Fast Normalized Cross-Correlation

By John P. Lewis. 2010

1,544 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b482ddd4ddfdd33a709f7f0663d3e5c116ff4d52

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Citations in the corpus

Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust (2008, methodology): “D map of values where each value is computed by cross-correlating the Gaussian dust model with a window of size sliding over the image, which will be referred to as NCC output.”
Vermiculate artefacts in image analysis of granular materials (2016, background): “Two popular measures are ‘normalised cross-correlation’ (NCC) (Lewis, 1995) and ‘zero normalised cross-correlation’ (ZNCC) (Pan et al., 2010).”
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch (2019, background): “Classic stereo approaches typically start by computing robust feature representations [38, 36, 31, 20, 41].”
Improved image-based deformation measurement for geotechnical applications (2016, methodology): “Initially, the displacement of the seed subset is estimated to the nearest integer pixel value using normalized cross correlation (NCC) (Lewis 1995).”
Optical flow modeling and computation: A survey (2015, methodology): “In [163] only the numerator is computed with FFT and the denominator is rewritten as a product of sums, independent of the position of the pixel and thus efﬁciently computable with integral images [83].”
Reverse-Engineering a Cryptographic RFID Tag (2008, methodology): “Our template matching is based on normalized crosscorrelation which is a well-known similarity test [14] and implemented using the MATLAB image processing library.”
Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis (2011, background): “The registration is based on the normalized cross correlation metric [74], which is appropriate for several reasons.”
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain (2008, background): “Note, however, that it could be possible to modify Lewis's fast normalized correlation measure (Lewis, 1995) to speed up this process even further.”
Fast Disparity Estimation Using Dense Networks (2018, methodology): “The local algorithms attempt to minimize a cost function between image patches such as in Sum of Absolute Differences (SAD), Sum of Squared Differences (SSD), and Normalized Cross Correlation (NCC) [11].”
Learning Generative Models with Visual Attention (2013, methodology): “rithm4. In comparison, a near state-of-the-art face detection system from OpenCV 2.4.9 obtains the same 97% accuracy. It uses Haar Cascades, which is a form of AdaBoost5. Normalized Cross Correlation [31] obtained 93% accuracy, while Euclidean distance template matching achieved an accuracy of only ”
PGSR: Planar-Based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction (2024, unknown): “We use the normalized cross correlation (NCC) [61] of patches in the reference frame and the neighboring frame to measure the photometric consistency: 3) Geometric Regularization Loss: Finally, the geometric regularization loss includes single-view geometric, multi-view geometric, and multi-view…”
Burst photography for high dynamic range and low-light imaging on mobile cameras (2016, methodology): “Similar to the way normalized cross-correlation can be accelerated [Lewis 1995], the L2 version of equation 1 can be computed with a box filter and a convolution:”
RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, background): “ARRSI detects feature points and performs normalised cross-correlation (NCC) [43] matching on the maximum moment map of PC.”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, unknown): “Landmarks are reprojected in the image and matched thanks to their patches with Normalized Cross-Correlation [156].”
Dissimilarity measures in detrital geochronology (2017, methodology): “This procedure was inspired by similar approaches in image processing (Lewis, 1995; Pan et al., 2009).”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, methodology): “Several algorithms are available to accomplish displacement extraction; these work in principle by template matching or by tracking either the contours of the constant phase or intensity through time [55,197,198].”
Jump (2016, methodology): “This can be accelerated using slidingwindow box filtering and FFTs, as has been done with normalized cross-correlation [Lewis 1995].”
A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration (2010, methodology): “Additionally, our optimizations are distinct from the well-known paper (Lewis, 1995) which optimized non-gradient-based CC assuming a constant neighborhood in one of the two images.”
Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation (2011, methodology): “Although NCC has been documented to be simple and reliable, a number of drawbacks have been reported as well (Lewis, 1995; Scambos et al., 1992; Zhao et al., 2006).”