Head CT Image Convolution Feature Segmentation and Morphological Filtering for Densely Matching Points of IoTs

Morphological Filtering for Image Enhancement and Feature Detection

A Block-Grouping Method for Image Denoising by Block Matching and 3-D Transform Filtering

Image denoising by block matching and threedimensionaltransform filtering (BM3D) is a two steps state-ofthe-art algorithm that uses the redundancy of similar blocks innoisy image for removing noise. Similar blocks which can havesome overlap are found by a block matching method and groupedto make 3-D blocks for 3-D transform filtering. In this paper wepropose a new block grouping algorithm in th...

Image Correlation, Convolution and Filtering

This note discusses the basic image operations of correlation and convolution, and some aspects of one of the applications of convolution, image filtering. Image correlation and convolution differ from each other by two mere minus signs, but are used for different purposes. Correlation is more immediate to understand, and the discussion of convolution in section 2 clarifies the source of the mi...

Matching Feature Points for Telerobotics ∗

A system that quickly and reliably matches points from images of a same scene, taken by three different cameras is presented. The first step consists in the weak calibration of the camera system. Then feature points are iteratively selected and matched in the first two images, guided by a fundamental matrix. Each correspondence is then validated by computing the position of matches in the third...

Detecting and matching feature points

This paper proposes a new feature point detector which uses a wedge model to characterize corners by their orientation and angular width. This detector is compared to two popular feature point detectors: the Harris and SUSAN detectors, on the basis of some defined quality attributes. It is also shown how feature points between widely separated views can be matched by using the information provi...