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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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DEEP LEARNING-BASED OBSTACLE DETECTION AND DEPTH ESTIMATION
(2019-01-01)
Hsieh, Yi-Yu
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Lin, Wei-Yu
;
Li, Dong-Lin
;
Chuang, Jen-Hui
;
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.
A Fully Automatic Approach for Fisheye Camera Calibration
(2018-01-01)
Tai, Yen-Chou
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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.
A Light Deep Learning Based Method for Bank Serial Number Recognition
(2018-01-01)
Umam, Ardian
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Chuang, Jen-Hui
;
Li, Dong-Lin
;
資訊工程學系
;
電機工程學系
A full stage bank serial number (SN) recognition system is proposed in this paper. We introduce Block-wise Prediction Networks (BPN) to treat the localization of an SN as block-wise binary classification, which can be considered as a coarse version of dense/pixel-wise prediction used in semantic segmentation. The benefits include short execution time, which is equal to 85.22 ms in CPU, and the use of global features instead of local features to improve the segmentation. Our system then separates the localized Region of Interest (Rol) into individual characters, and feeds them into softmax CNN classifier. Experimental results show that the proposed method can achieve 99.92% and 99.24% accuracy for character and SN of Renminbi (RMB), respectively, tested with 2,368 two sides images of 1,184 RMB bills.
Fast Imaging in the Dark by using Convolutional Network
(2019-01-01)
Chiu, Mian Jhong
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Wang, Guo-Zhen
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Chuang, Jen-Hui
;
交大名義發表
;
資訊工程學系
While fast imaging in low-light condition is crucial for surveillance and robot applications, it is still a formidable challenge to resolve the seemingly inevitable high noise level and low photon count issues. A variety of image enhancement methods such as de-blurring and de-noising have been proposed in the past. However, limitations can still be found in these methods under extreme low-light condition. To overcome such difficulty, a learning-based image enhancement approach is proposed in this paper. In order to support the development of learning-based methodology, we collected a new low-lighting dataset (<0.1 lux) of raw short-exposure (6.67 ms) images, as well as the corresponding long-exposure reference images. Based on such dataset, we develop a light-weight convolutional network structure which is involved with fewer parameters and has lower computation cost compared with a regular-size network. The presented work is expected to make possible the implementation of more advanced edge devices, and their applications.
A Novel Egocentric Pointing System Based on Smart Glasses
(2017-01-01)
Hsieh, Yi-Yu
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Wei, Yu-Han
;
Chen, Kuan-Wen
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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.
AUTOMATIC GENERATION OF EPIPOLAR CURVES
(2018-01-01)
Umam, Ardian
;
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.
PEDESTRIAN DETECTION IN AERIAL IMAGES USING VANISHING POINT TRANSFORMATION AND DEEP LEARNING
(2018-01-01)
Chang, Ya-Ching
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Chen, Hua-Tsung
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Chuang, Jen-Hui
;
Liao, I-Chun
;
資訊工程學系
Drones are well-liked nowadays. However, deep learning models for object detection still cannot have high detection rates for pedestrians in aerial images even though they already show high precision on PASCAL VOC 2007. The main challenges of aerial image analysis include: (i) the size of an object in aerial images can be very small, and (ii) the objects in aerial images are tilted outward due to perspective projection deformation, which make the pedestrians hard to recognize in aerial images. In this paper, we utilize image partition and vanishing point transformation to overcome the above challenges. Experimental results demonstrate that such pre-processing methods can indeed increase the detection rates significantly for some deep learning models.
Fully Automatic Camera Calibration for Principal Point Using Flat Monitors
(2018-01-01)
Lu, Mu-Tien
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Chuang, Jen-Hui
;
交大名義發表
;
National Chiao Tung University
In this paper, a novel, fully automatic camera calibration procedure is proposed to estimate the camera principal point, i.e., the intersection of the optical axis and the image plane. The basic idea is to derive the orthogonal projection of the camera optical axis onto the flat-screen monitor via changing/analyzing simple edge patterns displayed on the flat monitor. The principal point can then be estimated as the intersection of images of line features thus derived on the screen, called calibration lines, from multiple monitor configurations. Thus, the calibration can be performed automatically using fixed camera and multiple monitors, or using multiple poses of a movable monitor. Several experiments are developed to evaluate the proposed method for accuracy as well as robustness, and the results show that the proposed approach compares favorably with some previous works, including the classic Zhang's algorithm that derives the principal point together with other camera parameters at the same time.
ESTHER: Joint Camera Self-Calibration and Automatic Radial Distortion Correction From Tracking of Walking Humans
(2019-01-01)
Tang, Zheng
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Lin, Yen-Shuo
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Lee, Kuan-Hui
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Hwang, Jenq-Neng
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Chuang, Jen-Hui
Camera calibration and radial distortion correction are the crucial prerequisites for many applications in image big data and computer vision. Many existing works rely on the Manhattan world assumption to estimate the camera parameters automatically; however, they may perform poorly when there was lack of man-made structure in the scene. As walking humans are the common objects in video surveillance, they have also been used for camera self-calibration, but the main challenges include the noise reduction for the estimation of vanishing points, the relaxation of assumptions on unknown camera parameters, and the radial distortion correction. In this paper, we present a novel framework for camera self-calibration and automatic radial distortion correction. Our approach starts with the reliable human body segmentation that is facilitated by robust object tracking. Mean shift clustering and Laplace linear regression are, respectively, introduced in the estimation of the vertical vanishing point and the horizon line. The estimation of distribution algorithm, an evolutionary optimization scheme, is then utilized to optimize the camera parameters and the distortion coefficients, in which all the unknowns in camera projection can be fine-tuned simultaneously. Experiments on the three public benchmarks and our own captured dataset demonstrate the robustness of the proposed method. The superiority of this algorithm is also verified by the capability of reliably converting 2D object tracking into 3D space.
Image super-resolution by estimating the enhancement weight of self example and external missing patches
(2018-08-01)
Lin, Fang-Ju
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Chuang, Jen-Hui
;
資訊工程學系
;
Department of Computer Science
Image super-resolution (SR) is the process of generating a high-resolution (HR) image using one or more low-resolution (LR) inputs. Many SR methods have been proposed, but generating the small-scale structure of an SR image remains a challenging task. We hence propose a single-image SR algorithm that combines the benefits of both internal and external SR methods. First, we estimate the enhancement weights of each LR-HR image patch pair. Next, we multiply each patch by the estimated enhancement weight to generate an initial SR patch. We then employ a method to recover the missing information from the high-resolution patches and create that missing information to generate a final SR image. We then employ iterative back-projection to further enhance visual quality. The method is compared qualitatively and quantitatively with several state-of-the-art methods, and the experimental results indicate that the proposed framework provides high contrast and better visual quality, particularly for non-smooth texture areas.

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