Showing papers on "Image gradient published in 2005"

Vector filtering for color imaging

Abstract: Vector processing operations use essential spectral and spatial information to remove noise and localize microarray spots. The proposed fully automated vector technique can be easily implemented in either hardware or software; and incorporated in any existing microarray image analysis and gene expression tool.

Color2Gray: salience-preserving color removal

Abstract: Visually important image features often disappear when color images are converted to grayscale. The algorithm introduced here reduces such losses by attempting to preserve the salient features of the color image. The Color2Gray algorithm is a 3-step process: 1) convert RGB inputs to a perceptually uniform CIE L*a*b* color space, 2) use chrominance and luminance differences to create grayscale target differences between nearby image pixels, and 3) solve an optimization problem designed to selectively modulate the grayscale representation as a function of the chroma variation of the source image. The Color2Gray results offer viewers salient information missing from previous grayscale image creation methods.

Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation

Abstract: We present a novel variational approach for segmenting the image plane into a set of regions of parametric motion on the basis of two consecutive frames from an image sequence. Our model is based on a conditional probability for the spatio-temporal image gradient, given a particular velocity model, and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length. Exploiting the Bayesian framework, we derive a cost functional which depends on parametric motion models for each of a set of regions and on the boundary separating these regions. The resulting functional can be interpreted as an extension of the Mumford-Shah functional from intensity segmentation to motion segmentation. In contrast to most alternative approaches, the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional. Minimizing this functional with respect to its dynamic variables results in an eigenvalue problem for the motion parameters and in a gradient descent evolution for the motion discontinuity set. We propose two different representations of this motion boundary: an explicit spline-based implementation which can be applied to the motion-based tracking of a single moving object, and an implicit multiphase level set implementation which allows for the segmentation of an arbitrary number of multiply connected moving objects. Numerical results both for simulated ground truth experiments and for real-world sequences demonstrate the capacity of our approach to segment objects based exclusively on their relative motion.

Medical Images Edge Detection Based on Mathematical Morphology

Abstract: Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms

Detection and classification of edges in color images

Abstract: Up to now, most of the color edge detection methods are monochromatic-based techniques, which produce, in general, better than when traditional gray-value techniques are applied. In this overview, we focus mainly on vector-valued techniques because it is easy to understand how to apply common edge detection schemes to every color component. Opposed to this, vector-valued techniques are new and different. The second part of the article addresses the topic of edge classification. While edges are often classified into step edges and ramp edges, we address the topic of physical edge classification based on their origin into shadow edges, reflectance edges, orientation edges, occlusion edges, and specular edges. In the rest of this article we discuss various vector-valued techniques for detecting discontinuities in color images. Then operators are presented based on vector order statistics, followed by presentation by examples of a couple of results of color edge detection. We then discuss different approaches to a physical classification of edges by their origin.

Patch based blind image super resolution

Abstract: In this paper, a novel method for learning based image super resolution (SR) is presented. The basic idea is to bridge the gap between a set of low resolution (LR) images and the corresponding high resolution (HR) image using both the SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic framework. We show that in this framework, the estimation of the LR image formation parameters is straightforward. The whole framework is implemented via an annealed Gibbs sampling method. Experiments on SR on both single image and image sequence input show that the proposed method provides an automatic and stable way to compute super-resolution and the achieved result is encouraging for both synthetic and real LR images.

Globally Optimal Geodesic Active Contours

Abstract: An approach to optimal object segmentation in the geodesic active contour framework is presented with application to automated image segmentation. The new segmentation scheme seeks the geodesic active contour of globally minimal energy under the sole restriction that it contains a specified internal point pint. This internal point selects the object of interest and may be used as the only input parameter to yield a highly automated segmentation scheme. The image to be segmented is represented as a Riemannian space S with an associated metric induced by the image. The metric is an isotropic and decreasing function of the local image gradient at each point in the image, encoding the local homogeneity of image features. Optimal segmentations are then the closed geodesics which partition the object from the background with minimal similarity across the partitioning. An efficient algorithm is presented for the computation of globally optimal segmentations and applied to cell microscopy, x-ray, magnetic resonance and cDNA microarray images.

Super-Resolution Image Restoration from Blurred Low-Resolution Images

Abstract: In this paper, we study the problem of reconstructing a high-resolution image from several blurred low-resolution image frames. The image frames consist of decimated, blurred and noisy versions of the high-resolution image. The high-resolution image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, the high-resolution image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore the high-resolution image. Computer simulations are given to illustrate the effectiveness of the proposed method.

Method for deblurring an image

Abstract: A method for deblurring an image. A first image is captured. A second image is captured, wherein the second image is more blurred and more exposed than the first image. The second image is deblurred based on the first image.

