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Author: search.filters.author.Chitsobhuk, OrachatSubject: search.filters.subject.Image Processing

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    Camera Pose Estimation using CNN
    (2020-08-23)
    Wattanacheep, Bhattarabhorn
    ;
    Chitsobhuk, Orachat
    Estimating camera pose is a significant process, which assures the success of the 3D modeling performance. This research presents a camera pose estimation using convolutional neural network (CNN) to transfer learning from pre-trained deep learning VGG19 model in order to extract features from a single image using several datasets captured in indoor and outdoor environments with diverse perspectives and photographic styles. Due to the large dimensions of the extracted features, Latent Semantic Analysis (LSA) are introduced prior to the CNN input. Then, the CNN is trained to predict the camera views and translations. The prediction performance is measured in terms of average mean square errors and compared to the reference techniques. As a result, the regression estimation of the proposed CNN model outperforms the others with average 0.24 degrees rotation error and 0.26 m. translation errors.
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    Prediction of 3D rotation and translation from 2D images
    (2019-07-27)
    Wattanacheep, Bhattarabhorn
    ;
    Chitsobhuk, Orachat
    The prediction of three-dimensional (3D) rotation and translation can be retrieved from two-dimensional (2D) images to build 3D models from large collections of images. In this paper, the process starts by extracting the features of images via transfer learning approach from Deep Neural Network model called VGG19. Even though the features extracted from VGG19 are usually adopted in image recognition application; in this research, we apply these features to the prediction model to obtain rotation and translation parameters. Due to the large size of the feature dimensions, it is necessary to perform dimensional reduction technique called latent semantic analysis (LSA) to decrease the feature dimensions and remain only the important ones. Then, the regression estimation technique based on the idea of Support Vector Machine (SVM) is used to predict the rotation and translation parameters. The accuracy is estimated by comparing the prediction results with the corresponding ground truth set. The average errors of rotation and translation of 3D prediction from 2D images are approximately 0.2419 degrees and 1.35 meters respectively.