Edge magnitude based multilevel thresholding using Cuckoo search technique

Multilevel thresholding technique is popular and extensively used in the field of image processing. In this paper, a multilevel threshold selection is proposed based on edge magnitude of an image. The gray level co-occurrence matrix (second order statistics) of the image is used for obtaining multilevel thresholds by optimizing the edge magnitude using Cuckoo search technique. New theoretical formulation for objective functions is introduced. Key to our success is to exploit the correlation among gray levels in an image for improved thresholding performance. Apart from qualitative improvements the method also provides us optimal threshold values. Results are compared with histogram (first order statistics) based between-class variance method for multilevel thresholding. It is observed that the results of our proposed method are encouraging both qualitatively and quantitatively.     

Skin cancer extraction with optimum fuzzy thresholding technique

This paper discusses a new approach to segment different types of skin cancers using fuzzy logic approach. The traditional skin cancer segmentation involves the analysis of image features to delineate the cancerous region from the normal skin. Using low level features such as colour and intensity, segmentation can be done by obtaining a threshold level to separate the two regions. Methods like Otsu optimisation provide a quick and simple process to optimise such threshold level; however this process is prone to the lighting and skin tone variations. Fuzzy clustering algorithm has also been widely used in image processing due to its ability to model the fuzziness of human visual perception. Classical fuzzy C means (FCM) clustering algorithm has been applied to image segmentation with good results; however, the classical FCM is based on type-1 fuzzy sets and is unable to handle uncertainties in the images. In this paper, we proposed an optimum threshold segmentation algorithm based on type-2 fuzzy sets algorithms to delineate the cancerous area from the skin images. By using the 3D colour constancy algorithm, the effect of colour changes and shadows due to skin tone variation in the image can be significantly reduced in the preprocessing stage. We applied the optimum thresholding technique to the preprocessed image over the RGB channels, and combined individual results to achieve the overall skin cancer segmentation. Compared to the Otsu algorithm, the proposed method is less affected by the shadows and skin tone variations. The results also showed more tolerance at the boundary of the cancerous area. Compared with the type-1 FCM algorithm, the proposed method significantly reduced the segmentation error at the normal skin regions.     

An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix

Image thresholding is a process that separates particular object within an image from their background. An optimal thresholding technique can be taken as a single objective optimization task, where computation and obtaining a solution can become inefficient, especially at higher threshold levels. In this paper, a new and efficient color image multilevel thresholding approach is presented to perform image segmentation by exploiting the correlation among gray levels. The proposed method incorporates gray-level co-occurrence matrix (GLCM) and cuckoo search (CS) in order to effectively enhance the optimal multilevel thresholding of colored natural and satellite images exhibiting complex background and non-uniformities in illumination and features. The experimental results are presented in terms of mean square error (MSE), peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), computational time (CPU time in seconds), and optimal threshold values for each primary color component at different thresholding levels for each of the test images. In addition, experiments are also conducted on the Berkeley Segmentation Dataset (BSDS300), and four performance indices of image segmentation- Probability Rand Index (PRI), Variation of Information (VoI), Global Consistency Error (GCE), and Boundary Displacement Error (BDE) are tested. To evaluate the performance of proposed algorithm, other optimization algorithm such as artificial bee colony (ABC), bacterial foraging optimization (BFO), and firefly algorithm (FA) are compared using GLCM as an objective function. Moreover, to show the effectiveness of proposed method, the results are compared to existing context sensitive multilevel segmentation techniques based on Tsalli's entropy. Experimental results showed the superiority of proposed technique in terms of better segmentation results with increased number of thresholds.     

Image thresholding using fuzzy entropies

An image can be regarded as a fuzzy subset of a plane. A fuzzy entropy measuring the blur in an image is a functional which increases when the sharpness of its argument image decreases. We generalize and extend the relation "sharper than" between fuzzy sets in view of implementing the properties of a relation "sharper than" between images. We show that there are infinitely many implementations of this relation into an ordering between fuzzy sets (equivalently, images). Relying upon these orderings, we construct classes of fuzzy entropies which are useful for image thresholding by cost minimization. Assuming the image to be a degraded version of an ideal two level image (object/background), a fuzzy entropy can be introduced in a cost functional to force the fitting function to be as close as possible to a two-valued function. The minimization problem is numerically solved, and the results obtained on a synthetic image are reported.     

A minimum-cost thresholding technique for unsupervised change detection

We propose an automatic thresholding technique for difference images in unsupervised change detection. Such a technique takes into account the different costs that may be associated with commission and omission errors in the selection of the decision threshold. This allows the generation of maps in which the overall change-detection cost is minimized, i.e. the more critical kind of error is reduced according to end-user requirements.     

