一种新的粘连字符图像分割方法

针对监控画面采样图像中数字的自动识别问题,提出一种新的粘连字符图像分割方法。该方法以预处理后二值图像的连通状况来判定字符粘连的存在,并对粘连字符图像采用上下轮廓极值法确定候选粘连分割点,以双向最短路径确定合适的图像分割线路。仿真实验表明,该方法能有效解决粘连字符图像的分割问题。     

A new iris segmentation method for non-ideal iris images

Many researchers have studied iris recognition techniques in unconstrained environments, where the probability of acquiring non-ideal iris images is very high due to off-angles, noise, blurring and occlusion by eyelashes, eyelids, glasses, and hair. Although there have been many iris segmentation methods, most focus primarily on the accurate detection with iris images which are captured in a closely controlled environment. This paper proposes a new iris segmentation method that can be used to accurately extract iris regions from non-ideal quality iris images. This research has following three novelties compared to previous works; firstly, the proposed method uses AdaBoost eye detection in order to compensate for the iris detection error caused by the two circular edge detection operations; secondly, it uses a color segmentation technique for detecting obstructions by the ghosting effects of visible light; and thirdly, if there is no extracted corneal specular reflection in the detected pupil and iris regions, the captured iris image is determined as a “closed eye” image.     

A total color difference measure for segmentation in color images

A single-value total color difference (TCD) measurement for scene segmentation is proposed and evaluated experimentally. Both chrominance and luminance difference criteria are considered. The luminance component is defined by a unit in luminance change expressed in terms of MacAdam's Just Noticeable Difference, JND. The chromaticity component is derived directly from JND. Experiments using both pixel and region analysis show that the proposed TCD can effectively indicate object boundaries over a wide range of luminance changes. The results have been evaluated both subjectively and quantitatively. For comparison purposes, results have been obtained in several color spaces.     

Feature encoding for unsupervised segmentation of color images

In this paper, an unsupervised segmentation method using clustering is presented for color images. We propose to use a neural network based approach to automatic feature selection to achieve adaptive segmentation of color images. With a self-organizing feature map (SOFM), multiple color features can be analyzed, and the useful feature sequence (feature vector) can then be determined. The encoded feature vector is used in the final segmentation using fuzzy clustering. The proposed method has been applied in segmenting different types of color images, and the experimental results show that it outperforms the classical clustering method. Our study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.     

Unsupervised multiscale segmentation of color images

This paper proposes a new multiresolution technique for color image representation and segmentation, particularly suited for noisy images. A decimated wavelet transform is initially applied to each color channel of the image, and a multiresolution representation is built up to a selected scale 2^J. Color gradient magnitudes are computed at the coarsest scale 2^J, and an adaptive threshold is used to remove spurious responses. An initial segmentation is then computed by applying the watershed transform to thresholded magnitudes, and this initial segmentation is projected to finer resolutions using inverse wavelet transforms and contour refinements, until the full resolution 2⁰ is achieved. Finally, a region merging technique is applied to combine adjacent regions with similar colors. Experimental results show that the proposed technique produces results comparable to other state-of-the-art algorithms for natural images, and performs better for noisy images.     

A new and robust method for character segmentation and recognition in license plate images

This paper provides a new and fast method for segmentation and recognition of characters in license plate images. For this purpose, various methods have been proposed in literature. However, most of them suffer from: sensitivity to non-uniform illumination distribution, existence of shade in license plate, license plate color and the need for receiving an exact image of the license plate. In the proposed algorithm, non-uniform illumination and noise are reduced by a Gaussian lowpass filter and also by an innovational Laplacian-like transform and characters are segmented by a set of indigenous and relative features. To be prepared for recognition, the segmented characters are normalized by a local algorithm. Two feed-forward neural networks with back-propagation learning method are employed for character recognition. The principal component analysis (PCA) is used to decrease input data and, consequently, computational complexity. The proposed algorithm does not necessarily need an exact plate image and can receive a band from the vehicle original image as an input, which includes the plate. Our proposed method is completely robust to the disturbances such as non-uniform brightness distribution on the various positions of a license plate image and the plate color. In order to evaluate our algorithm, we applied it on a database including 120 vehicle images with different backgrounds, plate colors, brightness distributions, distances and viewing angles. The results confirm the robustness of the proposed method against severe imaging conditions.     

