Scale-space color blob and ridge detection

Abstract

Feature detection in color images frequently consists in image conversion from color to grayscale and then one of grayscale detectors application. This approach has a few disadvantages: some features become indistinguishable in grayscale and features ordering based on response of grayscale detector do not accord with features order of importance from human's perception point of view. There are two essential contribu tions in this paper. First, the method for direct detection of blobs and ridges in color images is proposed. Sec ond, for scale space ridge detection we introduce a 3D non maxima suppression procedure (in two orthogo nal directions) which makes ridge detection simple and easy programmable in contrast to Lindeberg's auto matic scale selection approach. The proposed algorithms also produce estimates of blobs sizes and ridges width.

Colour key-point detection

“…Laplacian of Gaussian have been tested marginally on RGB images by Corso and Hager [14] and Unnikrichnan and Hebert [15]. Tahery and Drew [16] and Khanina et al [17] propose a colour adaptation of the Hessian approach. Everts et al [6] use the Gabor filters marginally.…”

Crab Larvae Counter Using Image Processing

“…Images obtained may contain fake noise or blobs or holes that may be caused by noise or lighting. Such foreign BLOB can interfere with the results of the BLOB analysis [12]. If the image contains several unfamiliar blobs, we must pre-process the image before using it.…”

Enhancing spatio-chromatic representation with more-than-three color coding for image description

“…In this case the blobs are obtained from each channel of the opponent space by using Lindeberg's blob detector [12]. Khanina et al [13,14] adapted the scale-space technique for color images and proposed to use the Hessian matrix. Ming and Ma [15] proposed a weighted multi-scale blob detector using a hybrid operator which combines the Laplacian and the determinant of the Hessian.…”