Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.

/pdf/sealing-clay-text-segmentation-based-on-radon-like-features-4gs8096tf0.pdf

Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters

Soft computing is expected to play a gradually important role in many applications as well as image enhancement. The pattern for soft computing is the human brain. The soft computing evaluation has been principally strong with fuzzy logic. The fuzzy logic is information demonstration as a rule for managing of vagueness. In this paper the Multi-Dimensional optimized problem is addressed by discussing the best possible thresholding using fuzzy entropy for Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained best possible thresholding values for different levels of speckle noisy and low contrast images. The fuzzy entropy method has formed better results compared to bi-level and multi-level thresholding techniques.

Optimal thresholding for enhancement of low contrasted images using soft computing

In this study, the regions of interest of the suspected tumor in the breast magnetic resonance imaging (MRI) are detected using a new automatic global thresholding approach. As a key pre-processing stage alongside the tumor segmentation process, image thresholding is one of the most significant image processing techniques. In order to prepare the image for more precise tumor detection in the following stage of segmentation, it is crucial to identify the areas of any suspected tumor in the breast MRI. The proposed Mean Maximum Raw Thresholding approach (MMRT), which uses the sub-window frames concept to automatically seek the best threshold value that separates the image into two classes-suspected tumor locations and the background-considers the tumor intensity features. On 40 test images from the RIDER breast MRI dataset, the approach is implemented and tested. The results are then analyzed and recorded in relation to the dataset's Ground Truths using two assessment strategies: pixel-based assessment (Jaccard = 0.643 and Dice = 0.683 measures), and quality assessment (PSNR = 69.97 and MSE = 0.011 measures). The evaluation findings demonstrate that the suggested approach outperforms five common thresholding methods that were tried on the same dataset. 

Mean Maximum Raw Thresholding Algorithm for Detecting Suspected Regions of Interest of Tumors in Breast Magnetic Resonance Imaging

In this paper, we introduce an image segmentation framework which applies automatic threshoding selection using fuzzy set theory and fuzzy density model. With the use of different types of fuzzy membership function, the proposed segmentation method in the framework is applicable for images of unimodal, bimodal and multimodal histograms. The advantages of the method are as follows: (1) the threshoding value is automatically retrieved thus requires no prior knowledge of the image; (2) it is not based on the minimization of a criterion function therefore is suitable for image intensity values distributed gradually, for example, medical images; (3) it overcomes the problem of local minima in the conventional methods. The experimental results have demonstrated desired performance and effectiveness of the proposed approach.

Density Based Fuzzy Thresholding for Image Segmentation

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Density Based Fuzzy Thresholding for Image Segmentation