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To increase the precision of edge detection, several subpixel techniques had been proposed, including curve-fitting, moment-based, [23] [24] reconstructive, and partial area effect methods. [25] These methods have different characteristics. Curve fitting methods are computationally simple but are easily affected by noise.
In many applications, e.g., medical or satellite imaging, the edges are key features and thus must be preserved sharp and undistorted in smoothing/denoising. Edge-preserving filters are designed to automatically limit the smoothing at “edges” in images measured, e.g., by high gradient magnitudes.
Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image registration, video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition.
JPEG compression can introduce ringing artifacts at sharp transitions, which are particularly visible in text. This is a due to loss of high frequency components, as in step response ringing. JPEG uses 8×8 blocks , on which the discrete cosine transform (DCT) is performed.
Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera.By comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels.
A Python + Fortran 90 implementation of the Generalized Multiparticle Mie method, especially suited for plasmonics and near field computation. 2017 CELES A. Egel, L. Pattelli and G. Mazzamuto [21] MATLAB + CUDA Running on NVIDIA GPUs, with high performance for many spheres. 2020 QPMS M. Nečada [22] C, Python
Otsu's method performs well when the histogram has a bimodal distribution with a deep and sharp valley between the two peaks. [ 6 ] Like all other global thresholding methods, Otsu's method performs badly in case of heavy noise, small objects size, inhomogeneous lighting and larger intra-class than inter-class variance. [ 7 ]
Active contour model, also called snakes, is a framework in computer vision introduced by Michael Kass, Andrew Witkin, and Demetri Terzopoulos [1] for delineating an object outline from a possibly noisy 2D image.