed because the convolution kernel is different at each pixel. We show that recursive filtering, which was introduced to approximate Gaussian convolution, can be extended to the spacevariant case and leads to a very simple implementation that makes it ideal for that application. Three main recursive algorithms have emerged, produced by independent derivation methods. We assess and compare their performance in traditional filtering applications and in our specific space-variant case. All three methods drastically cut down the cost of Gaussian filtering to a limited number of operations per pixel that is independent of the scale selected. In addition we show that two of those algorithms have excellent accuracy in that the output they produce differs from the output obtained performing real Gaussian convolution by less than 1%. r 2003 Elsevier Ltd. All rights reserved. 1

Mitigation of contrast loss in underwater images

The quality of an underwater image is degraded due to the effects of light scattering in water, which are resolution loss and contrast loss. Contrast loss is the main degradation problem in underwater images which is caused by the effect of optical back-scatter. A method is proposed to improve the contrast of an underwater image by mitigating the effect of optical back-scatter after image acquisition. The proposed method is based on the inverse model of an underwater image model, which is validated experimentally in this work. It suggests that the recovered image can be obtained by subtracting the intensity value due to the effect of optical back-scatter from the degraded image pixel and then scaling the remaining by a factor due to the effect of optical extinction. Three filters are proposed to estimate for optical back-scatter in a degraded image. Among these three filters, the performance of BS-CostFunc filter is the best. The physical model of the optical extinction indicates that the optical extinction can be calculated by knowing the level of optical back-scatter. Results from simulations with synthetic images and experiments with real constrained images in monochrome indicate that the maximum optical back-scatter estimation error is less than 5%. The proposed algorithm can significantly improve the contrast of a monochrome underwater image. Results of colour simulations with synthetic colour images and experiments with real constrained colour images indicate that the proposed method is applicable to colour images with colour fidelity. However, for colour images in wide spectral bands, such as RGB, the colour of the improved images is similar to the colour of that of the reference images. Yet, the improved images are darker than the reference images in terms of intensity. The darkness of the improved images is because of the effect of noise on the level of estimation errors.EThOS - Electronic Theses Online Servicety of ManchesterThe Petroleum Institute in Abu DhabiGBUnited Kingdo

Foveation for 3D visualization and stereo imaging

Even though computer vision and digital photogrammetry share a number of goals, techniques, and methods, the potential for cooperation between these fields is not fully exploited. In attempt to help bridging the two, this work brings a well-known computer vision and image processing technique called foveation and introduces it to photogrammetry, creating a hybrid application. The results may be beneficial for both fields, plus the general stereo imaging community, and virtual reality applications. Foveation is a biologically motivated image compression method that is often used for transmitting videos and images over networks. It is possible to view foveation as an area of interest management method as well as a compression technique. While the most common foveation applications are in 2D there are a number of binocular approaches as well. For this research, the current state of the art in the literature on level of detail, human visual system, stereoscopic perception, stereoscopic displays, 2D and 3D foveation, and digital photogrammetry were reviewed. After the review, a stereo-foveation model was constructed and an implementation was realized to demonstrate a proof of concept. The conceptual approach is treated as generic, while the implementation was conducted under certain limitations, which are documented in the relevant context. A stand-alone program called Foveaglyph is created in the implementation process. Foveaglyph takes a stereo pair as input and uses an image matching algorithm to find the parallax values. It then calculates the 3D coordinates for each pixel from the geometric relationships between the object and the camera configuration or via a parallax function. Once 3D coordinates are obtained, a 3D image pyramid is created. Then, using a distance dependent level of detail function, spherical volume rings with varying resolutions throughout the 3D space are created. The user determines the area of interest. The result of the application is a user controlled, highly compressed non-uniform 3D anaglyph image. 2D foveation is also provided as an option. This type of development in a photogrammetric visualization unit is beneficial for system performance. The research is particularly relevant for large displays and head mounted displays. Although, the implementation, because it is done for a single user, would possibly be best suited to a head mounted display (HMD) application. The resulting stereo-foveated image can be loaded moderately faster than the uniform original. Therefore, the program can potentially be adapted to an active vision system and manage the scene as the user glances around, given that an eye tracker determines where exactly the eyes accommodate. This exploration may also be extended to robotics and other robot vision applications. Additionally, it can also be used for attention management and the viewer can be directed to the object(s) of interest the demonstrator would like to present (e.g. in 3D cinema). Based on the literature, we also believe this approach should help resolve several problems associated with stereoscopic displays such as the accommodation convergence problem and diplopia. While the available literature provides some empirical evidence to support the usability and benefits of stereo foveation, further tests are needed. User surveys related to the human factors in using stereo foveated images, such as its possible contribution to prevent user discomfort and virtual simulator sickness (VSS) in virtual environments, are left as future work.reviewe

A computational and psychophysical study of motion induced distortions of perceived location.