Efficient product sampling using hierarchical thresholding

We present an efficient method for importance sampling the product of multiple functions. Our algorithm computes a quick approximation of the product on the fly, based on hierarchical representations of the local maxima and averages of the individual terms. Samples are generated by exploiting the hierarchical properties of many low-discrepancy sequences, and thresholded against the estimated product. We evaluate direct illumination by sampling the triple product of environment map lighting, surface reflectance, and a visibility function estimated per pixel. Our results show considerable noise reduction compared to existing state-of-the-art methods using only the product of lighting and BRDF.     

Grey level thresholding using second-order statistics

This letter describes algorithms for global thresholding of grey-tone images which use second-order grey level statistics. Two measures of interaction between classes of intensity levels are defined on simple co-occurance matrices and are used to evaluate and select thresholds. One of these measures is seen to be independent of the grey level histogram and effective in selecting thresholds for images with unimodal grey level distributions. The algorithms are also used for multithresholding without modifications.     

Estimating probabilities under the three-parameter gamma distribution using composite sampling

Composite sampling may be used in industrial or environmental settings for the purpose of quality monitoring and regulation, particularly if the cost of testing samples is high relative to the cost of collecting samples. In such settings, it is often of interest to estimate the proportion of individual sampling units in the population that are above or below a given threshold value, C. We consider estimation of a proportion of the form p=P(X>C) from composite sample data, assuming that X follows a three-parameter gamma distribution. The gamma distribution is useful for modeling skewed data, which arise in many applications, and adding a shift parameter to the usual two-parameter gamma distribution also allows the analyst to model a minimum or baseline level of the response. We propose an estimator of p that is based on maximum likelihood estimates of the parameters α, β, and γ, and an associated variance estimator based on the observed information matrix. Theoretical properties of the estimator are briefly discussed, and simulation results are given to assess the performance of the estimator. We illustrate the proposed estimator using an example of composite sample data from the meat products industry.     

Image segmentation using a dynamic thresholding pyramid

An image segmentation algorithm based on multi-resolution processing is presented. The algorithm is based on applying a local clustering at each level of a linked pyramid data structure allowing seed nodes to be defined. These seed nodes are the root nodes of regions at the base of the pyramid, appearing in the multi-resolution data structure at a level appropriate to the region size. By applying a merging process followed by a classification step, accurate segmentations are obtained for both natural and synthetic images without the need for a priori knowledge. Results show that the algorithm gives accurate segmentations even in low signal to noise ratios.     

一种新的基于二维Tsallis熵的阈值方法

Tsallis熵首先出现在统计力学中。对于呈现远距离交互,长时间记忆以及具有不规则结构的物理系统来说,它的表达式中引入了一个实数q作为参数。在利用图像像素的灰度值和像素的邻域平均灰度值建立的二维直方图的基础上,提出了基于二维Tsallis熵的阈值方法;同时为解决计算复杂度高、运算时间长这一缺点,利用群体智能中的粒子群优化（PSO）算法来优化搜索分割阈值（t,s）的过程,其中t和s分别是图像的像素灰度阈值以及邻域平均灰度阈值。通过对真实图像的处理实验证明,该方法不仅能够对目标图像进行准确的分割,而且大大减少了运算时间。     

Image thresholding using type II fuzzy sets

Image thresholding is a necessary task in some image processing applications. However, due to disturbing factors, e.g. non-uniform illumination, or inherent image vagueness, the result of image thresholding is not always satisfactory. In recent years, various researchers have introduced new thresholding techniques based on fuzzy set theory to overcome this problem. Regarding images as fuzzy sets (or subsets), different fuzzy thresholding techniques have been developed to remove the grayness ambiguity/vagueness during the task of threshold selection. In this paper, a new thresholding technique is introduced which processes thresholds as type II fuzzy sets. A new measure of ultrafuzziness is also introduced and experimental results using laser cladding images are provided.     

Voiced/non-voiced speech classification using adaptive thresholding with bivariate EMD

This paper introduces a robust voiced/non-voiced (VnV) speech classification method using bivariate empirical mode decomposition (bEMD). Fractional Gaussian noise (fGn) is employed as the reference signal to derive a data adaptive threshold for VnV discrimination. The analyzing speech signal and fGn are combined to generate a complex signal which is decomposed into a finite number of complex-valued intrinsic mode functions (IMFs) by using bEMD. The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. The log-energies of both types of IMFs are calculated. There exist similarities between the IMF log-energy representation of fGn and unvoiced speech signals. Hence, the upper confidence limit from IMF log-energies of fGn is used as data adaptive threshold for VnV classification. If the subband log-energy of speech segment exceeds the threshold, the segment is classified as voiced and unvoiced otherwise. The experimental results show that the proposed algorithm performs better than the recently reported methods without requiring any training data for a wide range of SNRs.     

Reversible watermarking method based on adaptive thresholding and companding technique

Embedding and extraction of secret information as well as the restoration of the original un-watermarked image are highly desirable in sensitive applications such as military, medical, and law enforcement imaging. This paper presents a novel reversible watermarking approach for digital images using integer-to-integer wavelet transform, companding technique, and adaptive thresholding, enabling it to embed and recover the secret information as well as restore the image to its pristine state. The proposed method takes advantage of block-based watermarking and iterative optimization of threshold for companding which avoids histogram pre- and postprocessing. Consequently, it reduces the associated overhead usually required in most of the reversible watermarking techniques. As a result, it generates less distortion between the watermarked and the original image. Experimental results on regular as well as medical images show that the proposed method outperforms the existing reversible watermarking approaches reported in the literature.     

