A robust hit-or-miss transform for template matching applied to very noisy astronomical images

The morphological hit-or-miss transform (HMT) is a powerful tool for digital image analysis. Its recent extensions to grey level images have proven its ability to solve various template matching problems. In this paper we explore the capacity of various existing approaches to work in very noisy environments and discuss the generic methods used to improve their robustness to noise. We also propose a new formulation for a fuzzy morphological HMT which has been especially designed to deal with very noisy images. Our approach is validated through a pattern matching problem in astronomical images that consists of detecting very faint objects: low surface brightness galaxies. Despite their influence on the galactic evolution model, these objects remain mostly misunderstood by the astronomers. Due to their low signal to noise ratio, there is no automatic and reliable detection method yet. In this paper we introduce such a method based on the proposed hit-or-miss operator. The complete process is described starting from the building of a set of patterns until the reconstruction of a suitable map of detected objects. Implementation, running cost and optimisations are discussed. Outcomes have been examined by astronomers and compared to previous works. We have observed promising results in this difficult context for which mathematical morphology provides an original solution.     

On the application of the convex hull to histogram analysis in threshold selection

A recently proposed algorithm for computing the convex hull of a grey-level histogram in image segmentation is shown to be inefficient due to the fact that it does not exploit the histogram's structure. It is pointed out that a histogram is a weakly externally visible polygon and thus a very simple linear convex hull algorithm will work for such applications.     

改进颜色融合的医学图像彩色化技术

彩色化后的医学图像能清晰体现患者病灶信息有利于医患沟通。提出改进颜色融合的医学图像彩色化方法,首先利用基于KNN的图像前背景区分算法,强化病灶区域的边界信息;然后以此为约束条件,只需提供简单的着色输入;最后将边界能量引入颜色融合方法,得到较好的着色结果。着色图像保持了原图的灰度信息不变,增加了彩色标记图像的颜色和真实感。实验结果表明,该算法具有较高的精确度,可有效的应用于医学图像彩色化处理。     

Real-time image segmentation for visual inspection of pharmaceutical tablets

Visual appearance is an important quality factor of pharmaceutical tablets. Due to the vast quantities of produced tablets and high-quality requirements, pharmaceutical companies are interested in employing automated systems for real-time visual tablet inspection with the speeds of up to 100 tablets per second. Such systems require reliable tablet manipulation, illumination, image acquisition, tablet image analysis, classification, and sorting system. Tablet image segmentation, in which each tablet image is partitioned into the tablet region and background, is the first and very important step in tablet image analysis. It this paper, we propose a novel real-time segmentation method for grey-level images that is based on border tracking. The proposed method was designed to be accurate, robust, and computationally undemanding. The performances of the method were objectively assessed on a large number of simulated and real tablet images. The obtained results indicated high reliability, accuracy, and speed. The 100% reliability was obtained for segmentation of real images of pharmaceutical tablets, while the segmentation times were no more than 1.5 ms or 15% of the whole time available for tablet image analysis. As such, the proposed method proved feasible for real-time visual quality inspection of pharmaceutical tablets. Based on just a few assumptions that are usually fulfilled, the method may be a valuable segmentation tool for many other visual quality inspection applications.     

On Topology Preservation in 3D Thinning

Topology preservation is a major concern of parallel thinning algorithms for 2D and 3D binary images. To prove that a parallel thinning algorithm preserves topology, one must show that it preserves topology for all possible images. But it would be difficult to check all images, since there are too many possible images. Efficient sufficient conditions which can simplify such proofs for the 2D case were proposed by Ronse [Discrete Appl. Math. 21, 1988, 69-79]. By Ronse′s results, a 2D parallel thinning algorithm can be proved to be topology preserving by checking a rather small number of configurations. This paper establishes sufficient conditions for 3D parallel thinning algorithms to preserve topology.     

Spatial and spectral morphological template matching

Template matching is a very topical issue in a wide range of imaging applications. Mathematical morphology offers the hit-or-miss transform, an operator which has been successfully applied for template matching in binary images. More recently, it has been extended to grayscale images and even to multivariate images. Nevertheless, these extensions, despite being relevant from a theoretical point-of-view, might lack practical interest due to the inherent difficulty to set up correctly the transform and its parameters (e.g. the structuring functions). In this paper, we propose a new and more intuitive operator which allows for morphological template matching in multivariate images from both a spatial and spectral point of view. We illustrate the potential of this operator in the context of remote sensing.     

