Search results for "Computer Science::Computer Vision and Pattern Recognition"
showing 10 items of 193 documents
Multispectral image denoising with optimized vector non-local mean filter
2016
Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A …
Depth-Adapted CNN for RGB-D cameras
2020
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt …
Microstructure reconstruction using entropic descriptors
2009
A multi-scale approach to the inverse reconstruction of a pattern's microstructure is reported. Instead of a correlation function, a pair of entropic descriptors (EDs) is proposed for stochastic optimization method. The first of them measures a spatial inhomogeneity, for a binary pattern, or compositional one, for a greyscale image. The second one quantifies a spatial or compositional statistical complexity. The EDs reveal structural information that is dissimilar, at least in part, to that given by correlation functions at almost all of discrete length scales. The method is tested on a few digitized binary and greyscale images. In each of the cases, the persuasive reconstruction of the mic…
A genetic algorithm for image segmentation
2002
The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose, a fitness function, based on the similarity between images, has been defined. The similarity is a function of both the intensity and the spatial position of pixels. Preliminary results, obtained using real images, show a good performance of the segmentation algorithm.
Texture advection on discontinuous flows
2015
Texture advection techniques, which transport textures using a velocity field, are used to visualize the dynamics of a flow on a triangle mesh. Some flow phenomena lead to velocity fields with discontinuities that cause the deformation of the texture which is not properly controlled by these techniques. We propose a method to detect and visualize discontinuities on a flow, keeping consistent texture advection at both sides of the discontinuity. The method handles the possibility that the discontinuity travels across the domain of the flow with arbitrary velocity, estimating its speed with least-squares approximation. The technique is tested with different sample scenarios and with two avala…
Automatic detection of cardiac contours on MR Images using fuzzy logic and dynamic programming
1997
International audience; Abstract: This paper deals with the use of fuzzy logic and dynamic programming in the detection of cardiac contours in MR Images. The definition of two parameters for each pixel allows the construction of the fuzzy set of the cardiac contour points. The first parameter takes into account the grey level, and the second the presence of an edge. A corresponding fuzzy matrix is derived from the initial image. Finally, a dynamic programming with graph searching is performed on this fuzzy matrix. The method has been tested on several MR images and the results of the contouring were validated by an expert in the domain. This preliminary work clearly demonstrates the interes…
Different averages of a fuzzy set with an application to vessel segmentation
2005
Image segmentation is a major problem in image processing, particularly in medical image analysis. A great number of segmentation procedures produce intermediate gray-scale images that can be understood as fuzzy sets. Additionally, some segmentation procedures tend to leave free tuning parameters (very influential in the final binary image) for the user. These different binary images can be easily aggregated (into a fuzzy set) by making use of fuzzy set theory. In any case, a single binary image is required so our interest is to associate a crisp set to a given fuzzy set in an intelligent and unsupervised manner. The main idea of this paper is to define the averages of a given fuzzy set by …
Keypoint descriptor matching with context-based orientation estimation
2014
Abstract This paper presents a matching strategy to improve the discriminative power of histogram-based keypoint descriptors by constraining the range of allowable dominant orientations according to the context of the scene under observation. This can be done when the descriptor uses a circular grid and quantized orientation steps, by computing or providing a global reference orientation based on the feature matches. The proposed matching strategy is compared with the standard approaches used with the SIFT and GLOH descriptors and the recent rotation invariant MROGH and LIOP descriptors. A new evaluation protocol based on an approximated overlap error is presented to provide an effective an…
Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation
2018
Part 8: Pattern Recognition and Image Processing; International audience; Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear Conjugat…
A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra
2013
Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to ex…