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Red eye detection using color and shape

Red eye detection using color and shape

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international workshop on local and non-local approximation in image processing, lnla

作者： Leena Lepisto Aki Launiainen Iivari Kunttu Nokia Corporation Tampere Finland

Red eye artifact caused by flash is a common problem in consumer photography. The artifact can be avoided by certain flash techniques, but the use of image processing algorithms has proved to be an efficient approach for the detection and correction of the red eyes in the images. In this paper, we present a novel method for an automatic detection of red eyes in the image. The method is based on the localization of red regions in the image, after which the special character of each candidate region is inspected based on shape analysis. Once the candidate regions have been detected, they are classified into red eyes and other regions based on specific classification rules. In the experiments, a wide set of digital photographs containing red eyes in different conditions and circumstances have been used. The results reveal that the method is able to detect relatively accurately the red eyes in the images, and it provides robustness and fastness required in the mobile devices. Also the rate of false positive detections is low.

关键词： Shape Eyes Face detection image quality Digital cameras Photography Employment image processing image analysis Robustness

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Methods for local phase quantization in blur-insensitive image analysis

Methods for local phase quantization in blur-insensitive ima...

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international workshop on local and non-local approximation in image processing, lnla

作者： Janne Heikkila Ville Ojansivu Infotech Oulu and Department of Electrical and Information Engineering University of Oulu Finland

image quality is often degraded by blur caused by, for example, misfocused optics or camera motion. Blurring may also deteriorate the performance of computer vision algorithms if the image features computed are sensitive to these degradations. In this paper, we present an image descriptor based on local phase quantization that is robust to centrally symmetric blur. The descriptor referred to as local phase quantization (LPQ) can be used to characterize the underlying image texture. We also present a decorrelation scheme and propose three approaches for extracting the local phase information. Different combinations of them result in totally six variants of the operator that can be used alternatively. We show experimentally that these operators have slightly varying performance under different blurring conditions. In all test cases, including also sharp images, the new descriptors can outperform two state-of-the-art methods, namely, local binary pattern (LBP) and a method based on Gabor filter banks.

关键词： Quantization image analysis Degradation Computer vision image motion analysis image texture analysis image quality Optical filters Optical sensors Cameras

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Context based super resolution image reconstruction

Context based super resolution image reconstruction

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international workshop on local and non-local approximation in image processing, lnla

作者： Emre Turgay Gozde B. Akar Department of Image Processing MGEO Division Aselsan Inc. Ankara Turkey Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey

In this paper a context based super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator identifies local gradients and textures for selecting the optimal SR method for the region of interest. Texture segmentation and gradient map estimation are done prior to the reconstruction stage. Gradient direction is used for optimal noise reduction along the edges for non-textured regions. On the other hand, regularization term is cancelled for textured regions so that the resultant method reduces to maximum likelihood (ML) solution. It is demonstrated on Brodatz texture database that ML solution gives the best PSNR values on textures compared to the regularized SR methods in the literature. Experimental results show that the proposed hybrid method has superior performance in terms of peak signal-to-noise-ratio (PSNR), structural similarity index measure (SSIM) compared the SR methods in the literature.

关键词： image resolution image reconstruction Strontium PSNR Signal resolution Maximum a posteriori estimation image segmentation Noise reduction Maximum likelihood estimation Databases

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Natural image statistics: Energy-based models estimated by score matching

Natural image statistics: Energy-based models estimated by s...

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international workshop on local and non-local approximation in image processing, lnla

作者： Urs Koster Aapo Hyvarinen Department of Computer Science University of Helsinki Finland Department of Mathematics and Statistics University of Helsinki Finland

Score matching is recent framework for parameter estimation in energy based models. In this class of probabilistic models, the intractable partition function has classically made learning a computationally expensive process necessitating the use of approximations, but with score matching this is no longer the case. This makes it straightforward to estimate models of natural images that have an intractable partition function, but offer advantages over classical model such as ICA by allowing for overcompleteness or nonlinear representations. Here we present two such models for natural images. The first is a hierarchical model with two layers estimated from the data, where the second layer serves to capture variance dependencies between first layer outputs. The second model we consider is an extension of ICA to a Markov random field, which allows us to generalize the ICA framework to images of arbitrary size.

