Relevant bibliographies by topics / Locally-adaptive thresholding

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  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Locally-adaptive thresholding'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Locally-adaptive thresholding"

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Evers, L., and T. J. Heaton. "Locally Adaptive Tree-Based Thresholding." Journal of Computational and Graphical Statistics 18, no. 4 (2009): 961–77. http://dx.doi.org/10.1198/jcgs.2009.07109.

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Sadatsharifi, Kasra, Mohamed A. Naiel, Mark Lamm, and Paul Fieguth. "Locally Adaptive Thresholding for Single-Shot Structured Light Patterns." Journal of Computational Vision and Imaging Systems 6, no. 1 (2021): 1–3. http://dx.doi.org/10.15353/jcvis.v6i1.3556.

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Image thresholding is a challenging task due to its sensitivity to environmental variations and degradation in the quality of the captured image. Although many image thresholding methods have been introduced, most of them require the fine tuning of a thresholding parameter that is not suitable for single-shot structured light (SSSL) based projector-camera applications. In this paper, we introduce a locally adaptive thresholding method with automatic parameter selection based on the local statistics of the distinct image partitions. For assessing the proposed scheme, we introduce an evaluation that relies on mapping SSSL patterns between the camera and projector spaces. Experimental results demonstrate the effectiveness of the proposed technique by maintaining the thresholding accuracy of the baseline method, without the need to fine tune the obtained thresholding parameter or any noticeable change in the qualitative results.
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Liu, Kang, Jian Zheng Cheng, and Li Cheng. "Locally Adaptive Wavelet Thresholding for Speech Enhancement." Applied Mechanics and Materials 380-384 (August 2013): 3618–22. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3618.

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There are strong dependencies between wavelet coefficients of speech signal,in this article,based on that,a new corresponding nonlinear threshold function derived in Bayesian framework is proposed to decrease the effect of the ambient noise.Analysis of the data shows the effectiveness of the proposed method that it removes white noise more effectually and gets better edge preservation.
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Hemachander, S., A. Verma, S. Arora, and Prasanta K. Panigrahi. "Locally adaptive block thresholding method with continuity constraint." Pattern Recognition Letters 28, no. 1 (2007): 119–24. http://dx.doi.org/10.1016/j.patrec.2006.06.005.

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Wen-Nung Lie. "Automatic target segmentation by locally adaptive image thresholding." IEEE Transactions on Image Processing 4, no. 7 (1995): 1036–41. http://dx.doi.org/10.1109/83.392347.

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Petrov, Miroslav Bulgaria. "CT image denoising based on locally adaptive thresholding." System research and information technologies, no. 4 (December 23, 2019): 39–48. http://dx.doi.org/10.20535/srit.2308-8893.2019.4.04.

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Heaton, T. J. "Adaptive thresholding of sequences with locally variable strength." Statistics and Computing 19, no. 1 (2008): 57–71. http://dx.doi.org/10.1007/s11222-008-9071-1.

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Chen, Cheng, Richard Leach, Jian Wang, Xiaojun Liu, Xiangqian Jiang, and Wenlong Lu. "Locally adaptive thresholding centroid localization in confocal microscopy." Optics Letters 46, no. 7 (2021): 1616. http://dx.doi.org/10.1364/ol.405443.

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Abdusalomov, Akmalbek, Mukhriddin Mukhiddinov, Oybek Djuraev, Utkir Khamdamov, and Taeg Keun Whangbo. "Automatic Salient Object Extraction Based on Locally Adaptive Thresholding to Generate Tactile Graphics." Applied Sciences 10, no. 10 (2020): 3350. http://dx.doi.org/10.3390/app10103350.

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Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visually impaired in identifying tactile information. In this paper, we introduce a novel saliency cuts method using local adaptive thresholding to obtain four regions from a given saliency map. First, we produced four regions for image segmentation using a saliency map as an input image and local adaptive thresholding. Second, the four regions were used to initialize an iterative version of the GrabCuts algorithm and to produce a robust and high-quality binary mask with a full resolution. Finally, salient objects’ outer boundaries and inner edges were detected using the solution from our previous research. Experimental results showed that local adaptive thresholding using integral images can produce a more robust binary mask compared to the results from previous works that make use of global thresholding techniques for salient object segmentation. The proposed method can extract salient objects with a low-quality saliency map, achieving a promising performance compared to existing methods. The proposed method has advantages in extracting salient objects and generating simple, important edges from natural scene images efficiently for delivering visually salient information to the visually impaired.
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Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

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Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, printed text, and handwritten text images. Threshold-based segmentation methods were investigated using misclassification error, MSE and PSNR. Experiments have shown that the Sauvola method outperforms real-world images, printed and handwritten text images in terms of misclassification error, PSNR, and MSE.
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Dissertations / Theses on the topic "Locally-adaptive thresholding"

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Hutchison, Luke Alexander Daysh. "Fast Registration of Tabular Document Images Using the Fourier-Mellin Transform." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/4.

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Image registration, the process of finding the transformation that best maps one image to another, is an important tool in document image processing. Having properly-aligned microfilm images can help in manual and automated content extraction, zoning, and batch compression of images. An image registration algorithm is presented that quickly identifies the global affine transformation (rotation, scale, translation and/or shear) that maps one tabular document image to another, using the Fourier-Mellin Transform. Each component of the affine transform is recovered independantly from the others, dramatically reducing the parameter space of the problem, and improving upon standard Fourier-Mellin Image Registration (FMIR), which only directly separates translation from the other components. FMIR is also extended to handle shear, as well as different scale factors for each document axis. This registration method deals with all transform components in a uniform way, by working in the frequency domain. Registration is limited to foreground pixels (the document form and printed text) through the introduction of a novel, locally adaptive foreground-background segmentation algorithm, based on the median filter. The background removal algorithm is also demonstrated as a useful tool to remove ambient signal noise during correlation. Common problems with FMIR are eliminated by background removal, meaning that apodization (tapering down to zero at the edge of the image) is not needed for accurate recovery of the rotation parameter, allowing the entire image to be used for registration. An effective new optimization to the median filter is presented. Rotation and scale parameter detection is less susceptible to problems arising from the non-commutativity of rotation and "tiling" (periodicity) than for standard FMIR, because only the regions of the frequency domain directly corresponding to tabular features are used in registration. An original method is also presented for automatically obtaining blank document templates from a set of registered document images, by computing the "pointwise median" of a set of registered documents. Finally, registration is demonstrated as an effective tool for predictive image compression. The presented registration algorithm is reliable and robust, and handles a wider range of transformation types than most document image registration systems (which typically only perform deskewing).
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Conference papers on the topic "Locally-adaptive thresholding"

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Kang Liu, Jianzheng Cheng, and Li Cheng. "Locally adaptive wavelet thresholding for speech enhancement." In 2012 4th Electronic System-Integration Technology Conference (ESTC). IEEE, 2012. http://dx.doi.org/10.1109/estc.2012.6485574.

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Boegel, Marco, Philip Hoelter, Thomas Redel, Andreas Maier, Joachim Hornegger, and Arnd Doerfler. "A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7318779.

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