A new approach for color image segmentation based on color mixture

The aim of this paper is to propose a new methodology for color image segmentation. We have developed an image processing technique, based on color mixture, considering how painters do to overlap layers of various hues of paint on creating oil paintings. We also have evaluated the distribution of cones in the human retina for the interpretation of these colors, and we have proposed a schema for the color mixture weight. This method expresses the mixture of black, blue, green, cyan, red, magenta, yellow and white colors quantified by the binary weight of the color that makes up the pixels of an RGB image with 8 bits per channel. The color mixture generates planes that intersect the RGB cube, defining the HSM (Hue, Saturation, Mixture) color space. The position of these planes inside the RGB cube is modeled, based on the distribution of r, g and b cones of the human retina. To demonstrate the applicability of the proposed methodology, we present in this paper, the segmentation of “human skin” or “non-skin” pixels in digital color images. The performance of the color mixture was analyzed by a Gaussian distribution in the HSM, HSV and YCbCr color spaces. The method is compared with other skin/non-skin classifiers. The results demonstrate that our approach surpassed the performance of all compared methodologies. The main contributions of this paper are related to a new way for interpreting color of binary images, taking into account the bit-plane levels and the application in image processing techniques.     

A new complete color normalization method for H&E stained histopatholgical images

Applied Intelligence - The popularity of digital histopathology is growing rapidly in the development of computer aided disease diagnosis systems. However, the color variations due to manual cell...     

基于张量投票和成份标注的彩色文本的分割

自然景观中文本会受到各种噪声的污染,这给文本的分割和识别带来困难.根据不同特征值将图像分层,使用张量投票框架对每层进行分析,可以有效地去除噪声并对缺失的图像进行修补.由于要根据经验判断阈值,要达到理想的效果必须经过多次运算.引入经改进成分标注方法,确定阁值,提高了效率.实验结果表明,该方法取得了比较好的效果.     

A new automated segmentation and classification of mammogram images

Multimedia Tools and Applications - Early-stage recognition of lesions is the better probable manner for fighting against breast cancer to find a disease with the highest ratio of malignancy around...     

A robust incremental learning framework for accurate skin region segmentation in color images

In this paper, we propose a robust incremental learning framework for accurate skin region segmentation in real-life images. The proposed framework is able to automatically learn the skin color information from each test image in real-time and generate the specific skin model (SSM) for that image. Consequently, the SSM can adapt to a certain image, in which the skin colors may vary from one region to another due to illumination conditions and inherent skin colors. The proposed framework consists of multiple iterations to learn the SSM, and each iteration comprises two major steps: (1) collecting new skin samples by region growing; (2) updating the skin model incrementally with the available skin samples. After the skin model converges (i.e., becomes the SSM), a post-processing can be further performed to fill up the interstices on the skin map. We performed a set of experiments on a large-scale real-life image database and our method observably outperformed the well-known Bayesian histogram. The experimental results confirm that the SSM is more robust than static skin models.     

Markov random field models for unsupervised segmentation oftextured color images

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation     

An FPGA implementation of real-time K-means clustering for color images

K-means clustering is a very popular clustering technique, which is used in numerous applications. In the k-means clustering algorithm, each point in the dataset is assigned to the nearest cluster by calculating the distances from each point to the cluster centers. The computation of these distances is a very time-consuming task, particularly for large dataset and large number of clusters. In order to achieve high performance, we need to reduce the number of the distance calculations for each point efficiently. In this paper, we describe an FPGA implementation of k-means clustering for color images based on the filtering algorithm. In our implementation, when calculating the distances for each point, clusters which are apparently not closer to the point than other clusters are filtered out using kd-trees which are dynamically generated on the FPGA in each iteration of k-means clustering. The performance of our system for 512 × 512 and 640 × 480  pixel images (24-bit full color RGB) is more than 30 fps, and 20–30 fps for 756 × 512 pixel images in average when dividing to 256 clusters.

Intelligent segmentation method for real-time defect inspection system

To extract desired flaws from various types of images, the integration of different segmentation methods is required. In this study, we present an intelligent method for automatic selection of a proper image segmentation method upon detecting a particular flaw type. The new method is capable of choosing the most suitable method from four segmentation methods currently available. The automatic selection procedures start from the pre-segmentation of an image to be examined. Then, the predetermined features are extracted from the original, foreground, and background images. After that, a suitable segmentation method will be selected using a classifier based on six features. Finally, the image is re-segmented by the selected segmentation method to discover flaws. The proposed method has been tested using 1676 defective images. The results show a significant reduction in misclassification rate from about 44% to 13.96%.     