关键词： Statistics Independent component analysis image processing Filters Mathematical model Parameter estimation Markov random fields Noise reduction Information technology Computer science

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Imaging without optics: Optics-less smart sensors

Imaging without optics: Optics-less smart sensors

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international workshop on local and non-local approximation in image processing, lnla

作者： L. Yaroslavsky Ch. Goerzen S. Umansky H. J. Caulfield Tampere International Center of Signal Processing Tampere University of Technology Tampere Finland Department of Physical Electronics Faculty of Engineering Tel-Aviv University Tel-Aviv Israel Physics Department Fisk University Nashville TN USA

A new family of radiation sensors is introduced that does not require any optics. These sensors consist of array of bare radiation detectors whose angular sensitivity is determined solely by their surface absorption. This array is supplemented with a signal processing unit that uses the detector outputs to compute optimal statistical estimates of the intensities and coordinates of radiation sources. Such sensors are capable of accurate localization and intensity estimation of a given number of radiation sources and of image forming when assuming of a given number of sources in known positions. The estimation accuracy is found to depend on the detectors' noise level, on the number of detectors and on spacing between radiation sources. Advantages and limitations of proposed opticsless sensors are discussed. In addition a hypothesis is advanced that the operational principle of the sensors can shed a light on possible mechanisms of cutaneous vision in Nature and on evolution of vision.

关键词： Optical sensors Optical imaging Intelligent sensors Optical signal processing Sensor arrays Radiation detectors Absorption Array signal processing image sensors Noise level

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Coupled multi-frame super-resolution with diffusive motion model and total variation regularization

Coupled multi-frame super-resolution with diffusive motion m...

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international workshop on local and non-local approximation in image processing, lnla

作者： Mehran Ebrahimi Edward R. Vrscay Anne L. Martel Department of Medical Biophysics Imaging Research Sunnybrook Health Science Centre University of Toronto Toronto ONT Canada Department of Applied Mathematics University of Waterloo Waterloo ONT Canada

The problem of recovering a high-resolution image from a set of distorted (e.g., warped, blurred, noisy) and low-resolution images is known as super-resolution. Accurate motion estimation from low-resolution measurements is a fundamental challenge of the super-resolution problem. Some recent promising advances in this area have been focused on coupling or combing the super-resolution reconstruction and the motion estimation. However, the existing approaches are limited to parametric motion models, e.g., affine transformations. In this paper, we shall address the coupled super-resolution problem with a non-parametric motion model. We then consider a variational formulation of the problem and use a PDE-approach to construct a numerical scheme for its solution. In this paper, diffusion regularization is used for the motion model and total variation regularization for the super-resolved image.

关键词： Spatial resolution Motion estimation image resolution image reconstruction High-resolution imaging Layout Interpolation Sensor arrays image sampling Robustness

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nonlocal image deblurring: Variational formulation with nonlocal collaborative L0-norm prior

Nonlocal image deblurring: Variational formulation with nonl...

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international workshop on local and non-local approximation in image processing, lnla

作者： Vladimir Katkovnik Karen Egiazarian Department of Signal Processing Tampere University of Technology Tampere Finland

Spatially adaptive nonlocal patch-wise estimation is one of the most promising recent directions in image processing. Within this framework a set of the state-of-the-art Block Matching 3-D (BM3D) algorithms has been developed for different imaging problems. Recently, a special prior hoas been proposed allowing to reformulate the multi-state hard-thresholding BM3D denoising as global minimization of an energy criterion. The out-standing performance of BM3D works as a strong argument in favor of this prior giving an efficient multilayer redundant image model. The variational formulation is used in order to design a novel recursive denoising algorithm. In this paper the nonlocal collaborative l 0 -norm prior is a tool to design deblurring algorithms, where the global penalty function works as an adaptive regularizator. The main contribution concerns the development and testing of algebraic and frequency domain recursive algorithms minimizing the global criterion. Simulation demonstrate a very good performance of the novel algorithms.

