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Investigating the Influence of Colour Spaces on Convolutional Neural Networks in Open-ended 3D Object Recognition

Mein, Anne-Jan (2021) Investigating the Influence of Colour Spaces on Convolutional Neural Networks in Open-ended 3D Object Recognition. Bachelor's Thesis, Artificial Intelligence.


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Due to the rising use of service robots there has been an increased interest in high performing 3D object recognition architectures for open-ended environments. These architectures have successfully been created with the use of Convolutional Neural Networks utilising an open-ended learning approach. Adding colour-information to the object representation has demonstrated to increase performance. This study aims to examine the influence of colour-spaces on neural networks with regards to open-ended object recognition. This has been done by converting the colour-information to the following colour-spaces: RGB, LAB, HSV, XYZ and YUV, as well as grayscaled images. These representations were then given as input for networks: MobileNetV2, vgg16 and ResNet50. Three rounds of experiments were performed with the first two rounds utilising an offline- and last round an online evaluation. The first round to determine the best hyperparameter configuration for each network The second round to compare the colour-spaces, resulting in each network using a different colour-space to reach their highest performance. The online evaluation showed that vgg16 combined with the YUV colour-space achieved the highest object recognition performance in an open-ended setup. These results indicate that choosing the choosing the correct colour-space for an object recognition architecture utilising a Convolutional NeuralNetwork can lead to a perrmance increase with regards to open-ended object recognition.

Item Type: Thesis (Bachelor's Thesis)
Supervisor name: Mohades Kasaei, S.H.
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 07 Jul 2021 10:34
Last Modified: 07 Jul 2021 10:34

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