This section discusses the procedure for extracting scale-adapted local regions, i.e., interest points equipped with characteristic scales. The Laplacian detector extracts image regions whose locations and characteristic scales are given by scale-space maxima of the Laplace operator. The Harris detector uses the same operator for scale selection, but finds the locations of interest points as th...

The study presents a robust yet simple technique for the extraction of lips from human face. Harris corner detection is applied on a morphologically pre-processed human face and the location and dimension of the lips along the horizontal and vertical directions are obtained. The lip dimension thus found is compared with a predetermined dimension of the same obtained through ‘data cursor’ operat...

Through the installation of a monocular vision sensor on a target and shooting sequence images of the floor, combined with the characteristics of the sequence images, an indoor mobile visual localization algorithm based on Harris-SIFT is proposed. The algorithm is first to establish first-order DOG scale space of sequence images of the floor, to extract feature points in each layer images of DO...

Registration of optical and synthetic aperture radar (SAR) images is challenging because extracting located identically unique features on both are tricky. This paper proposes a novel SAR image registration method based relative total variation (RTV) scale-invariant feature transform (SIFT), named RTV-SIFT, to extract points the edges structures construct structural edge descriptors improve acc...

With the register detection problem, the color textile image segmentation algorithm based on MeanShift and the block matching algorithm based on Harris corner detection were proposed. The extended MeanShift algorithm was used to segment the textile image, and then different color regions were extracted from the segmented standard image; then the feature points were detected by Harris operator, ...

Water-cycle algorithm based on evaporation rate (ErWCA) is a powerful enhanced version of the water-cycle (WCA) metaheuristics algorithm. ErWCA, like other algorithms, may still fall in sub-optimal region and have slow convergence, especially high-dimensional tasks problems. This paper suggests an ErWCA (EErWCA) version, which embeds local escaping operator (LEO) as internal updating process. a...