Image Recognition Model over Augmented Reality Based on Convolutional Neural Networks Through Color-Space Segmentation
Currently the image recognition and classification implementing Convolutional Neural Networks is highly used, where one of the most important factors is the identification and extraction of characteristics, events, among other aspects; but in many situations this task is left only in charge of the neural network, without establish and apply a previous phase of image processing that facilitates the identification of patterns. This can cause errors at the time of image recognition, which in critical mission scenarios such as medical evaluations can be highly sensitive. The purpose of this paper is to implement a prediction model based on convolutional neural networks for geometric figures classification, applying a previous phase of color-space segmentation as image processing method to the test dataset. For this, it will be carried out the approach, development and testing of a scenario focused on the image acquisition, processing and recognition using an AR-Sandbox and data analysis tools. Finally, the results, conclusions and future works are presented.
KeywordsImage acquisition Image processing Image recognition Convolutional neural network Dataset Loss function Accuracy ROC curve AR-SANDBOX
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