关键词： image restoration Noise reduction Algorithm design and analysis image processing nonhomogeneous media Collaborative tools Collaborative work international collaboration Testing Frequency domain analysis

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A note on multi-image denoising

A note on multi-image denoising

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international workshop on local and non-local approximation in image processing, lnla

作者： Toni Buades Yifei Lou J.M. Morel Zhongwei Tang Departament de Matematiques e Informatica Universitat de les Illes Balears Spain MAP5 Université René Descartes Paris France Department of Mathematics University of California Los Angeles USA Centre de Mathðmatiques et de Leurs Applications Ecole Normale Supérieure de Cachan France

Taking photographs under low light conditions with a hand-held camera is problematic. A long exposure time can cause motion blur due to the camera shaking and a short exposure time gives a noisy image. We consider the new technical possibility offered by cameras that take image bursts. Each image of the burst is sharp but noisy. In this preliminary investigation, we explore a strategy to efficiently denoise multi-images or video. The proposed algorithm is a complex image processing chain involving accurate registration, video equalization, noise estimation and the use of state-of-the-art denoising methods. Yet, we show that this complex chain may become risk free thanks to a key feature: the noise model can be estimated accurately from the image burst. Preliminary tests will be presented. On the technical side, the method can already be used to estimate a non parametric camera noise model from any image burst.

关键词： Noise reduction Cameras Layout Mathematics image processing State estimation Testing Colored noise Pixel Random variables

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Entropic segmentation by region growing and merging for drop shape analysis

Entropic segmentation by region growing and merging for drop...

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international workshop on local and non-local approximation in image processing, lnla

作者： Juan F. Gomez-Lopera Pedro L. Luque-Escamilla Jose Martinez-Aroza Ramon Roman-Roldan Miguel A. Cabrerizo-Vilchez Miguel A. Rodriguez-Valverde Francisco J. Montes-Ruiz-Cabello Departamento de Física Aplicada Universidad de Granada Granada Spain Departamento de Ingeniería Mecánica y Minera Universidad de Jaén Spain Departamento de Matemática Aplicada Universidad de Granada Granada Spain

A new approach to image segmentation based on entropic region growing and merging, which is useful in drop shape analysis, is presented in this paper. The procedure works in three steps. First, a normalized divergence matrix is obtained which gives the likelihood of being a boundary pixel for each pixel in the image. Second, a region growing algorithm is carried out on the divergence matrix, keeping a record of boundaries between adjacent regions. Third, some regions are merged by following a combined entropic criterion, based on both the divergences of the matrix along the common boundary and the global divergence between two adjacent regions. The final contour is adapted by a dynamical spline fitting. This general purpose algorithm is presented here applied to drop shape analysis.

关键词： Merging Shape Pixel Background noise image edge detection image segmentation image analysis Spline Fitting Algorithm design and analysis

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Lossy compression of images corrupted by mixed Poisson and additive Gaussian noise

Lossy compression of images corrupted by mixed Poisson and a...

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international workshop on local and non-local approximation in image processing, lnla

作者： V.V. Lukin S.S. Krivenko M.S. Zriakhov N.N. Ponomarenko S.K. Abramov A. Kaarna K. Egiazarian Department of Transmitters Receivers and Signal Processing Kharkov National Aerospace University KhAI Kharkov Ukraine Department of Information Technology Lappeenranta University of Technology Lappeeranta Finland Institute of Signal Processing Tampere University of Technology Tampere Finland

Lossy compression of images corrupted by mixed Poisson and additive Gaussian noise is considered. Peculiarities of noise filtering effects observed for two approaches to image compression are studied. This is first done for specially created artificial image and, then, for two standard test images. It is shown that the standard (direct) approach to lossy compression leads to different degrees of noise suppression depending upon image mean in homogeneous regions. The use of root transformation leads to better results. Moreover, coder parameters can be adjusted to mixed noise parameters if they are known a priori. Then it becomes possible to reach optimal operation point (OOP), i.e., such compression ratio (CR) or bpp that provide maximal peak signal-to noise ratio (PSNR) or minimal MSE calculated between compressed and noise free images without having an actual noise-free image. A provided CR can be about 10hellip30 depending upon image complexity.

关键词： image coding Additive noise Gaussian noise PSNR Chromium Signal processing Filtering Code standards Transmitters Information technology

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