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44 results for “Thresholding”
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Research ArticleOpen accessEvery word in the titleDetection of Plant Leaf Diseases Using K‒mean++ Intermeans Thresholding AlgorithmKittipol Wisaeng, Worawat Sa–NgiamviboolIn the field of agricultural information, the plant leaf disease detection is highly important for both farmer life and environment. To improve the accuracy of plant leaf disease detection and reduce the image processing time, the improved K‒mean++ clustering and intermeans thresholding method are proposed in this study. The proposed algorithms are used for training and testing diseases in plant … …Journal of Computer Science, Vol. 16, Issue 9 · 21 Sep 2020 · 6,750 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleImage Thresholding Using the Complement FeatureSalah AmeerA new feature (the complement feature) is proposed in an Eigen formulations for performing global image thresholding. The goal is to find an intensity or gray-level value below which is the background while above it is the foreground (object). Each pixel in the image is represented by a (2D) unit vector where the x-component is the normalized (to [0,1] or … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 2 · 8 Jun 2020 · 6,505 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleMulti-Level Image Thresholding via Nonlinear Fitting of the HistogramSalah AmeerThis paper proposes a novel scheme for multi-level image thresholding. The 1D cumulative histogram is curve-fitted to a polynomial of nonlinear basis function, the hyperbolic tangent (tanh). The thresholds are the positions of the minima of the first (odd) derivative of the fitting polynomial, since the histogram is the derivative of the cumulative histogram. The tanh function is considered as … …American Journal of Applied Sciences, Vol. 16, Issue 12 · 28 Dec 2019 · 7,533 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleHistogram Matching Schemes for Image ThresholdingSalah AmeerThis paper proposes several novel schemes for image thresholding. The idea is simply to compare the original image histogram to that of the thresholded image. Element by element comparison (sum of absolute difference between the two histograms) is found to be of better performance than a single feature (area or size) comparison. The optimum threshold is the one producing the … …American Journal of Engineering and Applied Sciences, Vol. 12, Issue 3 · 9 Sep 2019 · 7,872 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleTwo-Stage Estimation in Inverse Problems using Combined Wavelet Thresholding and Penalized Maximum LikelihoodRobert G Aykroyd, Hassan AljohaniInverse problems occur in a wide range of practical scientific investigations where the variables of interest are only observed indirectly, such as magnetic and seismic imaging in geophysics, electrical tomography in industrial process monitoring, or PET scanning in medicine. Linear inverse problems can be thought of as highly multivariate regression problems with strong multicollinearity where the aim is to interpret … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 29 Sep 2017 · 7,925 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation TechniqueSaleheh Heidari, Muhamad Taufik Abdullah, Lili Nurliana AbdullahIn automated pulmonary nodules extraction and lung disease diagnosis by image processing techniques, image segmentation is utilized as a primary and the most essential step of lung tumor analysis. But due to extensive similarity between pulmonary vessels, bronchus and arteries in lung region and the low contrast of the Computed-Tomography (CT) image the accuracy of lung tumor diagnosis is highly … …Journal of Computer Science, Vol. 11, Issue 1 · 17 Sep 2014 · 7,477 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleModified Curvelet Thresholding Algorithm for Image DenoisingAl-Dahoud Ali, Preety D. Swami, J. SinghaiProblem statement: This study introduced an adaptive thresholding method for removing additive white Gaussian noise from digital images. Approach: Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet transform methods which being nongeometrical lack sparsity and fail to show optimal rate of convergence. Results: Different behaviors of curvelet transform maxima of image and noise … …Journal of Computer Science, Vol. 6, Issue 1 · 31 Jan 2010 · 7,255 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleMultilevel Image Thresholding Using the Complement FeatureSalah AmeerA novel Eigen formulation is proposed for image segmentation. Each pixel is represented by a unit vector having the x-component as the normalized gray value of the pixel. The axes of inertia are simply the Eigen vectors of the auto-correlation matrix. The largest Eigen vector is used as the point of split. Each sub-image is further split in the same … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 3 · 28 Aug 2020 · 5,917 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSegmentation of Exudates via Color-based K-means Clustering and Statistical-based ThresholdingAtef Zaki Ghalwash, Aliaa Abdel-Halim Youssif, Ali Mohamed Nabil AllamThis paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image … …Journal of Computer Science, Vol. 13, Issue 10 · 14 Oct 2017 · 7,725 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAn Adaptive Detail Preserving Modified Progressive Pca Thresholding Based Image DenoisingN. Anilkumar, S. Radhakrishnan, Shaik Syed Basha et al.Radiologists use medical imaging tools to assess the anatomy and function of interior organs. However, medical images are frequently distorted by various types of noise generated during the capture, transmission, and reconstruction operations, which can dramatically reduce image quality and hide critical diagnostic features. As a result, developing effective denoising algorithms that suppress noise while maintaining structural information remains a … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 529 accessesView PDFCite