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Showing papers on "Image gradient published in 2009"


Proceedings ArticleDOI
01 Jan 2009
TL;DR: The Sobel operator performs a 2-D spatial gradient measurement on images to enhance the removal of redundant data, as a result, reduction of the amount of data is required to represent a digital image.
Abstract: Image edge detection is a process of locating the e dge of an image which is important in finding the approximate absolute gradient magnitude at each point I of an input grayscale image. The problem of getting an appropriate absolute gradient magnitude for edges lies in the method used. The Sobel operator performs a 2-D spatial gradient measurement on images. Transferring a 2-D pixel array into statistically uncorrelated data se t enhances the removal of redundant data, as a result, reduction of the amount of data is required to represent a digital image. The Sobel edge detector uses a pair of 3 x 3 convolution masks, one estimating gradient in the x-direction and the other estimating gradient in y‐direction. The Sobel detector is incredibly sensit ive to noise in pictures, it effectively highlight them as edges. Henc e, Sobel operator is recommended in massive data communication found in data transfer.

421 citations


Journal ArticleDOI
Taiping Zhang1, Yuan Yan Tang1, Bin Fang1, Zhaowei Shang1, Xiaoyu Liu1 
TL;DR: Theoretical analysis and experimental results validate that gradient faces is an illumination insensitive measure, and robust to different illumination, including uncontrolled, natural lighting, and is also insensitive to image noise and object artifacts.
Abstract: In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the gradient faces. Theoretical analysis shows gradient faces is an illumination insensitive measure, and robust to different illumination, including uncontrolled, natural lighting. In addition, gradient faces is derived from the image gradient domain such that it can discover underlying inherent structure of face images since the gradient domain explicitly considers the relationships between neighboring pixel points. Therefore, gradient faces has more discriminating power than the illumination insensitive measure extracted from the pixel domain. Recognition rates of 99.83% achieved on PIE database of 68 subjects, 98.96% achieved on Yale B of ten subjects, and 95.61% achieved on Outdoor database of 132 subjects under uncontrolled natural lighting conditions show that gradient faces is an effective method for face recognition under varying illumination. Furthermore, the experimental results on Yale database validate that gradient faces is also insensitive to image noise and object artifacts (such as facial expressions).

406 citations


Journal ArticleDOI
TL;DR: The comparative analysis of various Image Edge Detection methods is presented and it has been shown that the Canny's edge detection algorithm performs better than all these operators under almost all scenarios.
Abstract: —Edges characterize boundaries and are therefore considered for prime importance in image processing. Edge detection filters out useless data, noise and frequencies while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection methods. In this paper the comparative analysis of various Image Edge Detection methods is presented. The evidence for the best detector type is judged by studying the edge maps relative to each other through statistical evaluation. Upon this evaluation, an edge detection method can be employed to characterize edges to represent the image for further analysis and implementation. It has been shown that the Canny’s edge detection algorithm performs better than all these operators under almost all scenarios. Index Terms —About four key words or phrases in alphabetical order, separated by commas. I. I NTRODUCTION

200 citations


Journal ArticleDOI
TL;DR: A new unsupervised color image segmentation algorithm is proposed, which exploits the information obtained from detecting edges in color images in the CIE L*a*b* color space to identify some initial portion of the input image content.
Abstract: Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L*a*b* color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.

179 citations


Journal ArticleDOI
TL;DR: This paper presents an algorithm that extracts robust feature descriptors from 2.5D range images, in order to provide accurate point-based correspondences between compared range surfaces, inspired by the SIFT.

144 citations


Proceedings ArticleDOI
07 Nov 2009
TL;DR: A color image splicing detection method based on gray level co-occurrence matrix (GLCM) of thresholded edge image of image chroma is proposed and the effectiveness of the proposed method has been demonstrated by the experimental results.
Abstract: A color image splicing detection method based on gray level co-occurrence matrix (GLCM) of thresholded edge image of image chroma is proposed in this paper. Edge images are generated by subtracting horizontal, vertical, main and minor diagonal pixel values from current pixel values respectively and then thresholded with a predefined threshold T. The GLCMs of edge images along the four directions serve as features for image splicing detection. Boosting feature selection is applied to select optimal features and Support Vector Machine (SVM) is utilized as classifier in our approach. The effectiveness of the proposed method has been demonstrated by our experimental results.

