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This paper considers the problem of recovering a one or two dimensional discrete signal which is approximately sparse in its gradient from an incomplete subset of its Fourier coefficients which have been corrupted with noise. The results show that in order to obtain a reconstruction which is robust to noise and stable to inexact gradient sparsity of order s with high probability, it suffices to draw...
Tumor Segmentation of MRI Brain images is still a challenging problem. The paper proposes a fast MRI Brain Image segmentation method based on Artificial Bee Colony (ABC) algorithm and Fuzzy-C Means (FCM) algorithm. The value in continuous gray scale interval is searched using threshold estimation. The optimal threshold value is searched with the help of ABC algorithm. In order to get an efficient...
A major challenge for fMRI analysis of the developing brain is subject motion, which can corrupt T2∗-weighted (T2∗-w) signal intensity with spin history effects. Multi echo multislice EPI acqusitions can be used to create parameteric R2∗ mapping for fMRI that can provide independence from such signal variation. However, motion between slice acquisitions scatters the measurements over the anatomy of...
Automatic segmentation of fiber bundles can be beneficial to quantitative analysis on neuropsychiatric diseases. Previous clustering methods for fiber segmentation typically specify the number of clusters in advance or rely on prior knowledge. In this paper, we develop a new segmentation algorithm based on density-peaks clustering, in which the number of clusters can arise intuitively. This clustering...
To enhance post-acquisition processing of fetal brain MRI we have developed a template-to-slice block matching technique that matches a spatiotemporal (4D) atlas of the fetal brain to the corresponding section of the brain in each 2D fetal MRI scan. As compared to the recent studies which used feature based approaches for fetal brain localization, we propose a template matching approach that registers...
In this paper, we propose a novel dense correspondence based prediction approach to reduce the inter-image redundancy for image set compression. Unlike previous methods, we manage to utilize the dense correspondence to predict and parameterize the inter-image relation and then reconstruct a new reference for the subsequent HEVC inter-prediction and encoding. Comparing to relevant state-of-the-art...
Principal Component Analysis (PCA) is one of the most widely used tools for the representation of high-dimensional data. Many different versions have been proposed to enhance the robustness of the model. Most of these ideas are not median based formulation, which is always a robust estimator in statistics. In this paper, we attempt to design a new median based PCA model based on k-medians clustering,...
This paper addresses the problem of impulse denoising from hyper-spectral images. Impulse noise is sparse; removing impulse noise requires minimizing an l1-norm data fidelity term. Prior studies have exploited the intra-band spatial correlation (leading to sparsity in transform domain) and inter-band spectral-correlation (joint-sparsity) of hyper-spectral images for Gaussian denoising. In this work,...
The paper investigates combining Compressive Sensing (CS) with the robust Capon beamformer (RCB) for the purpose of medical ultrasound image formation with a much reduced number of samples compared to those used in current state-of-art ultrasound. The proposed CS algorithm uses wave atom dictionary as a low dimension projection, a Bernouli random matrix as a sensing matrix and a regularized-l1 optimization...
Blind reconstruction or deconvolution, is the process of restoring an observed image without explicit knowledge of the imaging system's point spread function (PSF). Images produced from an imaging system, for example confocal laser scanning or widefield optical microscope, are noisy and invariably blurred. For robust scientific interpretation and analysis of a typical image obtained in this way, it...
In this paper we present an update on the geometric modeling of urban scenes from physical measurements. This field of research has been studied for more than thirty years, but remains an important challenge in many scientific communities as photogrammetry, computer vision, robotics or computer graphics. After introducing the objectives and difficulties of urban reconstruction, we present an non-exhaustive...
In this paper, an automatic approach is presented to detect/extract buildings from spaceborne TomoSAR point clouds. The approach is systematic and allows robust detection of both tall and low height buildings and is, therefore, well suited for urban monitoring of larger areas from space. The presented approach is illustrated and validated by examples using TomoSAR point clouds generated from a stack...
A Robust Multi frame image Super Resolution Reconstruction (SRR) is a process which produces a better or superior quality, High Resolution (HR) image from multiple numbers of blurred noisy low resolution (LR) images of the similar scene, acquired under different conditions. It produces a high quality super resolution image with higher spatial frequency, reduced noise and image blur as compared to...
In many circumstances the limitation for use of video cameras is energy. The energy needed for compression and transmission of video is substantial, and is linear with the number of transmitted frames. Time-lapse photography, a drastic reduction of transmitted frame rate, is an obvious solution, say by transmitting one frame every several minutes. The temporal resolution of the video is lost. Can...
The multi-frame SRR (Super Resolution Reconstruction) algorithm has become the significant theme in digital image research society in the last ten years because of its performance and its cost effectiveness hence many robust norm functions (both redescending and non-redescending influence functions) have been usually incorporated in the multi-frame SRR framework, which is combined a stochastic Bayesian...
As a useful technology of 3D reconstruction based on binocular stereo vision, structure from motion is widely used in many fields and highly valuable in applications. However, few reviews have been focused on this technology. In this paper, the basic principles are overviewed. More specifically, the related works and main methods are discussed. Finally some future research directions are summarized.
An algorithm for the robust detection and recognition of gestures for the interaction between human and a domestic floor cleaner robot is presented. The gestures are selected through a user study, in which the participants are asked to show natural gestures to the robot in given specific interaction scenarios. The gestures selected are those repeated by majority of participants and consist both commanding...
Recovering low-rank and sparse matrices from partial, incomplete or corrupted observations is an important problem in many areas of science and engineering. In this paper, we propose a scalable robust bilateral factorization (RBF) method to recover both structured matrices from missing and grossly corrupted data such as robust matrix completion (RMC), or incomplete and grossly corrupted measurements...
The target application of this paper is 3D scene reconstruction for future real-time production scenarios in the broadcast domain as well as future post-production and on-set visual effect previews in the digital cinema area. Our approach is based on multiple trifocal camera capture systems which can be arbitrarily distributed on set. In this work we tackle the problem of multi-view data fusion from...
Frequency domain Normalized Convolution (NC) process is widely performed on images to retrieve and extract valuable information in noisy and distorted environment. Genetic Normalized Convolution (GNC) is carried out for features extraction in an image or features reconstructions in a distorted image. In this paper a hybrid approach is adopted where robust algorithm of convolution based on Normalized...
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