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8th International Conference of Pattern Recognition systems (ICPRS 2017)

作者： v. Jakstys v. Marcinkevicius P. Treigys Vilniaus Universitetas Vilnius LT

ISBN: (纸本)9781785616525

This paper focuses on the lateral chromatic aberration correction to eliminate the chromatic aberration effect with image processing methods. images were captured by the portable non-mydriatic eye fundus orbital camera that has no achromatic lenses. image processing methods for correcting the lateral chromatic aberration effect correction try to scale the fringed colour channels so that all channels spatially overlap each other correctly in the final image. Classical algorithms are based on different patterns evaluation to derive an appropriate model for the correction. In this research, we have shown that these patterns can be successfully replaced with a map of detected eye fundus blood vessels. A comparison of the pattern (chessboard and circles) based method and the proposed method was accomplished using five different correction models: simple, affine, projective and two radial ones with one and two coefficients. Quality of images was measured and evaluated using Blur Metric, Chromatic Zipper and Achromatic Zipper metrics. The results have showed that the chromatic aberration correction using both methods have a significantly improved quality of the original images.

关键词： aberrations biomedical optical imaging blood vessels cameras eye medical image processing CHROMATIC ABERRATION Fundus Oculi Biomedical optical imaging Medical image processing Cameras aberrations Blood vessels Eye images original image

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Towards composable GPU programming: Programming GPUs with eager actions and lazy views 8

Towards composable GPU programming: Programming GPUs with ea...

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8th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2017

作者： Haidl, Michael Steuwer, Michel Dirks, Hendrik Humernbrum, Tim Gorlatch, Sergei University of Münster Germany University of Edinburgh United Kingdom

ISBN: (纸本)9781450348836

In this paper, we advocate a composable approach to programming systems with Graphics processing Units (GPU): programs are developed as compositions of generic, reusable patterns. Current GPU programming approaches either rely on low-level, monolithic code without patterns (CUDA and OpenCL), which achieves high performance at the cost of cumbersome and error-prone programming, or they improve the programmability by using pattern-based abstractions (e.g., Thrust) but pay a performance penalty due to inefficient implementations of pattern composition. We develop an API for GPUs based programming on C++ with STL-style patterns and its compiler-based implementation. Our API gives the application developers the native C++ means (views and actions) to specify precisely which pattern compositions should be automatically fused during code generation into a single efficient GPU kernel, thereby ensuring a high target performance. We implement our approach by extending the range-v3 library which is currently being developed for the forthcoming C++ standards. The composable programming in our approach is done exclusively in the standard C++14, with STL algorithms used as patterns which we re-implemented in parallel for GPU. Our compiler implementation is based on the LLvM and Clang frameworks, and we use advanced multi-stage programming techniques for aggressive runtime optimizations. We experimentally evaluate our approach using a set of benchmark applications and a real-world case study from the area of image processing. Our codes achieve performance competitive with CUDA monolithic implementations, and we outperform pattern-based codes written using Nvidia's Thrust. Copyright © 2017 held by owner/author(s).

关键词： Graphics processing unit

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A modified technique for smart textural feature selection to extract retinal regions of interest using image pre-processing

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Journal of Physics: Conference Series 2018年第1期1096卷

作者： N Yu Ilyasova A S Shirokanev R A Paringer A v Kupriyanov A v Zolotarev Samara National Research University Moskovskoe shosse st. 34 Samara Russia 443086 Image Processing Systems Institute - Branch of the Federal Scientific Research Centre 'Crystallography and Photonics' of Russian Academy of Sciences Molodogvardeyskaya str. 151 Samara Russia 443001 Samara State Medical University Chapayevskaya st. 89 Samara Russia 443066

To increase the efficiency of the laser coagulation surgery the problem of the most accurate segmentation of fundus images is especially relevant. Fundus image segmentation is carried out with high accuracy using effective features and the minimum number of parameters for segmentation of a single image fragment. This paper describes a modified technique for smart textural feature selection to extract retinal regions of interest using image preprocessing algorithms. Preprocessing algorithms significantly influence the selected features which provide a minimum error of object recognition. In addition image preprocessing algorithms provide a more precise object selection. The informativeness of the obtained feature space is studied using discriminant data analysis. The best fragmentation block size segmentation and feature sets provides the necessary accuracy to identify regions of interest. Those regions are determined by the analysis of the following 4 classes of fundus images: exudates, thick, thin vessels and healthy areas. The advantages and disadvantages of the considered preprocessing algorithms were identified.

