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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Hsieh, Yi-Yu

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Now showing 1 - 8 of 8
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    DEEP LEARNING-BASED OBSTACLE DETECTION AND DEPTH ESTIMATION
    (2019-01-01)
    Hsieh, Yi-Yu
    ;
    Lin, Wei-Yu
    ;
    Li, Dong-Lin
    ;
    Chuang, Jen-Hui
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    資訊工程學系
    This paper proposed a modified YOLOv3 which has an extra object depth prediction module for obstacle detection and avoidance. We use a pre-processed KITTI dataset to train the proposed, unified model for (i) object detection and (ii) depth prediction and use the AirSim flight simulator to generate synthetic aerial images to verify that our model can be applied in different data domains. Experimental results show that the proposed model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset, while the unified approach can actually improve both (i) and (ii) at the same time.
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    A Fully Automatic Approach for Fisheye Camera Calibration
    (2018-01-01)
    Tai, Yen-Chou
    ;
    Hsieh, Yi-Yu
    ;
    Chuang, Jen-Hui
    ;
    資訊工程學系
    ;
    Department of Computer Science
    An automatic calibration procedure for a fisheye camera is presented in this paper by employing a flat panel monitor. The procedure does not require precise camera-monitor alignment, and any manual input of data or commands, making it useful for factory automation for mass production of such cameras. The fully automatic calibration procedure, which requires the generation of various test patterns on the display, and analysis of fisheye images of these patterns, consists of the following steps: (i) estimate the image center of the camera, (ii) identify the line on the monitor which intersects optical axis of the camera perpendicularly, and (iii) along the above line, obtain calibration data needed in de-warping the fisheye image. Experimental results demonstrate that the proposed approach performs satisfactorily in terms of effectiveness and accuracy.
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    A Novel Egocentric Pointing System Based on Smart Glasses
    (2017-01-01)
    Hsieh, Yi-Yu
    ;
    Wei, Yu-Han
    ;
    Chen, Kuan-Wen
    ;
    Chuang, Jen-Hui
    ;
    資訊工程學系
    In this paper, we propose a novel, egocentric pointing system based on Google Glass which is equipped with an optical head-mounted display (OHMD) and a near-eye camera, with the eye-pointing line passing through the lower left corner of the display. For a pointed target, the pointing (or ranging) algorithm is based on a distance-pixel curve established from the camera-eye (epipolar) geometry. Additional pointing algorithms for estimating gazing point on a planar surface are also developed by establishing another distance-pixel curve along the same epipolar line. Experiments show that less than 0.32 degrees angular error in the egocentric pointing can be achieved for object distance ranging from 80cm to 178cm by the best estimation scheme, with slightly less accurate results (i.e. 0.58 degrees) achievable by simpler estimation schemes.
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    AUTOMATIC GENERATION OF EPIPOLAR CURVES
    (2018-01-01)
    Umam, Ardian
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    Hsieh, Yi-Yu
    ;
    Marsya, Jiwa Malem
    ;
    Chuang, Jen-Hui
    ;
    資訊工程學系
    Fisheye camera is widely used in various applications because of its wider field-of-view. However, high distortion of images captured by fisheye cameras make it difficult for certain tasks which are based on traditional epipolar geometry (using epipolar lines) and stereo correspondence, such as depth map estimation. While most of existing depth map estimation methods use perspective-projection-based camera model, considering fisheye camera for depth map estimation will be beneficial because of its wider FOV. The availability of epipolar curves will be truly helpful for correspondence matching performed in depth map estimation. We propose a novel way of deriving epipolar curves using an auxiliary screen having benefits include: (i) deterministic results instead of statistical ones with accuracy within 1.5 pixels and (ii) the two epipoles as well as the epipolar curves in fisheye images can be obtained automatically, while unlimited number of the latter can be generated if needed.
