Scale Invariant Feature Transform

By Tony Lindeberg. Scholarpedia, 2012

596 citations recorded by the source corpus.

Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching developed by David Lowe (1999,2004). This descriptor as well as related image descriptors are used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.In its original formulation, the SIFT descriptor comprised a method for detecting interest points from a grey-level image at which statistics of local gradient directions of image intensities were accumulated to give a summarizing description of the local image structures in a local neighbourhood around each interest point, with the intention that this descriptor should be used for matching corresponding interest points between different images. Later, the SIFT descriptor has also been applied at dense grids (dense SIFT) which have been shown to lead to better performance for tasks such as object categorization and texture classification. The SIFT descriptor has also been extended from grey-level to colour images and from 2-D spatial images to 2+1-D spatio-temporal video.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/eab7aa438df70ecefc43c0a6abad1a6592a3b2a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Fan Liu
2. Shouheng Peng
3. Hansi Senaratne
4. Ning Yan
5. Amin Mobasheri
6. Zewen Li
7. Wenjie Yang
8. Ahmed Loai Ali

In Computer Vision and Pattern Recognition

1. Fan Liu
2. Shouheng Peng
3. Hansi Senaratne
4. Ning Yan
5. Amin Mobasheri
6. Zewen Li
7. Wenjie Yang
8. Ahmed Loai Ali

Citations in the corpus

• State of the Art in Defect Detection Based on Machine Vision (2021, background): “The scale-invariant feature transform (SIFT) is an image descriptor for image-based matching and recognition [133, 134].”
• A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020, methodology): “Different from the traditional feature extraction methods [17]–[19], CNN does not need to extract features manually.”
• A review of volunteered geographic information quality assessment methods (2017, methodology): “On a related work, Zhang and Kosecka (2006) used feature-based geometric matching using the image recognition software SIFT (Lindeberg 2012) to localize sample photos in urban environments.”