A novel iris segmentation using radial-suppression edge detection

Iris segmentation is a key step in the iris recognition system. The conventional methods of iris segmentation are based on the assumption that the inner and outer boundaries of an iris can be taken as circles. The region of the iris is segmented by detecting the circular inner and outer boundaries. However, we investigate the iris boundaries in the CASIA-IrisV3 database, and find that the actual iris boundaries are not always circular. In order to solve this problem, a new approach for iris segmentation based on radial-suppression edge detection is proposed in this paper. In the radial-suppression edge detection, a non-separable wavelet transform is used to extract the wavelet transform modulus of the iris image. Then, a new method of radial non-maxima suppression is proposed to retain the annular edges and simultaneously remove the radial edges. Next, a thresholding operation is utilized to remove the isolated edges and produce the final binary edge map. Based on the binary edge map, a self-adaptive method of iris boundary detection is proposed to produce final iris boundaries. Experimental results demonstrate that the proposed iris segmentation is desirable.     

Modeling for edge detection problems in blurred noisy images

The aim of this paper is to provide a theoretical set up and a mathematical model for the problem of image reconstruction. The original image belongs to a family of two-dimensional (2-D) possibly discontinuous functions, but is blurred by a Gaussian point spread function introduced by the measurement device. In addition, the blurred image is corrupted by an additive noise. We propose a preprocessing of data which enhances the contribution of the signal discontinuous component over that one of the regular part, while damping down the effect of noise. In particular we suggest to convolute data with a kernel defined as the second order derivative of a Gaussian spread function. Finally, the image reconstruction is embedded in an optimal problem framework. Now convexity and compactness properties for the admissible set play a fundamental role. We provide an instance of a class of admissible sets which is relevant from an application point of view while featuring the desired properties.     

分区均值的自适应噪声图像边缘检测算法

针对边缘检测算法存在的检测精度与抑噪的矛盾,提出一种基于新的图像边缘检测算法。算法将检测窗口按照0o,45o,90o和135o四个不同方向分别划分为两个子区域,先统计每个检测窗口(3×3)内脉冲噪声点的个数,如果超过3个,则扩大检测窗口至5×5。对于检测窗口每个方向划分的两个子区域,分别计算区域内的非噪声点的平均灰度值,利用平均值差的绝对值作为窗口的方向梯度值,进而求得中心点的梯度。然后,对梯度图像采用改进的非极大值抑制方法进行细化,并提取边缘。实验结果表明,该算法检测的图像边缘方向性较强,边缘较细,不仅对不同程度脉冲噪声干扰图像具有较强的抑噪能力,而且对高斯噪声也具有一定程度的抑制效果,算法具有较强的适应性。     

Real-world image segmentation using edge detection and subtraction

Preliminary work relating to edge-detection techniques associated with 3*3 masks and a local adaptive thresholding technique to provide a simple method for the extraction of edges from real-world images followed by further image enhancement techniques to obtain segmented pictures for further processing leading to counting, velocity measurement and tracking of road vehicles is presented.<>     

A novel approach to microcalcification detection using fuzzy logictechnique

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach     

一种新型的图像边缘检测技术

针对传统的数学形态学边缘检测算法,提出一种新型的基于数学形态学的膨胀,腐蚀,开,闭等变换,采用不同方向不同尺度结构元素对图像进行边缘检测,大尺度结构元素清除噪声,小尺度结构元素获取完整的边缘。通过计算获取边缘的信息熵,自动匹配权值系数,进而得到最终图像边缘的完整信息。此方法能够有效地抑制噪声,保持边缘信息的完整性,精确性。     

Automatic cardiac LV boundary detection and tracking using hybrid fuzzy temporal and fuzzy multiscale edge detection

