Wattanacheep, Bhattarabhorn
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Wattanacheep, Bhattarabhorn
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Email
bhattarabhorn.wa@kmitl.ac.th
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Item type:Publication, Plane alignment algorithm for torn document reconstruction(2015-08-17); Snippet alignment is a process to arrange pieces of the torn document to the original positions according to the direction of the alphabet line. It is a prerequisite to assure the effective reconstruction. The higher of the performance of the snippet alignment, the greater the opportunity for successful reconstruction. Therefore, this paper presents a plane alignment algorithm for torn document reconstruction. The proposed technique analyzes the contents inside the snippet such as the direction of the character alignment based on the histogram of the accumulated radius of the fitted ellipses. The direction result is then used to revert the snippet to its original position. Hough transform based local descriptor is extracted as shape feature. These parameters are helpful for accurate reconstruction. The proposed technique can achieve approximately 5.07 decrease in relative orientation error thus increase 24.11 percent in reverting precision. This can demonstrate the significant performance improvement of the proposed algorithm. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Prediction of 3D rotation and translation from 2D images(2019-07-27); 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.