Sungjun Hong

Sangyun Lee and Sungjun Hong. International Journal of Fuzzy Logic and Intelligent Systems 2022;22:339-49. https://doi.org/10.5391/IJFIS.2022.22.4.339

For an autonomous ship to navigate safely and avoid collisions with other ships, reliably detecting and classifying nearby ships under various maritime meteorological environments is essential. In this paper, a novel probabilistic ship detection and classification system based on deep learning is proposed. The proposed method aims to detect and classify nearby ships from a sequence of images. The method considers the confidence of a deep learning detector as a probability; the probabilities from the consecutive images are combined over time by Bayesian fusion. The proposed ship detection system involves three steps. In the first step, ships are detected in each image using Faster region-based convolutional neural network (Faster R-CNN). In the second step, the detected ships are gathered over time and the missed ships are recovered using the Intersection over Union of the bounding boxes between consecutive frames. In the third step, the probabilities from the Faster R-CNN are combined over time and the classes of the ships are determined by Bayesian fusion. To train and evaluate the proposed system, we collected thousands of ship images from Google image search and created our own ship dataset. The proposed method was tested with the collected videos and the mean average precision increased by 89.38 to 93.92% in experimental results.

Sungjun Hong, and Euntai Kim. IJFIS 2017;17:51-7. https://doi.org/10.5391/IJFIS.2017.17.2.51

Gait has received much interest from the biometric society in the vision field due to its utility in walker identification. In this paper, we present a regularized eigenspace-based gait recognition system for human identification. First, motion contour image (MCI) is extracted from walking sequences. In training phase, eigenfeature regularization and extraction (ERE) is applied to the gallery motion contour image to obtain regularized transformation matrix and gallery features. In test phase, regularized transformation matrix is applied to project motion contour image into the eigenspace to obtain probe features, and determine the identity based on the result of nearest neighbor classifier. Experiments are performed with the CASIA gait database A to evaluate the performance of the proposed gait recognition system. Experimental results justify the superiority of our proposed system in terms of correct classification rate.