This paper describes a new fully automatic fuzzy multiresolution-based algorithm for cardiac left ventricular (LV) epicardial and endocardial boundary detection and tracking on a sequence of short axis (SA) echocardiographic images of a complete cardiac cycle. This is a necessary step for automatic quantification of cardiac function using echo images. The proposed method is a "center-based" approach in which epicardial and endocardial boundary edge points are searched for on radial lines emanating from the LV center point. The central point of the LV cavity is estimated using a fuzzy-based technique in which the "uncertain" spatial, morphological, and intensity information of the image are represented as fuzzy sets and then combined by fuzzy operators. Edge-detection stage uses multiscale spatial and temporal information in a fuzzy multiresolution framework to identify a single moving edge point for each one of the epicardial and endocardial boundaries over the M radii in the N frames of a complete cardiac cycle. The raw extracted edge points are then processed in the wavelet domain to reduce the effects of noise from the boundaries and papillary muscles from the endocardial boundary extraction process. Finally, a uniform cubic B-spline approximation method is used to define the closed LV boundaries. Experiments with simulated and real echocardiographic images are presented.     

A novel FPGA-based architecture for Sobel edge detection operator

A novel FPGA-based architecture for Sobel edge detection algorithm has been proposed. The Sobel algorithm is chosen due to its property of providing a differencing as well as noise smoothing operation in the single kernel. Thus, noise sensitivity of first gradient based operations can be avoided by the use of this algorithm. The implementation of edge detection algorithms on a field programmable gate array (FPGA) is motivated by the fact that large memory FPGAs are now available, providing a platform for processing real time algorithms on application-specific hardware with substantially higher performance than programmable digital signal processors (DSPs). This architecture can be used as a building block of a pattern recognition system, autonomous robot navigation, and also as a system for creating an image dazzling effect in multimedia graphics. This architecture is implicitly pipelined to provide a system capable of operating at a clock speed of 99.499?MHz which is a significant improvement over programmable DSPs implementation.     

Nonlocal low-rank matrix completion for image interpolation using edge detection and neural network

In this paper, we propose a nonlocal low-rank matrix completion method using edge detection and neural network to effectively exploit the nonlocal inter-pixel correlation for image interpolation and other possible applications. We first interpolate the images using some basic techniques, such as bilinear and edge-directed methods. Then, each image patch is categorized as smooth regions, edge regions, or texture regions and adaptive interpolating mechanisms are applied to each specific type of regions. Finally, for each specific type of regions, neural networks and low-rank matrix completion are employed to accurately update the results. An iteratively re-weighted minimization algorithm is used to solve the low-rank energy minimization function. Our experiments on benchmark images clearly indicate that the proposed method produces much better results than some existing algorithms using a variety of image quality metric in terms of both objective image quality assessment and subjective quality assessment.     

Design of an image edge detection filter using the Sobel operator

The architecture of the edge detector presented is highly pipeline to perform the computations of gradient magnitude and direction for the output image samples. The chip design is based on a 2-μm, double-metal, CMOS technology and was implemented using a silicon compiler system in less than 2 man-months. It is designed to operate with a 10-MHz two-phase clock, and it performs approximately 200×10⁶ additions/s to provide the required magnitude and direction outputs every clock cycle. The function of the chip has been demonstrated with a prototype system that is performing image edge detection in real time     

维氏硬度计缺口图像边缘检测方法

维氏硬度测量法是物体硬度测量的一种重要方法,全自动的维氏硬度测量计都是以图像处理为基础。而维氏硬度计图像处理方法的关键是缺口图像的边缘提取,因为常用维氏硬度计测量方法中的最小二乘法和边角扫描法都是以缺口图像边缘为基础。传统的基于直方图的边缘检测方法在较好的缺口图像中能得到很好的效果,但是由于某些物体材料的反光性导致了该方法的失效,一种基于canny边缘检测和纹理边缘处理的算法能够较好的解决该问题。     

An ideal image edge detection scheme

This paper introduces a scale-invariant and contrast-invariant multi-scale differential edge detector. The method is a direct consequence of two key discoveries: (1) a precise scale normalization method and (2) a formula to verify scale-invariant detectors. The new scale normalization method provides differential operators with respect to scale, among them the scale-invariant edge detectors. To investigate these differential detectors quantitatively, mathematical functions were used to represent the edges and to solve for the parameters, including position, width, contrast, offset, and orientation, in closed form. Noise is filtered as a low-contrast feature. The method has been tested with various kinds of synthesized edge functions and can extract edge features accurately. It is suitable for real-world images of several kinds of degradation.     