139 citations


Proceedings ArticleDOI
28 Oct 2009
TL;DR: An improved canny algorithm with self-adaptive filter is used to replace the Gaussian filter, morphological thinning is adopted to thin the edge and morphological operator is used for refining treatment of edge points detection and the single pixel level edge.
Abstract: CANNY arithmetic operator has been proved to have good detective effect in the common usage of edge detection However, CANNY operator also has certain deficiencies Based on the analysis of the traditional CANNY algorithm, an improved canny algorithm is proposed in this paper In the algorithm, self-adaptive filter is used to replace the Gaussian filter, morphological thinning is adopted to thin the edge and morphological operator is used to achieved the refining treatment of edge points detection and the single pixel level edge The results of experiment show the improved CANNY algorithm is reasonable

132 citations


Proceedings ArticleDOI
12 Sep 2009
TL;DR: This paper compares the main approaches of partitioning an image into regions by using gray values and concludes that the edge detection has advantage of not necessarily needing closed boundaries and also its computation is based on difference.
Abstract: This paper, we will review the main approaches of partitioning an image into regions by using gray values in order to reach a correct interpretation of the image. We mainly compare the region-based segmentation with the boundary estimation using edge detection. Image segmentation is an important step for many image processing and computer vision algorithms while an edge can be described informally as the boundary between adjacent parts of an image. A formal definition is elusive, but edge detection is nonetheless a useful and ubiquitous image processing task. After comparing we have come to a conclusion that the edge detection has advantage of not necessarily needing closed boundaries and also its computation is based on difference. The region-segmentation in spite of improving multi-spectral images has the drawback of being applied only on closed boundaries. To reach the result of edge detection we have used the technique of performance metrics and Canny edge detection. We have applied Canny ground truth to acquire more features via displaying more details.

130 citations


Proceedings ArticleDOI
11 Apr 2009
TL;DR: A new automatic threshold algorithm for images processing based on Genetic Algorithms and improved Sobel operator is proposed and the comparative experiment results show that the new algorithm of automatic threshold is very effective and better than the classical Otsu methods.
Abstract: Edge detection of images is a classical problem in computer vision and image processing. The key of edge detection is the choice of threshold; the choice of threshold directly determines the results of edge detection. How to automatically determine an optimal threshold is one of difficult points of edge detection. In this paper, Sobel edge detection operator and its improved algorithm are first discussed in term of optimal thresholding. Then based on Genetic Algorithms and improved Sobel operator, a new automatic threshold algorithm for images processing is proposed. Finally, the edge detection experiments of two real images are conducted by means of two algorithms. The comparative experiment results show that the new algorithm of automatic threshold is very effective. The results are also better than the classical Otsu methods.

122 citations


Journal ArticleDOI
TL;DR: The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies.
Abstract: This paper presents a novel fast model for active contours to detect objects in an image, based on techniques of curve evolution The proposed model can detect objects whose boundaries are not necessarily defined by gradient, based on the minimization of a fuzzy energy, which can be seen as a particular case of a minimal partition problem This fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary However, the stopping term does not depend on the gradient of the image, as most of the classical active contours, but instead is related to the image color and spatial segments The fuzziness of the energy provides a balanced technique with a strong ability to reject ldquoweakrdquo local minima Moreover, this approach converges to the desired object boundary very fast, since it does not solve the Euler-Lagrange equations of the underlying problem, but, instead, calculates the fuzzy energy alterations directly The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies

115 citations


Proceedings ArticleDOI
07 Nov 2009
TL;DR: This paper presents a simple yet effective approach for estimating a defocus blur map based on the relationship of the contrast to the image gradient in a local image region, called the local contrast prior.
Abstract: Image defocus estimation is useful for several applications including deblurring, blur magnification, measuring image quality, and depth of field segmentation. In this paper, we present a simple yet effective approach for estimating a defocus blur map based on the relationship of the contrast to the image gradient in a local image region. We call this relationship the local contrast prior. The advantage of our approach is that it does not require filter banks or frequency decomposition of the input image; instead we only need to compare local gradient profiles with the local contrast. We discuss the idea behind the local contrast prior and demonstrate its effectiveness on a variety of experiments.

Patent
Dong Kyung Nam1, Yun-Tae Kim1, Du-sik Park1, Gee Young Sung1, Juyong Park1 
30 Apr 2009
TL;DR: In this article, a display apparatus and a method that may display a high depth 3D image is described, where the display method may separate an input image into a near-sighted image and a farsighted image, and image and output the nearsighted image using a light field method.
Abstract: A display apparatus and method that may display a high depth three-dimensional (3D) image is provided. The display method may separate an input image into a near-sighted image and a far-sighted image, image and output the near-sighted image using a light field method, and image and output the far-sighted image using a multi-view method.

