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.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Ma, Yan; Chen, Lajiao; Liu, Peng; Lu, Ke: Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation (2016)
- Ohura, Ryuji; Minamoto, Teruya: A blind digital image watermarking method based on the dyadic wavelet packet transform and fast interval arithmetic techniques (2015)
- Rybář, Vojtěch; Vejchodský, Tomáš: On the number of stationary patterns in reaction-diffusion systems. (2015)
- Sundararajan, D.: Discrete wavelet transform. A signal processing approach (2015)
- Cerda, Mauricio; Girau, Bernard: Asymmetry in neural fields: a spatiotemporal encoding mechanism (2013)
- Gunturk, Bahadir Kursat (ed.); Li, Xin (ed.): Image restoration. Fundamentals and advances (2012)
- Soltanzadeh, Ramin; Rabbani, Hossein; Talebi, Ardeshir: Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform (2012)
- Schmeelk, John: Medical image edge detectors (2011)
- Martens, Gaëtan; Poppe, Chris; Lambert, Peter; Van de Walle, Rik: Noise- and compression-robust biological features for texture classification (2010)
- Goh, Wooi-Boon; Chan, Kai-Yun: The multiresolution gradient vector field skeleton (2007)
- Chang, Lena: Multispectral image compression using eigenregion-based segmentation (2004)