Real-time edge detection and image segmentation

The implementation of real-time edge detection and image segmentation using analog VLSI is described. A novel technique of image segmentation using radially propagating signals is discussed. Current-mode processing is used to avoid offset voltages and lead to circuit implementations which are compatible with standard CMOS processes. A system using raylike signal propagation and implemented using 3-CMOS technology is described together with experimental results.     

基于边缘保持滤波的Canny彩色图像边缘检测方法

提出了基于边缘保持滤波的Canny彩色图像边缘检测方法。该方法利用了HSV颜色空间信道相关性低的优点,结合Canny算子定位准确的优点和边缘保持滤波理论,用边缘保持滤波取代传统的高斯滤波,用梯度矢量计算法替代传统的梯度标量计算法,从而增强了在平滑过程中对图像边缘的保持,最大程度保留了色彩的差异信息,实现了彩色图像边缘的自适应提取。实验结果证明,该方法将灰度空间的Canny算法推广到彩色矢量空间,充分利用了彩色信息,对彩色图像边缘提取具有较好的检测精度和准确度。     

A new approach in content-based image retrieval using fuzzy

Finding an image from a large set of images is an extremely difficult problem. One solution is to label images manually, but this is very expensive, time consuming and infeasible for many applications. Furthermore, the labeling process depends on the semantic accuracy in describing the image. Therefore many Content based Image Retrieval (CBIR) systems are developed to extract low-level features for describing the image content. However, this approach decreases the human interaction with the system due to the semantic gap between low-level features and high-level concepts. In this study we make use of fuzzy logic to improve CBIR by allowing users to express their requirements in words, the natural way of human communication. In our system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation. The texture and color attributes are computed in a way that model the Human Vision System (HSV). We proposed a new approach for graph matching that resemble the human thinking process. The proposed system is evaluated by different users with different perspectives and is found to match users’ satisfaction to a high degree.     

数据融合技术在图像边缘检测中的应用

提出了一种基于数据融合的图像边缘检测算法。此种方法对初始图像分别采用“Rob-erts”算子和“sobel”算子及小波变换三种方法进行边缘提取,再将三种算法结果通过数据融合的手段获得一幅新的边缘图像。实验结果表明,该方案所得到的边缘图像的效果优于单独采用“Sobel”或“Roberts”算子所获得的边缘图像,是一种行之有效的图像边缘提取算法。     

A novel image segmentation model with an edge weighting function

A variational model for image segmentation consists of a data term and a regularization term. Usually, the data term is chosen as squared $\text{ L }_{2}$ norm, and the regularization term is determined by the prior assumption. In this paper, we present a novel model in the framework of MAP (maximum a posteriori). A new iteratively reweighted $\text{ L }_{2}$ norm is used in the data term, which shares the advantages of $\text{ L }_{2}$ and mixed $\text{ L }_{21}$ norm. An edge weighting function is addressed in the regularization term, which enforces the ability to reduce the outlier effects and preserve edges. An improved region-based graph cuts algorithm is proposed to solve this model efficiently. Numerical experiments show our method can get better segmentation results, especially in terms of removing outliers and preserving edges.     

机器学习的光照不均图像边缘检测系统

针对传统图像检测系统检测质量较低,容易出现图像边缘细节模糊的现象,提出基于机器学习的光照不均图像边缘检测系统.构建图像边缘检测系统总体框架,检测系统主要分成图像收集、低级处理、高级处理以及检测结果的后续处理四部分,以该系统框架为基础采用支持向量机模式将正常样本空间内不能被线性分类或者近似性分类的图像,通过非线性映射至高...     

Gradient-based edge detection using nonlinear edge enhancingprefilters

This correspondence examines the use of nonlinear edge enhancers as prefilters for edge detectors. The filters are able to convert smooth edges to step edges and suppress noise simultaneously. Thus, false alarms due to noise are minimized and edge gradient estimates tend to be large and localized. This leads to significantly improved edge maps.