Inpainting the colors

Abstract: A framework for automatic image colorization, the art of adding color to a monochrome image or movie, is presented in this paper. The approach is based on considering the geometry and structure of the monochrome luminance input, given by its gradient information, as representing the geometry and structure of the whole colored version. The color is then obtained by solving a partial differential equation that propagates a few color scribbles provided by the user or by side information, while considering the gradient information brought in by the monochrome data. This way, the color is inpainted, constrained both by the monochrome image geometry and the provided color samples. We present the underlying framework and examples for still images and movies.

Summary of image edge detection

Abstract: Edge is one of the most fundamental and significant features. Edge detection is always one of the most classical studying projects of computer vision and image processing field. The fist step of image analysis and understanding is edge detection. The goal of edge detection is to recover information about shapes and reflectance or transmittance in an image. It is one of the fundamental steps in image processing, mage analysis, image patter recognition, and computer vision, as well as in human vision. The correctness and reliability of its results affect directly the comprehension machine system made for objective world. The summary for basic edge detection methods was made. It involved the detection methods only but not filtering, edge location, analysis of algorithm complexity and functional evaluation about a detector.

System and method for GPU-based 3D nonrigid registration

Abstract: A method of registering two images using a graphics processing unit includes providing a pair of images with a first and second image, calculating a gradient of the second image, initializing a displacement field on the grid point domain of the pair of images, generating textures for the first image, the second image, the gradient, and the displacement field, and loading said textures into the graphics processing unit. A pixel buffer is created and initialized with the texture containing the displacement field. The displacement field is updated from the first image, the second image, and the gradient for one or more iterations in one or more rendering passes performed by the graphics processing unit.

Eliminating structure and intensity misalignment in image stitching

Abstract: The aim of this paper is to achieve seamless image stitching for eliminating obvious visual artifact caused by severe intensity discrepancy, image distortion and structure misalignment, given that the input images are globally registered. Our approach is based on structure deformation and propagation while maintaining the overall appearance affinity of the result to the input images. This new approach is proven to be effective in solving the above problems, and has found applications in mosaic deghosting, image blending and intensity correction. Our new method consists of the following main processes. First, salient features or structures are robustly detected and aligned along the optimal partitioning boundary between the input images. From these features, we derive sparse deformation vectors to to uniformly encode the underlying structure and intensity misalignment. These sparse deformation cues will then be propagated robustly and smoothly into the interior of the target image by solving the associated Laplace equations in the image gradient domain. We present convincing results to show that our method can handle significant structure and intensity misalignment in image stitching

A robust structure-adaptive hybrid vector filter for color image restoration

Abstract: A robust structure-adaptive hybrid vector filter is proposed for digital color image restoration in this paper. At each pixel location, the image vector (i.e., pixel) is first classified into several different signal activity categories by applying a modified quadtree decomposition to luminance component (image) of the input color image. A weight-adaptive vector filtering operation with an optimal window is then activated to achieve the best tradeoff between noise suppression and detail preservation. Through extensive simulation experiments conducted using a wide range of test color images, the filter has demonstrated superior performance to that of a number of well known benchmark techniques, in terms of both standard objective measurements and perceived image quality, in suppressing several distinct types of noise commonly considered in color image restoration, including Gaussian noise, impulse noise, and mixed noise.

Image-gradient-guided real-time stereo on graphics hardware

Abstract: We present a real-time correlation-based stereo algorithm with improved accuracy. Encouraged by the success of recent stereo algorithms that aggregate the matching cost based on color segmentation, a novel image-gradient-guided cost aggregation scheme is presented in this paper. The new scheme is designed to fit the architecture of recent graphics processing units (GPUs). As a result, our stereo algorithm can run completely on the graphics board: from rectification, matching cost computation, cost aggregation, to the final disparity selection. Compared with many real-time stereo algorithms that use fixed windows, noticeable accuracy improvement has been obtained without sacrificing realtime performance. In addition, existing global optimization algorithms can also benefit from the new cost aggregation scheme. The effectiveness of our approach is demonstrated with several widely used stereo datasets and live data captured from a stereo camera.

System and method for multi-label image segmentation

Abstract: A system and method for multi-label image segmentation is provided. The method comprises the steps of: receiving image data including a set of labeled image elements; mapping a change in intensities of the image data to edge weights; determining potentials for each image element in the image data; and assigning a label, based upon the determined potentials, to each image element in the image data.

Image processing apparatus, image processing method, and image display apparatus

Abstract: The frame-parallax-adjustment-amount generating unit outputs, as first parallax data, parallax data of an image portion protruded most among image portions protruded more than a first reference value from a pair of frame images forming a three-dimensional image. The pixel-parallax-adjustment-amount generating unit outputs, as second parallax data, parallax data of an image portion retracted more than a second reference value from the pair of frame images. The adjusted-image generating unit generates a pair of image output data by moving the entire pair of image input data to the inner side based on the first parallax data and moving an image portion retracted more than the second reference value of the pair of image input data based on the second parallax data to adjust a parallax amount and outputs the pair of image output data.