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Comparative study on road and lane detection inmixed criticality embedded systems

Comparative study on road and lane detection inmixed critica...

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作者： FERHATOvIC, SANEL KTH

One of the main challenges for advanced driver assistance systems (ADAS)is the environment perception problem. One factor that makes ADAS hardto implement is the large amount of different conditions that have to betaken care of. The main sources for condition diversity are lane and roadappearance, image clarity issues and poor visibility conditions. A review ofcurrent lane detection algorithms has been carried out and based on that alane detection algorithm has been developed and implemented on a mixedcriticality platform. The thesis is part of a larger group project consisting offive master thesis students creating a demonstrator for autonomous platoondriving. The final lane detection algorithms consists of preprocessing stepswhere the image is converted to gray scale and everything except the regionof interest (ROI) is cut away. OpenCv, a library for image processing hasbeen utilized for edge detection and hough transform. An algorithm for errorcalculations is developed which compares the center and direction of the lanewith the actual vehicle position and direction during real experiments. Thelane detection system is implemented on a Raspberry Pi which communicateswith a mixed criticality platform through UART. The demonstrator vehiclecan achieve a measured speed of 3.5 m/s with reliable lane keeping using thedeveloped algorithm. It seems that the bottleneck is the lateral control ofthe vehicle rather than lane detection, further work should focus on controlof the vehicle and possibly extending the ROI to detect curves in an earlierstage. En stor utmaning för avancerade förarstödsystem (ADAS) är problemet med uppfattning av miljön runt omkring. En faktor som gör ADAS svårt att implementera är den stora mängd olika förhållanden som måste tas hand om. De största källorna till olikheter är utseendet på körfältet och vägen, dåliga siktförhållanden samt otydliga bilder. En granskning av nuvarande algoritmer för körfältsdetektering har utförts och baserat på den har en

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Learning Fast Sparsifying Overcomplete Dictionaries

Learning Fast Sparsifying Overcomplete Dictionaries

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European Signal processing Conference

作者： Cristian Rusu John Thompson Institute for Digital Communications University of Edinburgh

In this paper we propose a dictionary learning method that builds an overcomplete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally complexity. To achieve this we consider two factors (both to be learned from data) in order to design the dictionary: an orthonormal component made up of a fixed number of fast fundamental orthonormal transforms and a sparse component that builds linear combinations of elements from the first, orthonormal component. We show how effective the proposed technique is to encode image data and compare against a previously proposed method from the literature. We expect the current work to contribute to the spread of sparsity and dictionary learning techniques to hardware scenarios where there are hard limits on the computational capabilities and energy consumption of the computer systems.

关键词： Dictionaries Sparse matrices Signal processing algorithms Machine learning Transforms Linear programming Approximation algorithms

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Effect of temporal and spatial noise on the performance of hardware oriented background extraction algorithms

Effect of temporal and spatial noise on the performance of h...

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Annual IEEE Northeast Workshop on Circuits and systems (NEWCAS)

作者： D. García-Lesta v.M. Brea P. López D. Cabello Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS) Santiago de Compostela Spain

ISBN: (纸本)9781509049929

Background extraction is a fundamental task present in most computer vision applications such as video surveillance, optical motion capture or multimedia applications. In this paper we explore a particular foreground segmentation method based on the well-known Pixel-based Adaptive Segmenter (PBAS) algorithm, proposing modifications that will ease the hardware implementation. Also, the figures of merit of a focal-plane approach for foreground segmentation are studied through the impact of typical temporal and spatial noise sources present in the processing elements of smart image sensors such as leakage currents from analog memories or fixed pattern noise (FPN) from mismatch.

