Curvature scale space corner detector with adaptive threshold and dynamic region of support

Corners play an important role in object identification methods used in machine vision and image processing systems. Single-scale feature detection finds it hard to detect both fine and coarse features at the same time. On the other hand, multi-scale feature detection is inherently able to solve this problem. This paper proposes an improved multi-scale corner detector with dynamic region of support, which is based on Curvature Scale Space (CSS) technique. The proposed detector first uses an adaptive local curvature threshold instead of a single global threshold as in the original and enhanced CSS methods. Second, the angles of corner candidates are checked in a dynamic region of support for eliminating falsely detected corners. The proposed method has been evaluated over a number of images and compared with some popular corner detectors. The results showed that the proposed method offers a robust and effective solution to images containing widely different size features.published_or_final_versio

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding followed by morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture feature vectors is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.Comment: 7 page

Метод сегментации перекрывающихся форменных элементов крови на микроскопических медицинских изображениях

Рассматривается решение задачи эритроцитометрии с использованием методов компьютерного зрени

Метод сегментации форменных элементов крови на микроскопических медицинских изображениях

Метод сегментации форменных элементов крови на микроскопических медицинских изображениях / В. М. Михелев [ и др.] // Информационные технологии в науке, образовании и производстве (ИТНОП-2020) : сб. материалов VIII междунар. науч.-техн. конф., Белгород, 24-25 сент. 2020 г. / М-во науки и высшего образования РФ, НИУ БелГУ ; отв. ред. Е. В. Болгова. - Белгород, 2020. - С. 327-330. - Библиогр.: с. 330.В статье рассматривается решение задачи эритроцитометрии с использованием методов компьютерного зрения. Предложен комбинированный подход для извлечения контурных доказательств, который основан на обнаружении вогнутых точек с помощью анализа кривизны, использовании проверки на вогнутость и эффективной процедуры поиск

Corner detector based on global and local curvature properties

This paper proposes a curvature-based corner detector that detects both fine and coarse features accurately at low computational cost. First, it extracts contours from a Canny edge map. Second, it computes the absolute value of curvature of each point on a contour at a low scale and regards local maxima of absolute curvature as initial corner candidates. Third, it uses an adaptive curvature threshold to remove round corners from the initial list. Finally, false corners due to quantization noise and trivial details are eliminated by evaluating the angles of corner candidates in a dynamic region of support. The proposed detector was compared with popular corner detectors on planar curves and gray-level images, respectively, in a subjective manner as well as with a feature correspondence test. Results reveal that the proposed detector performs extremely well in both fields. © 2008 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Faster and better: a machine learning approach to corner detection

The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is importand because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations [Schmid et al 2000]. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection, and using machine learning we derive a feature detector from this which can fully process live PAL video using less than 5% of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and very high quality.Comment: 35 pages, 11 figure