Grey-level thresholding of images using a correlation criterion

A new threshold selection technique using a correlation is presented here. An optimum threshold is selected by maximizing the correlation between the original halftone image and the threshold bilevel image. Representative experimental results are presented.     

Lossy volume compression using Tucker truncation and thresholding

Tensor decompositions, in particular the Tucker model, are a powerful family of techniques for dimensionality reduction and are being increasingly used for compactly encoding large multidimensional arrays, images and other visual data sets. In interactive applications, volume data often needs to be decompressed and manipulated dynamically; when designing data reduction and reconstruction methods, several parameters must be taken into account, such as the achievable compression ratio, approximation error and reconstruction speed. Weighing these variables in an effective way is challenging, and here we present two main contributions to solve this issue for Tucker tensor decompositions. First, we provide algorithms to efficiently compute, store and retrieve good choices of tensor rank selection and decompression parameters in order to optimize memory usage, approximation quality and computational costs. Second, we propose a Tucker compression alternative based on coefficient thresholding and zigzag traversal, followed by logarithmic quantization on both the transformed tensor core and its factor matrices. In terms of approximation accuracy, this approach is theoretically and empirically better than the commonly used tensor rank truncation method.     

利用云模型的血细胞图像阈值化方法

经典统计阈值方法直接利用类方差构造最优阈值准则,具有一定的通用性,但在某些情况下缺乏实际应用的针对性。为了解决血细胞图像阈值化及白细胞核提取问题,提出了一种利用云模型的简单快速方法。该方法分别生成白细胞核和血细胞背景对应的云模型,利用各类云模型的超熵定义了新的阈值化准则,然后通过最大化该准则自动获取最优灰度阈值,最终完成血细胞图像二值化及白细胞核提取。实验结果表明,与Otsu法、最大熵法、最小误差法、最小类内方差和法以及最小极大类内方差法等方法相比,新方法更适合于血细胞图像分割,二值化效果好,白细胞核提取质量高,具有合理性和有效性。     

A convenient way of generating gamma random variables using generalized exponential distribution

In this paper we propose a very convenient way to generate gamma random variables using generalized exponential distribution, when the shape parameter lies between 0 and 1. The new method is compared with the most popular Ahrens and Dieter method and the method proposed by Best. Like Ahrens and Dieter and Best methods our method also uses the acceptance-rejection principle. But it is observed that our method has greater acceptance proportion than Ahrens and Dieter or Best methods.     

基于正则割（Ncut）的多阈值图像分割方法

在图像处理与目标识别中广为应用的阈值法是图像分割的一种重要方法,因此如何确定阈值是图像分割的关键。提出了一种新的图像阈值分割方法,把图像的一维灰度直方图的灰度级L和对应灰度级L的概率P视为二维平面上的点（L,P）,采用新的相似度函数来定义这些点之间的相似度,从而构建基于灰度级的相似度矩阵,然后使用正则割（Ncut）进行分类,根据分类结果确定图像的分割阈值。算法用基于灰度级的权值矩阵代替基于像素级的权值矩阵来描述图像像素的关系,因而需要的存储空间及实现的复杂性大大减少;与现有的阈值分割方法相比,该算法能够单阈值和多阈值分割图像,因此具有更为优越的性能。     

Automated thresholding of active lavas using AVHRR data

An automated algorithm for thresholding hot pixels in AVHRR data is presented. The algorithm, applied to cloud-free sub-images of channel 3 minus channel 4 brightness temperature, compares each target pixel with its immediate background and then compares this difference with the natural variation in the surrounding region. Application to images of Mount Etna identified thermally anomalous pixels containing active lava or vents.     

Fuzzy entropy based optimal thresholding using bat algorithm

Image segmentation is a very significant process in image analysis. Much effort based on thresholding has been made on this field as it is simple and intuitive, commonly used thresholding approaches are to optimize a criterion such as between-class variance or entropy for seeking appropriate threshold values. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. This paper considers image thresholding as a constrained optimization problem and optimal thresholds for 1-level or multi-level thresholding in an image are acquired by maximizing the fuzzy entropy via a newly proposed bat algorithm. The optimal thresholding is achieved through the convergence of bat algorithm. The proposed method has been tested on some natural and infrared images. The results are compared with the fuzzy entropy based methods that are optimized by artificial bee colony algorithm (ABC), genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO); moreover, they are also compared with thresholding methods based on criteria of between-class variance and Kapur's entropy optimized by bat algorithm. It is demonstrated that the proposed method is robust, adaptive, encouraging on the score of CPU time and exhibits the better performance than other methods involved in the paper in terms of objective function values.