Showing papers on "Image gradient published in 2007"

Image upsampling via imposed edge statistics

Abstract: In this paper we propose a new method for upsampling images which is capable of generating sharp edges with reduced input-resolution grid-related artifacts. The method is based on a statistical edge dependency relating certain edge features of two different resolutions, which is generically exhibited by real-world images. While other solutions assume some form of smoothness, we rely on this distinctive edge dependency as our prior knowledge in order to increase image resolution. In addition to this relation we require that intensities are conserved; the output image must be identical to the input image when downsampled to the original resolution. Altogether the method consists of solving a constrained optimization problem, attempting to impose the correct edge relation and conserve local intensities with respect to the low-resolution input image. Results demonstrate the visual importance of having such edge features properly matched, and the method's capability to produce images in which sharp edges are successfully reconstructed.

Content Based Image Retrieval Using Color, Texture and Shape Features

Abstract: Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. This paper presents a novel framework for combining all the three i.e. color, texture and shape information, and achieve higher retrieval efficiency. The image is partitioned into non- overlapping tiles of equal size. The color moments and moments on Gabor filter responses of these tiles serve as local descriptors of color and texture respectively. This local information is captured for two resolutions and two grid layouts that provide different details of the same image. An integrated matching scheme, based on most similar highest priority (MSHP) principle and the adjacency matrix of a bipartite graph formed using the tiles of query and target image, is provided for matching the images. Shape information is captured in terms of edge images computed using gradient vector flow fields. Invariant moments are then used to record the shape features. The combination of the color, texture and shape features provide a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.

Image encoding/decoding method and apparatus

Abstract: Provided are an image encoding/decoding method and apparatus, in which one of a plurality of color component images is predicted from a different color component image reconstructed using a correlation between the plurality of color component images. Using a reconstructed image of a first color component image selected from among the plurality of color component images forming a single color image, the other color component images are predicted.

The Fast Multilevel Fuzzy Edge Detection of Blurry Images

Abstract: To realize the fast and accurate detection of the edges from the blurry images, the fast multilevel fuzzy edge detection (FMFED) algorithm is proposed. The FMFED algorithm first enhances the image contrast by means of the fast multilevel fuzzy enhancement (FMFE) algorithm using the simple transformation function based on two image thresholds. Second, the edges are extracted from the enhanced image by the two-stage edge detection operator that identifies the edge candidates based on the local characteristics of the image and then determines the true edge pixels using the edge detection operator based on the extremum of the gradient values. Experimental results demonstrate that the FMFED algorithm can extract the thin edges and remove the false edges from the image, which leads to its better performance than the Sobel operator, Canny operator, traditional fuzzy edge detection algorithm, and other multilevel fuzzy edge detection algorithms

Perception-based contrast enhancement of images

Abstract: Study of contrast sensitivity of the human eye shows that our suprathreshold contrast sensitivity follows the Weber Law and, hence, increases proportionally with the increase in the mean local luminance. In this paper, we effectively apply this fact to design a contrast-enhancement method for images that improves the local image contrast by controlling the local image gradient with a single parameter. Unlike previous methods, we achieve this without explicit segmentation of the image, either in the spatial (multiscale) or frequency (multiresolution) domain. We pose the contrast enhancement as an optimization problem that maximizes the average local contrast of an image strictly constrained by a perceptual constraint derived directly from the Weber Law. We then propose a greedy heuristic, controlled by a single parameter, to approximate this optimization problem.

Color Image Enhancement Based on Single-Scale Retinex With a JND-Based Nonlinear Filter

Abstract: In this paper, we propose a color image enhancement based on the single-scale retinex (SSR) with a just noticeable difference (JND)-based nonlinear filter. In the proposed method, an input RGB color image is transformed into an HSV color image. Under the assumption of white-light illumination, the S and V component images are enhanced. In the enhancement of the V component image, the illumination is first estimated using the JND-based nonlinear filter. The output V component image is then obtained by subtracting some portion of the log signal of the estimated illumination from the log signal of the input V component image. The histogram modeling is next applied to the output V component image. The S component image is enhanced in proportion to the enhanced ratio of the V component image. Finally an output RGB color image is obtained from the enhanced V and S component images along with the original H component image. Experimental results show that the proposed method yields better performance of color enhancement over the conventional histogram equalization and SSR for test color images.

