Sentence examples for based on scale invariant from inspiring English sources

Among such methods, the most recent and effective ones [8, 9] are those based on scale invariant feature transform (SIFT) [10].

In this paper, a new method for Farsi/Arabic automatic font recognition is proposed which is based on scale invariant feature transform (SIFT) method.

The better localization hit rate is demonstrated by comparing the proposed approach to previous work based on scale invariant feature matching and visual vocabulary.

However, in the recent work, authors propose the use of classification of training data based on scale invariant features and learn the class-dependent dictionaries instead of a single universal one or on multi-scales.

Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners  such as edge-driven dual-bootstrap iterative closest point and Harris-partial intensity invariant feature descriptor (PIIFD) respectivley have been shown to be robust in registering multimodal retinal images.

These choices were appropriate when using supervised learning approaches, e.g. 'support vector machines', but were unnecessary and degraded performance when using random-forest a method based on scale invariant decision rules.

Image forensic literature offers several examples of detectors for such manipulation[1]; among them, the most recent and effective ones[2, 3] are those based on scale-invariant feature transform (SIFT [4].

Wang et al. [ 191] presented a DFP mosaic algorithm based on Scale-Invariant Feature Transform (SIFT) feature [ 192] to overcome low contrast and geometric distortion between different fields of view of DFPs.

Lowe summed up the existing feature detection method based on invariants technology, in 2004, and formally proposed an image scaling, rotation, and even affine transformation for invariant image with local feature description operator based on scale space SIFT [ 22].

The proposed method is based on utilizing Scale Invariant Feature Transforms (SIFT) feature vectors, Bag of visual words (BOVW) and tree augmented naive Bayes (TAN) approach for classifying containers X-Ray images.

It is composed by a verification architecture based on the Scale-Invariant Feature Transform algorithm (SIFT) with a vocabulary tree, providing a scheme that scales efficiently to a large number of features.