A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data

The objective function of the original (fuzzy) c-mean method is modified by a regularizing functional in the form of total variation (TV) with regard to gradient sparsity, and a regularization parameter is used to balance clustering and smoothing. An alternating direction method of multipliers in conjunction with the fast discrete cosine transform is used to solve the TV-regularized optimization problem. The new algorithm is tested on both synthetic and real data, and is demonstrated to be effective and robust in treating images with noise and missing data (incomplete data).     

A level set method for oil slick segmentation in SAR images

Despite much effort and significant progress in recent years, image segmentation remains a challenging problem in image processing, especially for the low contrast, noisy synthetic aperture radar (SAR) images. This paper explores the segmentation of oil slicks using a partial differential equation (PDE)‐based level set method, which represents the slick surface as an implicit propagation interface. Starting from an initial estimation with priori information, the level set method creates a set of speed functions to detect the position of the propagation interface. Specifically, the image intensity gradient and the curvature flow are utilized together to determine the speed and direction of the propagation. This allows the front interface to propagate naturally with topological changes, significant protrusions and narrow regions, giving rise to stable and smooth boundaries that discriminate oil slicks from the surrounding water. As the speckles are removed concurrently while the front interface propagates, the pre‐filtering of noise is saved. The proposed method has been illustrated by experiments on oil slick segmentation using the ERS‐2 SAR images. Its advantages over the traditional image segmentation approaches have also been demonstrated.     

A method for multi-spectral image segmentation evaluation based on synthetic images

A general framework for testing the quality of the segmentation of a multi-spectral satellite image is proposed. The method is based on the production of synthetic images with the spectral characteristics of the image pixels extracted from a signature multi-spectral image. The knowledge of the location of objects in the synthetic image provides a reference segmentation, which allows for a quantitative evaluation of the quality provided by a segmentation algorithm. The Hammoude metric and three external similarity indices (Rand, Corrected Rand, and Jaccard) were chosen to perform this evaluation, but other metrics can also be used. The proposed methodology can be used for any type of satellite image (or multi-spectral image), set of land cover types, and segmentation algorithms.A practical application was carried out to illustrate the value of the proposed method. A SPOT satellite image was used to extract the spectral signature of 8 land cover types. Three test images were produced using the 8 land cover classes and two different 5 class sub-sets. The segmentation results provided by a standard algorithm were compared with the reference or expected segmentation. The results clearly indicate that the quality of a segmentation obtained from a multi-spectral image not only depends on the geometric properties of the objects present in the image, but also on their spectral characteristics. The results suggest that a specific evaluation should be carried out for each particular experiment, as the segmentation results are very dependent on the choice of land cover types.     

A real-time CFAR thresholding method for target detection in hyperspectral images

In order to support immediate decision-making in critical circumstances such as military reconnaissance and disaster rescue, real-time onboard implementation of target detection is greatly desired. In this paper, a real-time thresholding method (RT-THRES) is proposed to obtain the constant false alarm rate (CFAR) thresholds for target detection in real-time circumstances. RT-THRES utilizes Gaussian mixture model (GMM) to track and fit the distribution of the target detector’s outputs. GMM is an extension to Gaussian probability density function, which could approximate any distribution smoothly. In this method, GMM is utilized to model the detector’s output, and then the detection threshold is calculated to achieve a CFAR detection. The conventional GMM’s parameter estimation by Expectation-Maximization (EM) requires all data samples in the dataset to be involved during the procedure and the the parameters would be re-estimated when new data samples available. Thus, GMM is difficult to be applied in real-time processing when newly observed data samples coming progressively. To improve GMM’s application availability in time-critical circumstance, an optimization strategy is proposed by introducing the Incremental GMM (IGMM) which allows GMM’s parameter to be estimated online incrementally. Experiments on real hyperspectral image and synthetic dataset suggest that RT-THRES can track and model the detection outputs’ distribution accurately which ensures the accuracy of the calculation of CFAR thresholds. Moreover, by applying the optimization strategy the computational consumption of RT-THRES maintains relatively low.

     

A new segmentation system for brain MR images based on fuzzy techniques

This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsupervised clustering methods including the FCM by Bezdek, suffer many problems that can be partially treated with a proper rule to construct the initial membership matrix to clusters. This work develops a specific method to construct the initial membership matrix to clusters in order to improve the strength of the clusters. The new FMCM is tested on a set of benchmarks and then the application to the segmentation of MR images is presented and compared with the results obtained using FCM.