In this thesis I begin by extending previous psychophysical research on the effects of visual motion on spatial localisation. In particular, I measured the perceived spatial shift of briefly presented static objects adjacent to a moving stimulus. It was found that the timing of the presentation of static objects with respect to nearby motion was crucial. I also found a decrease of this motion induced spatial displacement with the increasing distance of static objects from motion, suggesting a local effect of motion. The induced perceptual shift could also be reduced by introducing transient stimuli (flickering dots) in the background of the display. The next stage was to construct a computational model to provide a mechanism that could facilitate such shifts in position. To motivate our combined model of motion computation and spatial representation we considered what functions could be attributed to V1 cells on the basis of their contrast sensitivity functions. I found that functions based on sums of differential of Gaussian operators could provide good fits to previously found V1 data. The properties of V1 cells as derivatives of Gaussian kernel filters on an image were used to build a spatial representation, where position is represented in the weighting of these filter outputs, rather than in a one-to-one isomorphic representation of the scene. This image representation can also be used along with temporal derivatives to calculate motion using the Multi-Channel Gradient Model scheme (Johnston et al, 1992). 1 demonstrate how this framework can incorporate motion signals to produce "in place" shifts of visual location. Finally a combined model of motion and spatial location is outlined and evaluated in relation to the psychophysical data

Pencil beam dose calculation for proton therapy on graphics processing units

Radiotherapy delivered using scanned beams of protons enables greater conformity between the dose distribution and the tumour than conventional radiotherapy using X rays. However, the dose distributions are more sensitive to changes in patient anatomy, and tend to deteriorate in the presence of motion. Online dose calculation during treatment delivery offers a way of monitoring the delivered dose in real time, and could be used as a basis for mitigating the effects of motion. The aim of this work has therefore been to investigate how the computational power offered by graphics processing units can be harnessed to enable fast analytical dose calculation for online monitoring in proton therapy. The first part of the work consisted of a systematic investigation of various approaches to implementing the most computationally expensive step of the pencil beam algorithm to run on graphics processing units. As a result, it was demonstrated how the kernel superposition operation, or convolution with a spatially varying kernel, can be efficiently implemented using a novel scatter-based approach. For the intended application, this outperformed the conventional gather-based approach suggested in the literature, permitting faster pencil beam dose calculation and potential speedups of related algorithms in other fields. In the second part, a parallelised proton therapy dose calculation engine employing the scatter-based kernel superposition implementation was developed. Such a dose calculation engine, running all of the principal steps of the pencil beam algorithm on a graphics processing unit, had not previously been presented in the literature. The accuracy of the calculation in the high- and medium-dose regions matched that of a clinical treatment planning system whilst the calculation was an order of magnitude faster than previously reported. Importantly, the calculation times were short, both compared to the dead time available during treatment delivery and to the typical motion period, making the implementation suitable for online calculation. In the final part, the beam model of the dose calculation engine was extended to account for the low-dose halo caused by particles travelling at large angles with the beam, making the algorithm comparable to those in current clinical use. By reusing the workflow of the initial calculation but employing a lower resolution for the halo calculation, it was demonstrated how the improved beam model could be included without prohibitively prolonging the calculation time. Since the implementation was based on a widely used algorithm, it was further predicted that by careful tuning, the dose calculation engine would be able to reproduce the dose from a general beamline with sufficient accuracy. Based on the presented results, it was concluded that, by using a single graphics processing unit, dose calculation using the pencil beam algorithm could be made sufficiently fast for online dose monitoring, whilst maintaining the accuracy of current clinical systems.Financial support for this project came from the ENTERVISION Marie Curie Initial Training Network, funded by the European Commission Seventh Framework Programme grant agreement number 264,552. The Tesla K40 graphics processing unit used in parts of this work was donated by Nvidia Corporation through their Hardware Grant Program