Optimal mean-absolute-error filtering of gray-scale signals by the morphological hit-or-miss transform

The binary hit-or-miss transform is applied to filter digital gray-scale signals. This is accomplished by applying a union of hit-or-miss transforms to an observed signal's umbra and then taking the surface of the filtered umbra as the estimate of the ideal signal. The hit-or-miss union is constructed to provide the optimal mean-absolute-error filter for both the ideal signal and its umbra. The method is developed in detail for thinning hit-or-miss filters and applies at once to the dual thickening filters. It requires the output of the umbra filter to be an umbra, which in general is not true. A key aspect of the paper is the complete characterization of umbra-preserving union-of-hit-or-miss thinning and thickening filters. Taken together, the mean-absolute-error theory and the umbra-preservation characterization provide a full characterization of binary hit-or-miss filtering as applied to digital gray-scale signals. The theory is at once applicable to hit-or-miss filtering of digital gray-scale images through the three-dimensional (3-D) binary hit-or-miss transform.     

Interactive image segmentation by maximal similarity based region merging

Efficient and effective image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. This paper presents a new region merging based interactive image segmentation method. The users only need to roughly indicate the location and region of the object and background by using strokes, which are called markers. A novel maximal-similarity based region merging mechanism is proposed to guide the merging process with the help of markers. A region R is merged with its adjacent region Q if Q has the highest similarity with Q among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, and then effectively extracts the object contour by labeling all the non-marker regions as either background or object. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. Extensive experiments are performed and the results show that the proposed scheme can reliably extract the object contour from the complex background.     

Extended Shearlet HMT Model-Based Image Denoising Using BKF Distribution

Images are often corrupted by noise in the procedures of image acquisition and transmission. It is a challenging work to design an edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method; it not only can exactly compute the Shearlet coefficients based on a multiresolution analysis, but also can represent images with very few coefficients. In this paper, we propose a new image denoising approach in extended DST domain, which combines hidden Markov tree (HMT) model and Bessel K Form (BKF) distribution. Firstly, the marginal statistics of extended DST coefficients are studied, and their distribution is analytically calculated by modeling extended DST coefficients with BKF probability density function. Then, an extended Shearlet HMT model is established for capturing the intra-scale, inter-scale, and cross-orientation coefficients dependencies. Finally, an image denoising approach based on the extended Shearlet HMT model is presented. Extensive experimental results demonstrate that our extended Shearlet HMT denoising approach can obtain better performances in terms of both subjective and objective evaluations than other state-of-the-art HMT denoising techniques. Especially, the proposed approach can preserve edges very well while removing noise.     

Striped-texture image segmentation with application to multimedia security

In today’s rapid growth of volume of multimedia data, security is important yet challenging problem in multimedia applications. Image, which covers the highest percentage of the multimedia data, it is very important for multimedia security. Image segmentation is utilized as a fundamental preprocessing of various multimedia applications such as surveillance for security by breaking a given image into multiple salient regions. In this paper, we present a new image segmentation approach based on frequency-domain filtering for images with stripe texture, and generalize it to lattice fence images. Our method significantly reduces the impact of stripes on segmentation performance. The approach proposed in this paper consists of three phases. Given the images, we weaken the effect of stripe texture by filtering in the frequency domain automatically. Then, structure-preserving image smoothing is employed to remove texture details and extract the main image structures. Last, we use an effective threshold method to produce segmentation results. Our method achieves very promising results for the test image dataset and could benefit a number of new multimedia applications such as public security.

     

基于小波域层次Markov模型的图像分割

针对两个状态的有限高斯混合模型逼近小波系数的不足和小波域隐马尔可夫树标号场相互独立的缺点,提出了一种基于小波域层次马尔可夫模型的图像分割算法,这种模型用有限通用混合模型逼近小波系数的分布,使有限高斯混合模型只是其一种特殊情况;在标号场的先验模型确定上,利用马尔可夫模型描述标号场的局部作用关系,给出标号场的具体表达式,克服了小波域马尔可夫树模型标号场相互独立的不足,然后利用贝叶斯准则,给出相应的分割因果算法。该模型不仅具有空域马尔可夫模型有效的递归算法的优点,同时具有小波域隐马尔可夫树模型中的马尔可夫参数变尺度行为。最后用真实的图像和合成图像同几种分割方法进行了对比实验,实验结果表明了本文算法的有效性和优异性。     