Patent
Koshi Hatakeyama1
28 Sep 2009
TL;DR: In this article, the image processing method processes an image generated by image pickup using an optical system (100), which includes a step of acquiring the image, an image restoration step (S2-S7) of performing image restoration processing to reduce a blur component of the image using an image restore filter, and a distortion correction step(S8) of applying geometric transformation processing on the image on which the image restoration has been performed.
Abstract: The image processing method processes an image generated by image pickup using an optical system (100). The method includes a step of acquiring the image, an image restoration step (S2-S7) of performing image restoration processing to reduce a blur component of the image using an image restoration filter, and a distortion correction step (S8) of performing geometric transformation processing to reduce a distortion component of the image on which the image restoration processing has been performed.

Proceedings ArticleDOI
01 Nov 2009
TL;DR: A new segmentation method which is based on the morphology method, fuzzy K-means algorithm and some parts operator of the Canny algorithm, and the course of Canny operator that calculating the value and direction of grads, non-maxima suppression to the grad value and lag threshold process into the post-treatment process is introduced.
Abstract: On the basis of analyzing the blur images with noise, this paper presents a new segmentation method which is based on the morphology method, fuzzy K-means algorithm and some parts operator of the Canny algorithm. Because of the Canny's good performance on good detection, good localization and only one response to a single edge, we introduce the course of Canny operator that calculating the value and direction of grads, non-maxima suppression to the grad value and lag threshold process into our post-treatment process. Through experiments, it is demonstrated that the image segmentation method in this paper is very effective.

Proceedings ArticleDOI
30 Oct 2009
TL;DR: A Contrast Limited Adaptive Histogram Equalization (CLAHE)-based method that establishes a maximum value to clip the histogram and redistributes the clipped pixels equally to each gray-level can limit the noise while enhancing the image contrast.
Abstract: The images degraded by fog suffer from poor contrast. In order to remove fog effect, a Contrast Limited Adaptive Histogram Equalization (CLAHE)-based method is presented in this paper. This method establishes a maximum value to clip the histogram and redistributes the clipped pixels equally to each gray-level. It can limit the noise while enhancing the image contrast. In our method, firstly, the original image is converted from RGB to HSI. Secondly, the intensity component of the HSI image is processed by CLAHE. Finally, the HSI image is converted back to RGB image. To evaluate the effectiveness of the proposed method, we experiment with a color image degraded by fog and apply the edge detection to the image. The results show that our method is effective in comparison with traditional methods.

01 Jan 2009
TL;DR: From the exprimental result, the Otsu algorithm can be applied in choosing the threshold value which can be used in Canny algorithm, and this method improves the effect of extracting the edge of the Canny algorithms, and achieves the expect result finally.
Abstract: Canny algorithm can be used in extracting the object's contour clearly by setting the appropriate parameters. The Otsu algorithm can calculate the high threshold value which is significant to the Canny algorithm, and then this threshold value can be used in the Canny algorithm to detect the object's edge. From the exprimental result, the Otsu algorithm can be applied in choosing the threshold value which can be used in Canny algorithm, and this method improves the effect of extracting the edge of the Canny algorithm, and achieves the expect result finally. Index Terms—image segmentation; Otsu; Canny; threshold; edge detection

Proceedings ArticleDOI
27 Oct 2009
TL;DR: The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.
Abstract: Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A color image edge detection algorithm is proposed in this paper. Average maximum color difference value is used to predict the optimum threshold value for a color image and thinning technique is applied to extract proper edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

Journal ArticleDOI
TL;DR: An adaptive image thresholding technique via minimax optimization of a novel energy functional that consists of a non-linear convex combination of an edge sensitive data fidelity term and a regularization term that shows promising results to preserve edge/texture structures in different benchmark images over other competing methods is introduced.

Patent
16 Nov 2009
TL;DR: In this paper, a video image capture component includes a light source operable in a first spectrum, a first image detector operating in a second spectrum, and a second image detector operated in the second spectrum.
Abstract: A video image capture component includes a light source operable in a first spectrum, a first image detector operable in the first spectrum, a second light source operable in a second spectrum, and a second image detector operable in the second spectrum. A filtering component generates a combination image by filtering a first image obtained by the first image detector with a high-contrast filter, resulting in a high-contrast image, and masking a second image obtained by the second image detector using the high-contrast image. A compositing component creates a composite image from the combination image and a selected image. A display component displays the composite image. Alternative systems and methods for creating a combination image include techniques involving thermal imaging, laser detection, and narrow band frequency detection.