Automatic color palette

Abstract: Color palettes are an important tool for color image analysis, since they are the initial point of different techniques such as quantization or indexing. This paper presents a new method for the automatic construction of a color palette, which adjusts dynamically its number of colors according to the visual content of the image. The method is based on appropriately segmenting the HSI color space, which is achieved by individually partitioning the histograms associated to each color component. As a result we obtain a hierarchical color palette, which represents the color image with a reduced number of colors.

A contour based image segmentation algorithm using morphological edge detection

Abstract: In this paper, a novel approach for edge-based image segmentation is proposed. Image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, it's always a tough task to partition an object in a graph-based image. We proposed an image segmentation algorithm by integrating mathematical morphological edge detector with region growing technique. The images are first enhanced by morphological closing operations, and then detect the edge of the image by morphological dilation residue edge detector. Moreover, we deploy growing seeds into the edge image that obtained by the edge detection procedure. By cross comparing the growing result and the detected edges, the partition lines of the image are generated. In this paper, we presented the theoretical backgrounds and procedure illustrations of the proposed algorithm. Furthermore, the proposed algorithm is implemented in C++ language and evaluate on several images with promising results.

Color Lesion Boundary Detection Using Live Wire

Abstract: The boundaries of oral lesions in color images were detected using a live-wire method and compared to expert delineations. Multiple cost terms were analyzed for their inclusion in the final total cost function including color gradient magnitude, color gradient direction, Canny edge detection, and Laplacian zero crossing. The gradient magnitude and direction cost terms were implemented so that they acted directly on the three components of the color image, instead of using a single derived color band. The live-wire program was shown to be considerably more accurate and faster compared to manual segmentations by untrained users.

Image processing method, image processing apparatus, and computer program used therewith

Abstract: An image processing method and apparatus performs calculating flash-component image data based on a first image captured without flash emission and a second image captured with flash emission, and generating a finally-adjusted image by using an intensity-adjusted flash-component image generated by executing intensity adjustment on the flash-component image data. The method and apparatus can generate a high quality image in which saturated pixels in a nonlinear transformation image, that is, overexposed highlights are reduced.

Gradient based image completion by solving poisson equation

Abstract: Image completion is a method to fill the missing portions of an image caused by the removal of one or more foreground or background elements. In this paper a novel image completion algorithm is proposed for removing significant objects from natural images or photographs. The completion is realized in the following three steps. First, a gradient-based model is presented to determine the gradient-patch filling order. This step is critical because a better filling order can improve the continuation of image structures. Second, we implement the gradient-patch update strategy by measuring the exponential distance between the source patch and the target one in gradient domain. In order to find a better patch matching and propagating algorithm, we incorporate the gradient and color information together to determine the target patch. Third, a complete image is achieved by solving the Poisson equation with the updated image gradient map. Some experimental results on real-scene photographs are given to demonstrate both the efficiency and image equality of our novel method.

Processes, products and systems for enhancing images of blood vessels

Abstract: Processes, program products, and systems for enhancing images. The process includes the steps of padding image data to produce padded image data, transforming the padded image data to produce transformed image data, smoothing a substantial portion of the transformed image data, re-transforming the transformed image data to produce smoothed image data, inverting the smoothed image data to generate inverted image data, and adding the inverted image data to unenhanced image data to generate a result image.

Multivariate entropy detector based hybrid image registration

Abstract: Image registration is used to match two images for spatial alignment and intensity alignment. One of the possible applications of image registration is for the evaluation of a printed image with respect to a given reference image. We propose a new hybrid image registration algorithm to identify the spatial or intensity variations between two color images. The proposed approach extracts salient descriptors from the two images using a multivariate entropy-based detector. The transformation parameters are obtained after establishing the correspondence between the salient descriptors of the two images, which yields better accuracy and lesser computational cost compared to the approaches present in the literature.

Method and apparatus for enhancing flash and ambient images

Abstract: A method and system generate an enhanced output image. A first image is acquired of a scene illuminated by a first illumination condition. A second image is acquired of the scene illuminated by a second illumination condition. First and second gradient images are determined from the first and second images. Orientations of gradients in the first and second gradient images are compared to produce a combined gradient image, and an enhanced output image is constructed from the combined gradient image.

Image generation apparatus, image generation system and image synthesis method

Abstract: A surveillance system includes an image deformation unit for deforming the image captured or picked up by a surveillance camera so that the appearance of a specified region within this image has the same geometric shape as that of its corresponding region on the map, and an image synthesis display unit operable to extract the specific region of the deformed image and synthesize it in the corresponding map region for on-screen visualization, obtaining the mutual relationship of a plurality of images.

Forming a single image from overlapping images

Abstract: Forming a single image from multiple images is described. A first image and a second image partially overlap to define a common overlap region, and each image has multiple pixels. A boundary between the first image and the second image is automatically calculated based on processed pixel values in the common overlap region. Then the first and second image may be integrated along the boundary to form a single image.