Bangjun Lei

Maximum similarity thresholding

Otsu method is one of the most popular image thresholding methods. The segmentation results of Otsu method are in general acceptable for the gray level images with bimodal histogram patterns that can be approximated with mixture Gaussian modal. However, it is difficult for Otsu method to determine the reliable thresholds for the images with mixture non-Gaussian modal, such as mixture Rayleigh modal, mixture extreme value modal, mixture Beta modal, mixture uniform modal, comb-like modal. In order to determine automatically the robust and optimum thresholds for the images with various histogram patterns, this paper proposes a new global thresholding method based on a maximum-image-similarity idea. The idea is inspired by analyzing the relationship between Otsu method and Pearson correlation coefficient (PCC), which provides a novel interpretation of Otsu method from the perspective of maximizing image similarity. It is then natural to construct a maximum similarity thresholding (MST) framework by generalizing Otsu method with the maximum-image-similarity concept. As an example, a novel MST method is directly designed according to this framework, and its robustness and effectiveness are confirmed by the experimental results on 41 synthetic images and 86 real world images with various histogram shapes. Its extension to multilevel thresholding case is also discussed briefly.

Median-based thresholding, minimum error thresholding, and their relationships with histogram-based image similarity

A popular histogram-based thresholding method is minimum error thresholding (MET) proposed by Kittler and Illingworth [Minimum error thresholding, Pattern Recognition 19 (1) (1986) 41-47], whereas Xue and Titterington recently proposed a median-based thresholding (MBT) [Median-based image thresholding, Image and Vision Computing 29 (9) (2011) 631-637]. Both MET and MBT can be derived from the maximization of log-likelihood. In this paper, we present a different theoretical interpretation about MBT and MET, from the perspective of minimizing Kullback-Leibler (KL) divergence. Since the KL divergence is a measure of the difference between two probability distributions, it is reasonable to regard MET and MBT as the special applications of histogram-based image similarity (HBIS) in the image thresholding. Further, it is natural to suggest a more universal image thresholding framework based on image similarity concept, since HBIS is just one of many image similarity methodologies. This thresholding framework directly transforms the threshold determining problem into an image comparison issue. Its significance is that it provides a concise and clear theoretical framework for developing potential thresholding methods with the plentiful image similarity theories.

Comparison of Image Quality Objective Evaluation

Several image quality evaluation methods is analyzed, such as the conventional mathematical statistics methods, Mean Square Error (MSE), Peak Signal to Noise ratio (PSNR), as well as methods based on the HVS, Structural SIMilarity (SSIM), Objective Picture Quality Scale (PQS), Wavelet and Weighted Mean Square Error (WWMSE), Wavelet and Energy -Weighted Mean Square Error (WEWMSE). The above objective evaluations of image quality are compared with an image library labeled. Simulation results are presented. It is useful for selection of image quality methods in the practical application, design of more rational methods in the theory research. KeywordsImage Quality Evaluation; Human Visual System (HVS); Objective Evaluation; Wavelet Transform

Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB, 2nd Edition

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Structure similarity-guided image binarization for automatic segmentation of epidermis surface microstructure images: STRUCTURE SIMILARITY-GUIDED IMAGE BINARIZATION

Partitioning epidermis surface microstructure (ESM) images into skin ridge and skin furrow regions is an important preprocessing step before quantitative analyses on ESM images. Binarization segmentation is a potential technique for partitioning ESM images because of its computational simplicity and ease of implementation. However, even for some state‐of‐the‐art binarization methods, it remains a challenge to automatically segment ESM images, because the grey‐level histograms of ESM images have no obvious external features to guide automatic assessment of appropriate thresholds. Inspired by human visual perceptual functions of structural feature extraction and comparison, we propose a structure similarity‐guided image binarization method. The proposed method seeks for the binary image that best approximates the input ESM image in terms of structural features. The proposed method is validated by comparing it with two recently developed automatic binarization techniques as well as a manual binarization method on 20 synthetic noisy images and 30 ESM images. The experimental results show: (1) the proposed method possesses self‐adaption ability to cope with different images with same grey‐level histogram; (2) compared to two automatic binarization techniques, the proposed method significantly improves average accuracy in segmenting ESM images with an acceptable decrease in computational efficiency; (3) and the proposed method is applicable for segmenting practical EMS images. (Matlab code of the proposed method can be obtained by contacting with the corresponding author.)

A novel enhancement method for fog-degraded images based on DBLA

This letter presents a novel enhancement method for fog-degraded images based on dominant brightness level analysis in analyzing the characteristics of the images captured by daylight sensor on photoelectric radar surveillance system. We first perform discrete wavelet transform(DWT) on the input images and perform contrast limited adaptive histogram equalization(CLAHE) operation on LL sub-band, and then decompose the LL sub-band into low-,middle-,and high-intensity layers using Gaussian filter. After the intensity transformation and inverse DWT, the resulting enhanced image is obtained by using the guided filter.

Image thresholding based on Adjusted Rand Index

This paper proposes a new image thresholding method by integrating Multi-scale Gradient Multiplication (MGM) transformation and Adjusted Rand Index (ARI). The proposed method evaluates the optimal threshold by computing the accumulation similarity between two image collections from the perspective of global spatial attributes of images. One of the image collections are obtained by binarizing the original gray level image with each possible gray level. The others are the reference images, produced by binarizing MGM image. The MGM image is the result of applying MGM transformation to the original image. ARI is a similarity measurement in statistics, particularly in data clustering, which can be readily computed based on two image matrices. To be more accurate, the optimal threshold is determined by maximizing the accumulation similarity of ARI. Comparisons with three well established thresholding methods are depicted for numbers of real-world images. Experiment results demonstrate the effectiveness and robustness of the proposed method.

Image thresholding based on maximum mutual information

Thresholding segmentation is a critical preprocessing step on many image processing applications. However, most of the existing thresholding methods can only deal with an image with some special histogram patterns. To automatically determine the robust and optimum thresholds for the images with various histogram patterns, this paper proposes a new thresholding segmentation method based on maximum mutual information. The optimal threshold value is determined by maximizing the mutual information between a series of binary images and a reference image. The reference image is generated by a multi-scale gradient multiplication transformation on the original gray level image. Experiments on synthetic images and real images show the effectiveness and the accuracy of the proposed segmentation method.