Method for generating a high-resolution virtual-focal-plane image

Abstract: The present invention provides a method for generating a high-resolution virtual-focal-plane image which is capable of generating a virtual-focal-plane image with an arbitrary desired resolution simply and rapidly by using multi-view images. The method of the present invention comprises a disparity estimating process step for estimating disparities by performing the stereo matching for multi-view images consisting of multiple images with different capturing positions and obtaining a disparity image; a region selecting process step for selecting an image among multi-view images as a basis image, setting all remaining images as reference images, and selecting a predetermined region on the basis image as a region of interest; a virtual-focal-plane estimating process step for estimating a plane in the disparity space for the region of interest based on the disparity image, and setting the estimated plane as a virtual-focal-plane; and an image integrating process step for obtaining image deformation parameters used for deforming each reference image to the basis image for the virtual-focal-plane, and generating the virtual-focal-plane image by deforming multi-view images with obtained image deformation parameters.

An Improved Canny Algorithm for Edge Detection

Abstract: Because of the high similarity between the gradient magnitudes of low-contrast edge and noise,the conventional Canny algorithm for detection,which is based on the double-threshold to detect and connect edges,damages partly the low-contrast edge though it suppresses noise.An improved Canny algorithm is therefore proposed instead of the double-threshold one,which detects and connects the edges in accordance to the difference of the gradient direction between the edge and the noise,so as to protect efficiently the details of all low-contrast edges with the noise suppressed simultaneously.The new approach is thus superior to the conventional one.Simulation tests have verified the effectiveness of the improved Canny algorithm for edge detection.

Segmentation-based image processing system

Abstract: A digital image can be processed by an image processing method that calculates a gradient map for the digital image, calculates a density function for the gradient map, calculates a modified gradient map using the gradient map, the density function and the selected scale level, and segments the modified gradient map. Prior to segmenting the modified gradient map, a sub-image of the digital image can be segmented at the selected scale level to determine if the selected scale level will give the desired segmentation.

Unsupervised Segmentation of Objects using Efficient Learning

Abstract: We describe an unsupervised method to segment objects detected in images using a novel variant of an interest point template, which is very efficient to train and evaluate. Once an object has been detected, our method segments an image using a conditional random field (CRF) model. This model integrates image gradients, the location and scale of the object, the presence of object parts, and the tendency of these parts to have characteristic patterns of edges nearby. We enhance our method using multiple unsegmented images of objects to learn the parameters of the CRF, in an iterative conditional maximization framework. We show quantitative results on images of real scenes that demonstrate the accuracy of segmentation.

Detail preserving shape deformation in image editing

Abstract: Shape deformation is a common practice in digital image editing, but can unrealistically stretch or compress texture detail. We propose an image editing system that decouples feature position from pixel color generation, by resynthesizing texture from the source image to preserve its detail and orientation around a new feature curve location. We introduce a new distortion to patch-based texture synthesis that aligns texture features with image features. A dense correspondence field between source and target images generated by the control curves then guides texture synthesis.

Color-image reproduction apparatus

Abstract: A color-image reproduction apparatus reproduces a color image on the basis of digital image data which are obtained through detection of an image by visible light detection elements and an infrared detection element. Visible image data are extracted from the image data, and infrared-containing image data are extracted from the image data. Brightness information is extracted from the visible image data or infrared-containing image data. An infrared component is removed from the visible image data. Color information is extracted from the visible image data from which the infrared component has been removed. The brightness information and the color information are combined so as to generate a quasi color image.

Content-based image retrieval using joint correlograms

Abstract: The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.