图像分割中的马尔可夫随机场方法综述

马尔可夫随机场方法是图像分割中一个极为活跃的研究方向。本文介绍了基于马尔可夫随机场模型的一般理论与图像的关系,给出它在图像分割中的通用框架:包括空域和小波域图像模型的建立、最优准则的选取、标号数的确定、图像模型参数的估计和图像分割的实现,评述了其在图像分割中的应用,展望其发展的方向。     

Robust iris segmentation on uncalibrated noisy images using mathematical morphology

This paper proposes a new approach for fast iris segmentation that relies on the closed nested structures of iris anatomy (the sclera is brighter than the iris, and the iris is brighter than the pupil) and on its polar symmetry. The described method applies mathematical morphology for polar/radial-invariant image filtering and for circular segmentation using shortest paths from generalized grey-level distances. The proposed algorithm obtained good results on the NICE-I contest and showed a very robust behavior, especially when dealing with half-closed eyes, different skin colours/illumination or subjects wearing glasses.     

基于随机森林特征选择算法的鼻咽肿瘤分割

针对医学图像中存在的灰度对比度低、器官组织边界模糊等问题，提出一种新的随机森林（RF）特征选择算法用于鼻咽肿瘤MR图像的分割。首先，充分提取图像的灰度、纹理、几何等特征信息用于构建一个初始的随机森林分类器；随后，结合随机森林特征重要性度量，将改进的特征选择方法应用于原始手工特征集；最终，以得到的最优特征子集构建新的随机森林分类器对测试图像进行分割。实验结果表明，该算法对鼻咽肿瘤的分割精度为：Dice系数79.197%，Acc准确率97.702%，Sen敏感度72.191%，Sp特异性99.502%。通过与基于传统随机森林和基于深度卷积神经网络（DCNN）的分割算法对比可知，所提特征选择算法能有效提取鼻咽肿瘤MR图像中的有用信息，并较大程度地提升小样本情况下鼻咽肿瘤的分割精度。     

基于多特征的SAR图像多尺度无监督分割

提出一种基于多特征的SAR图像多尺度无监督分割方法.该方法采用隐Markov树(hidden Markov tree,HMT)模型对SAR图像的灰度数据与标准差数据分别建模并分割,由两个分割结果提取SAR图像的结构性信息,并根据结构性信息,对灰度分割的后验概率进行平滑,最终的分割结果表明,该方法在很少损失结构性信息的基础上,能得到更光滑的分割结果.     

Image denoising in extended Shearlet domain using hidden Markov tree models

Denoising of images is one of the most basic tasks of image processing. It is a challenging work to design an edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method; it not only can exactly compute the Shearlet coefficients based on a multiresolution analysis, but also can provide nearly optimal approximation for a piecewise smooth function. In this paper, a new image denoising approach in extended Shearlet domain using hidden Markov tree (HMT) model is proposed. Firstly, the joint statistics and mutual information of the extended DST coefficients are studied. Then, the extended DST coefficients are modeled using an HMT model with Gaussian mixtures, which can effectively capture the intra-scale and inter-scale dependencies. Finally, the extended Shearlet HMT model is applied to image denoising. Extensive experimental results demonstrate that our extended Shearlet HMT denoising method can obtain better performances in terms of both subjective and objective evaluations than other state-of-the-art HMT denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.     

基于第二代Bandelet域隐马尔科夫树模型的图像分割

第二代Bandelet可以充分利用图像的内在几何正则性特点, 并能自适应获得图像的最优表示. 本文采用隐马尔可夫树(Hidden Markov tree, HMT)模型对图像的第二代Bandelet系数建模, 通过多尺度参数训练和基于上下文的最大后验概率进行图像分割. 为了评价本文方法的性能, 我们分别选择合成纹理图像、航拍图像和SAR图像进行实验, 并与小波域HMT模型分割方法(WD-HMTseg)和Contourlet域HMT模型分割方法(CHMTseg)进行比较说明算法的有效性. 实验结果表明本文方法不但在边缘准确性和区域一致性上有明显改进, 而且也降低了纹理图的错分概率.     

An efficient method for segmentation of images based on fractional calculus and natural selection

Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) and Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures.