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- 实验方案
- 产品
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe, Japan (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - Combining Adaptive Thresholding and Region Filling for Xylene Spills Detection from Ultraviolet Images
摘要： Numerous marine chemical spill accidents have caused enormous damage to the marine ecological environment. Multiple researchers are engaged in providing effective method to detect chemical spill detection. In this paper, we compare the properties between ultraviolet (UV) images and visible images. It turns out that the characteristics of UV images help eliminate the background influence. Then, we develop a new algorithm for segmenting chemical spills from UV imagery. This algorithm, combining adaptive thresholding and region filling, can effectively solve the problem of uneven illumination than the Otsu and FCM algorithms. Moreover, this algorithm does well in time consuming. Experimental results on UV imagery demonstrate that our approach can accurately segment chemical spill without producing too much false alarms.
关键词： region filling,UV images,segmentation,adaptive thresholding,chemical spill
更新于2025-09-23 15:22:29
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Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding
摘要： Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.
关键词： Blood Vessel Detection,Vesselness Enhancement Filter,Adaptive Thresholding
更新于2025-09-23 15:22:29
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Digital acquisition and character extraction from stone inscription images using modified fuzzy entropy-based adaptive thresholding
摘要： Soft computing is an emerging technology, which is more powerful with fuzzy logic by choosing the degree of membership function. This work is an effort to extract the foreground character from stone inscription images using fuzzy logic. Differentiating the character pixel from the stone background is a challenging task. Moreover, several collections of stone inscriptions are available, but only few of them are estampaged and preserved in a document format, which are highly exposed to deterioration. The Department of Archeology, Government of Tamil Nadu, acquired the inscriptions by a manual method called wax rubbing, which is time-consuming. The major challenges faced in character extraction from the camera-captured stone inscriptions are dif?culties in perspective distortion, various light illumination, similar background and foreground, deteriorated stones, lack of text shape, size, and noise. Many binarization methods have been proposed for printed and handwritten document images, but no such work has been reported for stone inscription images. In this paper, a new stone inscription image enhancement system is proposed by combining Modi?ed Fuzzy Entropy-based Adaptive Thresholding (MFEAT) with degree of Gaussian membership function and iterative bilateral ?lter (IBF). Since there is a variation in stone color, the images are equally normalized and stretched by linear contrast stretching, followed by foreground extraction by MFEAT, and the resultant image after binarization includes some noise. Hence, IBF is used to remove unwanted noise by preserving the character edges. The proposed fuzzy system helps predicting uncertainty among the character and the background pixels. The results were tested on various light illumination images and achieved a good PSNR rate compared to other binarizing techniques.
关键词： Fuzzy adaptive thresholding,Character extraction,Inscription image,Contrast stretching,Digital image acquisition,Iterative bilateral ?lter
更新于2025-09-10 09:29:36
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Image noise reduction based on adaptive thresholding and clustering
摘要： In this paper, we present a novel image denoising method based on adaptive thresholding and k-means clustering. In this method, we adopt the adaptive thresholding technique as an alternative to the traditional hard-thresholding of the block-matching and 3D filtering (BM3D) method. This technique has a high capacity to adapt and change according to the amount of the noise. More precisely, in our method the soft-thresholding is applied to the areas with heavy noise, on the contrary the hard-thresholding is applied to the areas with slight noise. Based on the adaptation and stability of the adaptive thresholding, we can achieve optimal noise reduction and maintain the high spatial frequency detail (e.g. sharp edges). Owing to the capacity of k-means clustering in terms of finding the relevant candidate-blocks, we adopt this clustering at the last estimate to partition the denoised image into several regions and identify the boundaries between these regions. Applying k-means clustering will allow us to force the block matching to search within the region of the reference block, which in turn will lead to minimize the risk of finding poor matching. The main reason of applying the K-means clustering method on the denoised image and not on the noised image is specifically due to the flaw of accuracy in detecting edges in the noisy image. Experimental results demonstrate that the new algorithm consistently outperforms other reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, in the proposed algorithm the time consumption of the image denoising is less than that in the other reference algorithms.
关键词： Candidate-blocks,Block matching,Adaptive thresholding,Hard-thresholding,Reference-blocks,K-means clustering,Soft-thresholding
更新于2025-09-09 09:28:46
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[IEEE 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) - Shah Alam, Malaysia (2018.7.11-2018.7.12)] 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) - An Effective Enhancement and Segmentation of Coronary Arteries in 2D Angiograms
摘要： Vessel enhancement in two-dimensional angiogram images is an essential pre-requisite step towards the isolation of coronary arteries. Hessian-based filters are the most commonly used vessel enhancement filters; however, these filters are more sensitive to noise and suppress the bifurcation regions. Suppression of bifurcation regions results in disconnected vessels. In this study, we present a technique that enhances the arteries of the heart in 2D angiograms and also refines the noisy vesselness obtained through Frangi’s method by using guided filter which produces more enhanced image that can be used as an effective pre-processing step for binarization of the Frangi vessel response having less discontinuities and joint suppression. The proposed approach makes use of the guided filter which smooths the edges, and at the same time preserves the edges as well for the enhancement of vessels. Following this filter, an Adaptive thresholding is applied to segment the coronary arteries from the angiogram. The proposed method has been tested on real angiography images and the efficiency of the method has been shown qualitatively as well as quantitatively.
关键词： adaptive thresholding,guided filter,segmentation,vessel enhancement,coronary arteries
更新于2025-09-09 09:28:46