Multichannel Image Classification Research Articles

CORROSION DETECTION FOR INDUSTRIAL OBJECTS: FROM MULTI-SENSOR SYSTEM TO 5D FEATURE SPACE

Abstract. Corrosion is a form of damage that often appears on the surface of metal-made objects used in industrial applications. Those damages can be critical depending on the purpose of the used object. Optical-based testing systems provide a form of non-contact data acquisition, where the acquired data can then be used to analyse the surface of an object. In the field of industrial image processing, this is called surface inspection. We provide a testing setup consisting of a rotary table which rotates the object by 360 degrees, as well as industrial RGB cameras and laser triangulation sensors for the acquisition of 2D and 3D data as our multi-sensor system. These sensors acquire data while the object to be tested takes a full rotation. Further on, data augmentation is applied to prepare new data or enhance already acquired data. In order to evaluate the impact of a laser triangulation sensor for corrosion detection, one challenge is to at first fuse the data of both domains. After the data fusion process, 5 different channels can be utilized to create a 5D feature space. Besides the red, green and blue channels of the image (1–3), additional range data from the laser triangulation sensor is incorporated (4). As a fifth channel, said sensor provides additional intensity data (5). With a multi-channel image classification, a 5D feature space will lead to slightly superior results opposed to a 3D feature space, composed of only the RGB channels of the image.

Supervised multichannel image classification algorithm using hierarchical histogram representation

Multichannel image processing, in particular, supervised classification, requires designing novel time effective algorithms, because in the most of the cases slightly dimension increase leads to significant processing time growth. In this paper we describe our supervised multichannel image classification algorithm based on a hierarchical representation of multivariate histograms. The algorithm estimates the joint sample set distribution, the particular distributions of each class and the decision rule by means of specific data structure called histogram-tree. Proposed algorithm provides faster learning and classification of multidimensional input data. The experimental evaluation of the algorithm has been conducted for the hyperspectral remote sensing images. The results demonstrate that proposed algorithm is faster than the commonly used C4.5 classifier.