Image Processing Toolbox

Image Processing Toolbox™ provides a comprehensive set of reference-standard algorithms, functions, and apps for image processing, analysis, visualization, and algorithm development. You can perform image analysis, image segmentation, image enhancement, noise reduction, geometric transformations, and image registration. Many toolbox functions support multicore processors, GPUs, and C-code generation. Image Processing Toolbox supports a diverse set of image types, including high dynamic range, gigapixel resolution, embedded ICC profile, and tomographic. Visualization functions and apps let you explore images and videos, examine a region of pixels, adjust color and contrast, create contours or histograms, and manipulate regions of interest (ROIs). The toolbox supports workflows for processing, displaying, and navigating large images.


References in zbMATH (referenced in 22 articles )

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  1. Deng, Daiguo; Wu, Hefeng; Sun, Peng; Wang, Ruomei; Shi, Zhuo; Luo, Xiaonan: A new geometric modeling approach for woven fabric based on Frenet frame and spiral equation (2018)
  2. Bhandari, A.K.; Kumar, Anil; Singh, G.K.; Soni, Vivek: Dark satellite image enhancement using knee transfer function and gamma correction based on DWT-SVD (2016) ioport
  3. Ma, Yan; Chen, Lajiao; Liu, Peng; Lu, Ke: Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation (2016) ioport
  4. Ohura, Ryuji; Minamoto, Teruya: A blind digital image watermarking method based on the dyadic wavelet packet transform and fast interval arithmetic techniques (2015)
  5. Rybář, Vojtěch; Vejchodský, Tomáš: On the number of stationary patterns in reaction-diffusion systems. (2015)
  6. Sundararajan, D.: Discrete wavelet transform. A signal processing approach (2015)
  7. Cerda, Mauricio; Girau, Bernard: Asymmetry in neural fields: a spatiotemporal encoding mechanism (2013)
  8. Stanimirović, Predrag; Miladinović, Marko; Stojanović, Igor; Miljković, Sladjana: Application of the partitioning method to specific Toeplitz matrices (2013)
  9. Gocławski, Jarosław; Sekulska-Nalewajko, Joanna; Kuźniak, Elżbieta: Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses (2012)
  10. Gunturk, Bahadir Kursat (ed.); Li, Xin (ed.): Image restoration. Fundamentals and advances (2012)
  11. Soltanzadeh, Ramin; Rabbani, Hossein; Talebi, Ardeshir: Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform (2012)
  12. Schmeelk, John: Medical image edge detectors (2011)
  13. Mammarella, Marco; Campa, Giampiero; Napolitano, Marcello R.; Fravolini, Mario L.: Comparison of point matching algorithms for the UAV aerial refueling problem (2010) ioport
  14. Martens, Gaëtan; Poppe, Chris; Lambert, Peter; Van de Walle, Rik: Noise- and compression-robust biological features for texture classification (2010) ioport
  15. Radaelli, A.G.; Peiró, J.: On the segmentation of vascular geometries from medical images (2010)
  16. Gocławski, Jarosław; Sekulska-Nalewajko, Joanna; Gajewska, Ewa; Wielanek, Marzena: An automatic segmentation method for scanned images of wheat root systems with dark discolourations (2009)
  17. Miljković, Olga: Image pre-processing tool (2009)
  18. Hilewitz, Yedidya; Lee, Ruby B.: Fast bit gather, bit scatter and bit permutation instructions for commodity microprocessors (2008) ioport
  19. Cai, Weiling; Chen, Songcan; Zhang, Daoqiang: Fast and robust fuzzy $c$-means clustering algorithms incorporating local information for image segmentation (2007)
  20. Goh, Wooi-Boon; Chan, Kai-Yun: The multiresolution gradient vector field skeleton (2007)

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