Convex Objects Recognition and Classification Using Spectral and Morphological Descriptors (original) (raw)

Morphological description of colour images for content-based image retrieval

2019

Placed within the context of content-based image retrieval, we study in this paper the potential of morphological operators as far as color description is concerned, a booming field to which the morphological framework, however, has only recently started to be applied. More precisely, we present three morphology-based approaches, one making use of granulometries independently computed for each subquantized color and two employing the principle of multiresolution histograms for describing color, using respectively morphological levelings and watersheds. These new morphological color descriptors are subsequently compared against known alternatives in a series of experiments, the results of which assert the practical interest of the proposed methods.

Object Recognition Using Colour, Shape and Affine Invariant Ratios

British Machine Vision Conference, 1996

This paper describes a spectral-spatial model (for colour object recognition) which exploits the shape, colour and position of regions on the surface of a rigid object in describing it. Given a model and test image (with colour constancy pre-processing) suitably segmented into colour regions, model and test regions with similar shape and colour are identified. If at least three model

Color Image Segmentation and Recognition based on Shape and Color Features

2014

Recent advances in computer technology have made it possible to create databases for large number of images. A major approach directed towards achieving CBIR is the use of low-level visual features of the image data to segment, index and retrieve relevant images from the image database. Segmentation is the partition of a digital image into regions to simplify the image representation into something that is more meaningful and easier to analyze. Color based segmentation is significantly affected by the choice of color space. In different color spaces, the L*a*b color space is a better representation of the color content of an image. In this paper the L*a*b color space and K- means algorithm is used for segmentation of color images. Shape description or representation is an important issue both in object recognition and classification. After segmentation this paper focuses on the shape descriptor- eccentricity and color features for achieving efficient and effective retrieval performa...

Color Image Indexing Using Mathematical Morphology

Analele Universitatii "Ovidius" Constanta - Seria Matematica, 2014

A mathematical morphology based approach for color image indexing is explored in this paper. Morphological signatures are powerful descriptions of the image content in the framework of mathematical morphology. A morphological signature (either a pattern spectrum or a differential morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element. For image indexing it is considered a morphological feature extraction algorithm which includes more complex morphological operators: i.e. color gradient, homotopic skeleton, Hit-or-Miss transform. In the end, illustrative application examples of the presented approach on real acquired images are also provided.

On morphological color texture characterization

2007

Abstract We investigate the combined use of multiple structuring elements with the standard morphological texture characterization tools, namely morphological covariance and granulometry. The resulting operator is applied to both grayscale and color images in the context of texture classification. As to its extension to color texture data, it is realized by means of a weighting based reduced vector ordering in the IHLS color space, equipped with genetically optimized arguments.