关键词： Algorithm design and analysis Leakage currents Heuristic algorithms Mathematical model Pipelines image segmentation image sensors

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Graph-Based Interpolation for Zooming in 3D Scenes

Graph-Based Interpolation for Zooming in 3D Scenes

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European Signal processing Conference

作者： Pinar Akyazi Pascal Frossard Ecole Polytechnique Fcderalc de Lausanne (EPFL) Signal Processing Laboratory (LTS4) Lausanne Switzerland

In multiview systems, color plus depth format builds 3D representations of scenes within which the users can freely navigate by changing their viewpoints. In this paper we present a framework for view synthesis when the user requests an arbitrary viewpoint that is closer to the 3D scene than the reference image. On the target image plane, the requested view obtained via depth-image-based-rendering (DIBR) is irregularly structured and has missing information due to the expansion of objects. We propose a novel framework that adopts a graph-based representation of the target view in order to interpolate the missing image pixels under sparsity priors. More specifically, we impose that the target image is reconstructed with a few atoms of a graph-based dictionary. Experimental results show that the reconstructed views have better PSNR and MSSIM quality than the ones generated within the same framework with analytical dictionaries, and are comparable to the ones reconstructed with Tv regularization and linear interpolation on graphs. visual results, however, show that our method better preserves the details and results in fewer disturbing artifacts than the other interpolation methods.

关键词： Interpolation Dictionaries Three-dimensional displays image reconstruction Navigation Signal processing algorithms Signal processing

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ProxImaL: Efficient image Optimization using Proximal algorithms

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ACM TRANSACTIONS ON GRAPHICS 2016年第4期35卷 1-15页

作者： Heide, Felix Diamond, Steven Niessner, Matthias Ragan-Kelley, Jonathan Heidrich, Wolfgang Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA Univ British Columbia Vancouver BC V5Z 1M9 Canada KAUST Thuwal Saudi Arabia

Computational photography systems are becoming increasingly diverse, while computational resources-for example on mobile platforms-are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying image processing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.

关键词： computational photography digital image processing optimization

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The adaptive approach to abnormal situations recognition using images from condition monitoring systems

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Optical Memory and Neural Networks (Information Optics) 2016年第2期25卷 79-87页

作者： Savchenko, A.v. Milov, v.R. National Research University Higher School of Economics Nizhny Novgorod Russia Institute of Radio Electronics and Information Technologies Nizhny Novgorod State Technical University n.a. R.E. Alekseev Nizhny Novgorod Russia

Decision support in equipment condition monitoring systems with image processing is analyzed. Long-run accumulation of information about earlier made decisions is used to realize the adaptiveness of the proposed approach. It is shown that unlike conventional classification problems, the recognition of abnormalities uses training samples supplemented with reward estimates of earlier decisions and can be tackled using reinforcement learning algorithms. We consider the basic stages of contextual multi-armed bandit algorithms during which the probabilistic distributions of each state are evaluated to evaluate the current knowledge of the states, and the decision space is explored to increase the decision-making efficiency. We propose a new decision-making method, which uses the probabilistic neural network to classify abnormal situation and the softmax rule to explore the decision space. A modelling experiment in image processing was carried out to show that our approach allows a higher accuracy of abnormality detection than other known methods, especially for small-size initial training samples. © 2016, Allerton Press, Inc.

关键词： Neural networks

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Fast HEvC intra coding decision based on statistical cost and corner detection

Fast HEVC intra coding decision based on statistical cost an...

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International Conference on systems, Signals and image processing, IWSSIP

作者： Biao Min Zhe Xu Ray C. C. Cheung Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781509063451

As the successor of H.264, High Efficient video Coding (HEvC) standard includes various novel techniques, including Coding Tree Unit (CTU) structure and additional angular modes used in intra coding. These new techniques promote the coding efficiency on one hand, while increasing the computational complexity significantly on the other hand. In this paper, we propose a fast intra block partitioning algorithm for HEvC to reduce the coding complexity, based on the statistical cost and corner detection algorithm. A block is considered as a multiple gradients region which will be split into multiple small ones, as the corner points are detected inside the block. A block without corner points existing is treated as being non-split when its RD cost is small according the statistics of the previous frames. The proposed fast algorithm achieves nearly 63% encoding time reduction with 3.42%, 2.80%, and 2.53% BD-Rate loss for Y, U, and v components, averagely. The experimental results show that the proposed method is efficient to fast decide the block partitioning in intra coding of HEvC, even though only static parameters are applied to all test sequences.

关键词： Copper image coding Complexity theory Partitioning algorithms Encoding image edge detection Mathematical model

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