A Coupled Minimization Problem for Medical Image Segmentation with Priors

Abstract: We present a coupled minimization problem for image segmentation using prior shape and intensity profile. One part of the model minimizes a shape related energy and the energy of geometric active contour with a parameter that balances the influence from these two. The minimizer corresponding to a fixed parameter in this minimization gives a segmentation and an alignment between the segmentation and prior shape. The second part of this model optimizes the selection of the parameter by maximizing the mutual information of image geometry between the prior and the aligned novel image over all the alignments corresponding to different parameters in the first part. By this coupling the segmentation arrives at higher image gradient, forms a shape similar to the prior, and captures the prior intensity profile. We also propose using mutual information of image geometry to generate intensity model from a set of training images. Experimental results on cardiac ultrasound images are presented. These results indicate that the proposed model provides close agreement with expert traced borders, and the parameter determined in this model for one image can be used for images with similar properties.

Three-dimensional image display apparatus and method for enhancing stereoscopic effect of image

Abstract: A three-dimensional (3D) image display apparatus for enhancing a stereoscopic effect of an image is provided. The 3D image display apparatus includes a disparity estimator which estimates the disparity between a first image and a second image which are obtained by photographing the same object from different angles; a computing unit which computes the adjustment disparity between the first image and the second image using a histogram obtained by computing the frequency of the estimated disparity; and an output unit which applies the computed adjustment disparity to the first image and the second image and outputs the first image and the second image in which the disparity is adjusted. Therefore, the input disparity between the first image and the second image is adjusted, and an image with an enhanced stereoscopic effect may be provided to a user.

Arbitrary view generation based on DIBR

Abstract: An efficient depth image based rendering (DIBR) for generating arbitrary novel views is proposed. The proposed method fills the holes by preprocessing depth images and merging two desired images. In order to solve the visibility problem, the pixels in reference image are processed in an occlusion-compatible order. Furthermore, the reference images are corrected before 3D image warping so as to reduce the edge artifacts. Experimental results have show that the proposed method can provide a satisfactory image quality.

Adaptable model-based tracking using analysis-by-synthesis techniques

Abstract: In this paper we present a novel analysis-by-synthesis approach for real-time camera tracking in industrial scenarios. The camera pose estimation is based on the tracking of line features which are generated dynamically in every frame by rendering a polygonal model and extracting contours out of the rendered scene. Different methods of the line model generation are investigated. Depending on the scenario and the given 3D model either the image gradient of the frame buffer or discontinuities of the z-buffer and the normal map are used for the generation of a 2D edge map. The 3D control points on a contour are calculated by using the depth value stored in the z-buffer. By aligning the generated features with edges in the current image, the extrinsic parameters of the camera are estimated. The camera pose used for rendering is predicted by a line-based frame-to-frame tracking which takes advantage of the generated edge features. The method is validated and evaluated with the help of ground-truth data as well as real image sequences.

Vision based navigation and guidance system

Abstract: A method of controlling an autonomous vehicle with a vision-based navigation and guidance system The method includes capturing image frames at a predetermined frame rate Detecting image points that define an edge in each image frame Using image points in each image frame to locate a determined edge Dynamically predicting the location of image points that define an edge in a subsequent image frame Using the prediction of the location of image points that define an edge to narrow the search area for the edge in the subsequent image frame Determining offset distances between determined edges in sequential image frames and controlling the vehicle based at least in part on determined offset distances between determined edges in sequential image frames

Localized and highly constrained image reconstruction method

Abstract: An image reconstruction method includes reconstructing a composite image of a subject using a conventional reconstruction method. The composite image employs the best information available regarding the subject of the scan and this information is used to constrain the reconstruction of a highly undersampled image frames or improve the SNR of image frames. A blurred and normalized weighting image is produced from image frame data, and this normalized weighting image is multiplied by the composite image.

An Edge Detection Algorithm Based on Improved CANNY Operator

Abstract: Edge detection is an important aspect for image processing, and primary step of spatial data extraction in geography information system. For contour detection, this paper proposed the improved template algorithm, which is not only including the gradient directions of X and Y, but also the first order partial finite differences of directions 45 and 135 degree in calculating the amplitude values. These mostly improved the calculation accuracy of the amplitude values. In the non-maxima suppression process, the factor ratio of four quadrants of linear interpolation is improved to achieve better detection results. Experiments showed that this improved CANNY algorithm has better noise suppression and edge continuity.

