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Titlebook: Image and Video Technology – PSIVT 2015 Workshops; RV 2015, GPID 2013, Fay Huang,Akihiro Sugimoto Conference proceedings 2016 Springer Nat

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41#
發(fā)表于 2025-3-28 14:45:51 | 只看該作者
42#
發(fā)表于 2025-3-28 20:13:24 | 只看該作者
Conference proceedings 2016ymposium on Image and Video Technology, PSIVT2015, during November 23-24, 2015, in Auckland, New Zealand..The 29 revised full papers presented werecarefully selected from 58 submissions. Their topics diversely ranged fromwell-established areas to novel current trends: robot vision, RV 2015; 2D and3D
43#
發(fā)表于 2025-3-29 02:04:33 | 只看該作者
44#
發(fā)表于 2025-3-29 05:22:28 | 只看該作者
Computer Vision Technology for Vehicular Robot to Follow Guided Track Using Neuro-Fuzzy Systemman driving skill of nonlinear relation between vanishing lines of guide lanes on the camera image and the steering angle of vehicular robot. In straight and curved road, the driving performances by the proposed control scheme are measured in simulation and experimental test.
45#
發(fā)表于 2025-3-29 10:33:50 | 只看該作者
46#
發(fā)表于 2025-3-29 12:41:58 | 只看該作者
47#
發(fā)表于 2025-3-29 16:56:25 | 只看該作者
48#
發(fā)表于 2025-3-29 22:55:09 | 只看該作者
Robust Segmentation of Aerial Image Data Recorded for Landscape Ecology Studiess paper, four segmentation methods are compared using images of native vegetation, introduced weeds and agriculture recorded from a quadcopter flown over a warm temperate island (Waiheke Island, New Zealand), and also images recorded from a fixed wing UAV in a polar desert (McMudro Dry Valleys, Anta
49#
發(fā)表于 2025-3-30 02:09:42 | 只看該作者
Integrated Parallel 2D-Leap-Frog Algorithm for Noisy Three Image Photometric Stereoan initial guess . for a non-linear 2D-Leap-Frog. Computationally, the integrability of non-integrable normals is enforced here by Conjugate Gradient which avoids numerous inversions of the large size matrices. In sequel, . is fed through to the adjusted version of non-linear 2D-Leap-Frog. Such sett
50#
發(fā)表于 2025-3-30 08:07:03 | 只看該作者
Multi-Run: An Approach for Filling in Missing Information of 3D Roadside Reconstructioncalculate the depth information of the tracked features; then compute the motion data between every two frames using a perspective-n-point solver. Additionally, we propose a technique that uses a Kalman-filter fusion to track the selected feature points, and to filter outliers. Furthermore, we use t
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