Book ChapterDOI
14 Jul 2009
TL;DR: A two-stage graph cut based model for segmentation of touching cell nuclei in fluorescence microscopy images that can be easily balanced using a single user parameter is presented.
Abstract: Methods based on combinatorial graph cut algorithms received a lot of attention in the recent years for their robustness as well as reasonable computational demands These methods are built upon an underlying Maximum a Posteriori estimation of Markov Random Fields and are suitable to solve accurately many different problems in image analysis, including image segmentation In this paper we present a two-stage graph cut based model for segmentation of touching cell nuclei in fluorescence microscopy images In the first stage voxels with very high probability of being foreground or background are found and separated by a boundary with a minimal geodesic length In the second stage the obtained clusters are split into isolated cells by combining image gradient information and incorporated a priori knowledge about the shape of the nuclei Moreover, these two qualities can be easily balanced using a single user parameter Preliminary tests on real data show promising results of the method

Patent
Li Hong1
26 May 2009
TL;DR: In this article, a method for detecting or predicting whether a test image is blurred is presented, which includes extracting a training statistical signature (366) that is based on a plurality of data features (362, 364) from a training image set.
Abstract: The present invention is directed to a method for detecting or predicting (302, 602) whether a test image is blurred. In one embodiment, the method includes extracting a training statistical signature (366) that is based on a plurality of data features (362, 364) from a training image set (14, 16), the training image set (14, 16) including a sharp image (14) and a blurry image (16); training a classifier (368) to discriminate between the sharp image (14) and the blurry image (16) based on the training statistical signature; and applying (302, 602) the trained classifier to a test image that is not included in the training image set (14, 16) to predict whether the test image is sharp (18) or blurry (20). The step of extracting can include measuring one or more statistical moments (576, 776) for various levels (L0-L5), estimating a covariance (577, 777) between adjacent levels (L0-L5), and/or extracting various metadata features (364, 664) from the images (14, 16). The step of training (300, 600) can include training a non-linear support vector machine (300) or a linear discriminant analysis (600) on the training statistical signature of the training image set (14, 16).

Proceedings ArticleDOI
07 Nov 2009
TL;DR: A color image enhancement method that uses retinex with a robust envelope to improve the visual appearance of an image and yields a better (almost hallow-free) performance than traditional image enhancement methods.
Abstract: In this paper, we propose a color image enhancement method that uses retinex with a robust envelope to improve the visual appearance of an image. The word “retinex” is hybird “retina” and “cortex”, suggesting that human visual perception is involved in this color image enhancement. To avoid the gray-world violation, a color-shifting problem, an input RGB color image is transformed into an HVS color image, but only the V component is enhanced. Furthermore, to prevent hallow artifacts, we construct a robust envelope with a gradient-dependent weighting to limit disturbances around intensity gaps such as edges and corners. Our experiment results show that the proposed method yields a better (almost hallow-free) performance than traditional image enhancement methods.

Patent
05 Nov 2009
TL;DR: In this paper, a depth image of a scene may be received, observed, or captured by a device, and the image may then be processed and a refined image may be rendered based on the processed image.
Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. The image may then be processed. For example, the image may be downsampled, a shadow, noise, and/or a missing potion in the image may be determined, pixels in the image that may be outside a range defined by a capture device associated with the image may be determined, a portion of the image associated with a floor may be detected. Additionally, a target in the image may be determined and scanned. A refined image may then be rendered based on the processed image. The refined image may then be processed to, for example, track a user.

Proceedings ArticleDOI
24 May 2009
TL;DR: A novel approach is proposed by considering the distance from contour edges, fine gradient value are applied to fill the contour gaps to achieve gradual transaction in bit-depth expansion algorithm.
Abstract: Color bit-depth is an important attribute to image quality However, the precision in various image capture devices limits the color bit-depth and introduces loss in visual quality Expanding the color bit-depth is an important image enhancement issue A good bit-depth expansion system manipulates low color bit-depth image for best visual quality as displayed on high color bit-depth monitors However, in most color bit-depth expansion algorithms, severe contouring effect is observed in smooth gradient area which degrades the visual quality In this paper, a novel approach is proposed By considering the distance from contour edges, fine gradient value are applied to fill the contour gaps to achieve gradual transaction

Patent
11 Dec 2009
TL;DR: In this paper, an image processing apparatus has a noise reduction processing control portion which controls the contents of image processing for obtaining the third image from the first image according to the noise level in the first one.
Abstract: An image processing apparatus outputs an output image by synthesizing a first image obtained by shooting, a second image obtained by shooting with an exposure time longer than the exposure time of the first image, and a third image obtained by reducing noise in the first image. The image processing apparatus has a noise reduction processing control portion which controls the contents of image processing for obtaining the third image from the first image according to the noise level in the first image.