Chrominance Edge Preserving Grayscale Transformation with Approximate First Principal Component for Color Edge Detection

Abstract: Edges that are visible in color images may not be detected in the corresponding grayscale image. This is due to the neighboring objects having different hues but the same intensities. Hence, a color edge preserving grayscale conversion algorithm is proposed that helps detect color edges using only the luminance component. The algorithm calculates an approximation to the first principal component to form a new set of luminance coefficients instead of using the conventional luminance coefficients. This method can be directly applied to all existing grayscale edge detectors for color edge detection. Processing only one channel instead of three channels results in lower computational complexity compared to other color edge detectors. Experimental results on test images show similar edge detection capabilities to typical color edge detectors at reduced complexity levels.

Color-based clustering for text detection and extraction in image

Abstract: This paper proposes a new approach for the text detection and extraction in image. The novelty of our approach mainly lies in the color-based clustering into two phases: In text detection phase, we consider jointly the two significant features of text regions in image: homogeneous color and sharp edges, and color-based clustering is employed to decompose the color edge map of image into several edge maps, which makes the text detection of image more accurate. In text extraction phase, on one hand, for effective text recognition, we consider the color difference between the text and background in image, and color-based clustering is utilized to remove image noise. Another hand, for effective binarization of text region, instead of performing binarization in a constant color plane as in the existing methods, our approach can adaptively select the best color plane according to the text contrast difference among color planes for binarization. Experimental results show our approach is better than the existing methods.

Anisotropic Continuous-Scale Morphology

Abstract: We describe a new approach to incorporate adaptivity into the partial differential equations (PDEs) of morphological dilation and erosion. By multiplication of the image gradient with a space-variant matrix, the speed of the evolution is locally adapted to the data. This is used to create anisotropic morphological evolutions that enhance coherent, flow-like image structures. We show that our adaptive method can be implemented by means of a simple modification of the classical Rouy-Tourin finite difference scheme. Numerical experiments confirm that the proposed dilations and erosions are capable of real anisotropic behaviour that can be used for closing interrupted lines.

Apparatus for and method of processing digital image

Abstract: An apparatus for and method of processing a digital image are provided. The apparatus for processing a digital image includes: a detection unit which detects the direction or a magnitude of an edge of an input image signal; and an edge enhancement unit which determines the degree of enhancing the edge based on the detected direction or the detected magnitude of the edge, and enhances the edge of the input image signal according to the determined degree of enhancement.

Fast Extraction of Edge Histogram in DCT Domain based on MPEG7

Abstract: Due to the decoding procedure of JPEG image and filtering for edge detection, the feature extraction process of MPEG-7 Edge Histogram Descriptor (EHD) is time consuming as well as computationally expensive. To improve efficiency of compressed image retrieval, we propose a new edge histogram generation algorithm in DCT domain in this paper. Using edge information provided by the AC coefficients of DCT coefficients, we can get the direction and strength, which is utilized for the edge histogram generation, of the edge directly in DCT domain. The experimental results demonstrate that our system has good performance in terms of retrieval efficiency and effectiveness.

Similarity relation matrix-based color edge detection

Abstract: A novel edge detection algorithm for color images was described in this paper. In the proposed method, smoothness of each pixel in color image is firstly calculated by means of similarity relation matrix and is normalized to maximum gray level. In other words, color image in three-dimensional color spaces is mapped into one dimension. Accordingly the edges are performed in such a way that pixels lower than thresholds are assigned to be edge. Thus with proposed method, edge pixels in a color image are detected simultaneously without any complex calculations such as gradient, Laplace and statistical calculations.

Control Camera and Light Source Positions using Image Gradient Information

Abstract: In this paper, we propose an original approach to control camera position and/or lighting conditions in an environment using image gradient information. Our goal is to ensure a good viewing condition and good illumination of an object to perform vision-based task (recognition, tracking, etc.). Within the visual servoing framework, we propose solutions to two different issues: maximizing the brightness of the scene and maximizing the contrast in the image. Solutions are proposed to consider either a static light and a moving camera, either or a moving light and a static/moving camera. The proposed method is independent of the structure, color and aspect of the objects. Experimental results on both synthetic and real images are finally presented.