Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition on RGB-D Data
In this paper a modified dendrite morphological neural network (DMNN) is applied for 3D object recognition. For feature extraction, shape and color information were used. The first two Hu’s moment invariants are calculated based on 2D grayscale images, and color attributes were obtained converting the RGB (Red, Green, Blue) image to the HSI (Hue, Saturation, Intensity) color space. For testing, a controlled lab color image database and a real image dataset were considered. The problem with the real image dataset, without controlling light conditions, is that objects are difficult to segment using only color information; for tackling this problem the Depth data provided by the Microsoft Kinect for Windows sensor was used. A comparative analysis of the proposed method with a MLP (Multilayer Perceptron) and SVM (Support Vector Machine) is presented and the results reveal the advantages of the modified DMNN.
KeywordsDendrite morphological neural network 3D object recognition Kinect depth segmentation color classification
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