Journal ArticleDOI
TL;DR: Experimental results with the laboratory for image and video engineering data set show the effectiveness of the proposed quality metric based on the Harris response, which is computed from the gradient information matrix and its eigenvalues.
Abstract: Image degradation deforms the structure of an image. Thus, to evaluate the quality of an image is equivalent to measuring the degree of deformation of the structure of the image. In this letter, we propose a new image quality metric based on the Harris response, which is computed from the gradient information matrix and its eigenvalues. When an image is degraded by image compression, noise, transmission error, and so on, gradient information of the image is changed, causing the Harris response to change. Therefore, the degree of change in the Harris response of the image is related to the quality degradation of the image. Experimental results with the laboratory for image and video engineering data set show the effectiveness of the proposed quality metric.

PatentDOI
Yang-Ming Zhu1, Charles Nortmann1
TL;DR: In this paper, an image display system consists of an image generating module (50) configured to generate an image by color coding an input image in accordance with a colormap (52) assigning colors to intensities of an intensity spectrum; a colorormap modifying module (56) configures to select a portion (72, 82, 92) of the intensity spectrum to be transparent; and a display (42) configured to display the generated image.
Abstract: An image display method comprises: color coding a second image respective to an intensity spectrum with a portion or portions (72, 82, 92) of the intensity spectrum set to be transparent to generate a color coded second image; combining a first image and the color coded second image to generate a fused image; and displaying the fused image. An image display system comprises: an image generating module (50) configured to generate an image by color coding an input image in accordance with a colormap (52) assigning colors to intensities of an intensity spectrum; a colormap modifying module (56) configured to select a portion (72, 82, 92) of the intensity spectrum to be transparent; and a display (42) configured to display the generated image.

Patent
18 May 2009
TL;DR: In this article, an image composition apparatus for composing color images with black-and-white images including infrared components, and an image-composition method thereof are presented. But the image composition method is not suitable for the use of RGB images.
Abstract: Provided are an image composition apparatus for composing color images with black-and-white images including infrared components, and an image composition method thereof. The image composition method includes generating a first image signal with color information and a second image signal including infrared components without color information, dividing the first image signal into a brightness signal and a color signal, composing the brightness signal of the first image signal with a brightness signal of the second image signal to generate a composed brightness signal, and composing the composed brightness signal with the color signal of the first image signal to generate a color image.

Book ChapterDOI
07 Jul 2009
TL;DR: It is shown that well-known full-reference image quality measures can be estimated from the residual image without the reference image, and a procedure is proposed that has the potential to enhance the image quality of given image denoising algorithms.
Abstract: State-of-the-art image denoising algorithms attempt to recover natural image signals from their noisy observations, such that the statistics of the denoised image follow the statistical regularities of natural images. One aspect generally missing in these approaches is that the properties of the residual image (defined as the difference between the noisy observation and the denoised image) have not been well exploited. Here we demonstrate the usefulness of residual images in image denoising. In particular, we show that well-known full-reference image quality measures such as the mean-squared-error and the structural similarity index can be estimated from the residual image without the reference image. We also propose a procedure that has the potential to enhance the image quality of given image denoising algorithms.

Patent
Kang-eui Lee1
11 Sep 2009
TL;DR: In this article, a method and apparatus for creating a high dynamic range (HDR) image while preventing a ghost effect is provided, which detects an area of a first image and a second image of the scene where there exists an object in motion and creates an HDR image, with respect to the area where there exist a motion, by correcting a brightness level of the first image to a brightnesslevel of the second image to form a corrected first image.
Abstract: A method and apparatus for creating a high dynamic range (HDR) image while preventing a ghost effect is provided. The method of creating an HDR image for a scene including an object in motion, includes detecting an area of a first image of the scene and a second image of the scene where there exists an object in motion and creating an HDR image, with respect to the area where there exists a motion, by correcting a brightness level of the first image to a brightness level of the second image to form a corrected first image. The method further includes substituting a corrected area of the corrected first image for a corresponding area of the s second image to form a substituted second image, and fusing the corrected first image and the substituted second image.