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    DEEP LEARNING-BASED HUMAN ACTIVITY ANALYSIS FOR AERIAL IMAGES
    (2017-01-01)
    Wang, Han-Yang
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    Chang, Ya-Ching
    ;
    Hsieh, Yi-Yu
    ;
    Chen, Hua-Tsung
    ;
    Chuang, Jen-Hui
    Due to the advantages of high mobility and the ability to fly in the sky, drone has inspired more and more applications in recent years. On the other hand, deep learning-based human activity analysis is an important research topic in security surveillance; however, there are few research works on such analysis with aerial images so far. Because of perspective projection, people in aerial images look tilted, which would degrade the performance of human activity analysis. In order to cope with the issue of perspective projection for aerial images, we modify the CNN architecture of a state-ofthe-art object detection method, YOLOv2 [12], and build an aerial image dataset with a drone for new model training. Finally, a post -processing method is proposed to classify the pose of a detected person as normal or abnormal, so that the task of human activity analysis with aerial images can be accomplished.
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    Investigating Size Personalization for More Accurate Eye Tracking Glasses
    (2017-01-01)
    Hsieh, Yi-Yu
    ;
    Liu, Chia-Chen
    ;
    Wang, Wei-Lin
    ;
    Chuang, Jen-Hui
    ;
    交大名義發表
    Personalized eyewear frame could improve the accuracy of eye tracking. To obtain the personalized frame size (temple length), we propose a new measuring instrument that consists of (i) the hardware, a 3D printed trial frame which has marks but no scales, and (ii) the software, a vision-based measurement which is view invariant. The vision-based measurement has accuracy and precision that are both 0.02 cm, while the trial frame can achieve a precision of 0.17 cm for secure wearing. Moreover, dispersion up to 2.56 cm is obtained among the personalized frame sizes for just a fairly small group of users, indicating the importance of having such a personalized measurement system.
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    An Active Helper Searching Mechanism for Directional Cooperative Media Access Control (MAC)
    (2013)
    Hsieh, Yi-Yu
    ;
    Lai, Jiunn-Ru
    ;
    Chao, His-Lu
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Cooperative communication [1] and directional antenna systems are considered two key technologies for future wireless networks. There are also issues about the combination of these two technologies.[12,13] Most research in this area needed a table recording the near-by transmission. However, due to the property of passive overhearing, the table may only be partially good for the operation, especially for directional antenna systems. In this paper, we proposed an active helper searching mechanism to improve the completeness of the table. The helper activated its own helpers searching rather than waiting for the overhearing communication from others\'. We compared four schemes including the base 802.11g scheme, the D-NoopMAC scheme, the D-CoopMAC scheme with the CoopTable built by conventional methods and the D-CoopMAC scheme with D-CoTable built with our proposed active helper searching mechanism. The simulation results proved that coop-directional MAC schemes with the active helper scheme improved their performance. We also concluded that nodes with directional antenna should be regulated to help those omni-directional nodes to increase the total network throughput. We leaved node mobility issues as the future work.
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    INCORPORATING TEXTURE INFORMATION INTO REGION-BASED UNSUPERVISED IMAGE SEGMENTATION USING TEXTURAL SUPERPIXELS
    (2014)
    Hsu, Chih-Yu
    ;
    Hsieh, Yi-Yu
    ;
    Lo, Kuo-Hua
    ;
    Chuang, Jen-Hui
    ;
    資訊工程學系
    Recently, an unsupervised image segmentation framework, Segmentation by Aggregating Superpixels (SAS) is proposed and shown to be very promising. However, the texture cues, which have been shown to be very effective in many researches, are not used. In this paper, we propose an effective method for incorporating texture information into the SAS framework, using superpixels. To extract texture information, our algorithm first uses texture filtering and subsequently GMM clustering. Then, we develop an edge-aware low-pass filtering to generate multiple-scale textural superpixels (TXSPs) from the clustering results. Finally, by joining TXSPs with the superpixel set originally used in SAS, the incorporation of texture information is accomplished. Our method achieves superior performance on the Berkeley Segmentation Dataset (BSDS300) under several evaluation criteria when compared to other benchmark algorithms.