Romulus Terebes
Technical University of Cluj-Napoca
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Publication
Featured researches published by Romulus Terebes.
international conference on pattern recognition | 2002
Romulus Terebes; Olivier Lavialle; Pierre Baylou; Monica Borda
This paper deals with the filtering and the enhancement of strongly oriented patterns. We propose a new filter combining scalar and tensor based diffusivities. The computation of an orientation confidence allows its to choose the best strategy to locally diffuse the gray levels. We show that this approach overcomes some drawbacks of the classical methods like corner smoothing or pinhole effect. The proposed method was applied on digital images of engravings. This type of images contain strongly oriented patterns and a large amount of details that have to be preserved during the diffusion.
Computerized Medical Imaging and Graphics | 2012
Bogdan Belean; Monica Borda; Bertrand Le Gal; Romulus Terebes
Automation is an open subject in DNA microarray image processing, aiming reliable gene expression estimation. The paper presents a novel shock filter based approach for automatic microarray grid alignment. The proposed method brings up significantly reduced computational complexity compared to state of the art approaches, while similar results in terms of accuracy are achieved. Based on this approach, we also propose an FPGA based system for microarray image analysis that eliminates the shortcomings of existing software platforms: user intervention, increased computational time and cost. Our system includes application-specific architectures which involve algorithm parallelization, aiming fast and automated cDNA microarray image processing. The proposed automated image processing chain is implemented both on a general purpose processor and using the developed hardware architectures as co-processors in a FPGA based system. The comparative results included in the last section show that an important gain in terms of computational time is obtained using hardware based implementations.
Medical & Biological Engineering & Computing | 2015
Bogdan Belean; Romulus Terebes; Adrian Bot
Abstract Microarray image processing is known as a valuable tool for gene expression estimation, a crucial step in understanding biological processes within living organisms. Automation and reliability are open subjects in microarray image processing, where grid alignment and spot segmentation are essential processes that can influence the quality of gene expression information. The paper proposes a novel partial differential equation (PDE)-based approach for fully automatic grid alignment in case of microarray images. Our approach can handle image distortions and performs grid alignment using the vertical and horizontal luminance function profiles. These profiles are evolved using a hyperbolic shock filter PDE and then refined using the autocorrelation function. The results are compared with the ones delivered by state-of-the-art approaches for grid alignment in terms of accuracy and computational complexity. Using the same PDE formalism and curve fitting, automatic spot segmentation is achieved and visual results are presented. Considering microarray images with different spots layouts, reliable results in terms of accuracy and reduced computational complexity are achieved, compared with existing software platforms and state-of-the-art methods for microarray image processing.
IEEE Transactions on Geoscience and Remote Sensing | 2008
Sorin Pop; Olivier Lavialle; Marc Donias; Romulus Terebes; Monica Borda; Sebastien Guillon; Noomane Keskes
We present a new method for the denoising and fusion, which is dedicated to multiazimuth seismic data. We propose to combine low-level fusion and diffusion processes through the use of a unique model based on partial differential equations. The denoising process is driven by the seismic fault preserving diffusion equation. Meanwhile, relevant information (as seismic faults) is injected in the fused 3-D images by an inverse diffusion process. One of the advantages of such an original approach is to improve the quality of the results in case of noisy inputs, which are frequently occurring in seismic unprocessed data. Some examples on synthetic and real seismic data will demonstrate the efficiency of our method.
e health and bioengineering conference | 2015
Raul Malutan; Romulus Terebes; Christian Germain; Monica Borda; Mihaela Cislariu
The paper proposes a method for ultrasound image denoising by using a classical signal processing method, i.e. Independent Component Analysis. The main idea is to process ultrasound images by the sparse code shrinkage algorithm based on ICA. We use the FastICA algorithm to estimate the inverse of the unknown mixing matrix and then apply the shrinkage operator for each determined independent component. The sparse code shrinkage method is compared with other speckle noise filtering algorithms and the results obtained show that sparse code shrinkage is a good method for multiplicative noise reduction in both test images and ultrasound images.
international conference on information fusion | 2007
Sorin Pop; Olivier Lavialle; Romulus Terebes; Monica Borda
In this paper, we present a new general method for image fusion based on partial differential equation (PDE). We propose to combine pixel-level fusion and diffusion processes through one single powerful equation. The insertion of the relevant information contained in sources is achieved in the fused image by reversing the diffusion process. To solve the well-known instability problem of an inverse diffusion process, we introduce two additional constraints. Then, we propose a general PDE, which integrates one of these constraints as a regularization term. One of the advantages of such an original approach is to improve the quality of the results in case of noisy input images. To answer to the requirements of a 3D specific fusion application, we also propose an extension of the general equation. Finally, few examples and comparisons with classical fusion models will demonstrate the efficiency of our method both on 2D and 3D cases.
advanced concepts for intelligent vision systems | 2007
Sorin Pop; Olivier Lavialle; Romulus Terebes; Monica Borda
In this paper, we present a new general method for image fusion based on Partial Differential Equation (PDE).We propose to combine pixel-level fusion and diffusion processes through one single powerful equation. The insertion of the relevant information contained in sources is achieved in the fused image by reversing the diffusion process. To solve the well-known instability problem of an inverse diffusion process, a regularization term is added. One of the advantages of such an original approach is to improve the quality of the results in case of noisy input images. Finally, few examples and comparisons with classical fusion models will demonstrate the efficiency of our method both on blurred and noisy images.
conference on computer as a tool | 2005
Sorin Pop; Romulus Terebes; Monica Borda; Olivier Lavialle; I. Voicu; Pierre Baylou
In this paper we propose a 3D filter for denoising and enhancing strongly oriented data. Our approach is based on 2D directional diffusion model proposed by Terebes et al. (2004). The diffusion process is modulated by a non-linear function depending on the absolute values of the directional derivatives in the 3 orthogonal directions of the space. These directions are the eigenvectors of a structure tensor. We compare this extension with an existing 3D extension designed by Dargent et al. (2004)
ieee international conference on automation, quality and testing, robotics | 2008
Romulus Terebes; Monica Borda; Christian Germain; Olivier Lavialle; Sorin Pop
This paper introduces a new method for directional diffusion based image filtering and enhancement. The main novelty of our approach consists in considering asymmetric orientation information for steering a diffusion process along the main directions of the constituent structures of an image. The diffusion process is obtained by generalizing an existing approach defined on a symmetric framework. The increased efficiency of the method for the preservation and enhancement of junctions and corners, coupled with good noise filtering capabilities, is illustrated through an experimental setup involving both synthetic and real images.
international conference on image processing | 2007
Sorin Pop; Romulus Terebes; Monica Borda; Sebastien Guillon; Naamen Keskes; Pierre Baylou; Olivier Lavialle
In order to aid the interpretation of seismic data, we present a new method for the denoising and fusion of multiple 3D registered blocks of the same area of subsoil. We propose to combine low-level fusion and diffusion processes through the use of a unique model based on partial differential equations (PDE). The denoising process is driven by the seismic fault enhancing diffusion equation. Meanwhile, relevant information (as seismic faults) is injected in the fused blocks by an inverse diffusion process. One of the advantages of such an original approach is to improve the quality of the results in case of noisy inputs, frequently occurring in seismic unprocessed data. Finally, two examples will demonstrate the efficiency of our method on synthetic and real seismic data.