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Hoang-Hon Trinh

Bio: Hoang-Hon Trinh is an academic researcher from Ho Chi Minh City University of Technology. The author has contributed to research in topics: Vanishing point & Geometric modeling. The author has an hindex of 1, co-authored 3 publications receiving 8 citations.

Papers
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Journal ArticleDOI
TL;DR: A method for the automated reconstruction of architectures from two views of a monocular camera using reference planes to estimate image homography instead of using the conventional camera pose estimation method and the texture of faces is mapped from 2D images to a 3D plane.
Abstract: In this paper, a method for the automated reconstruction of architectures from two views of a monocular camera is proposed. While this research topic has been studied over the last few decades, we contend that a satisfactory approach has not yet been devised. Here, a new method to solve the same problem with several points of novelty is proposed. First, reference planes are automatically detected using color, straight lines, and edge/vanishing points. This approach is quite robust and fast even when different views and complicated conditions are presented. Second, the camera pose and 3D points are accurately estimated by a two-view geometry constraint in the convex optimization approach. It has been demonstrated that camera rotations are appropriately estimated, while translations induce a significant error in short baseline images. To overcome this problem, we rely only on reference planes to estimate image homography instead of using the conventional camera pose estimation method. Thus, the problem associated with short baseline images is adequately addressed. The 3D points and translation are then simultaneously triangulated. Furthermore, both the homography and 3D point triangulation are computed via the convex optimization approach. The error of back-projection and measured points is minimized in L ∞-norm so as to overcome the local minima problem of the canonical L 2-norm method. Consequently, extremely accurate homography and point clouds can be achieved with this scheme. In addition, a robust plane fitting method is introduced to describe a scene. The corners are considered as properties of the plane in order to limit the boundary. Thus, it is necessary to find the exact corresponding corner positions by searching along the epipolar line in the second view. Finally, the texture of faces is mapped from 2D images to a 3D plane. The simulation results demonstrate the effectiveness of the proposed method for scenic images in an outdoor environment.

9 citations

Proceedings ArticleDOI
01 Nov 2017
TL;DR: A new robust method based on vehicle motion data to show the exact shape of the street without depending on the edge elements and the use of motion data is a sustainable method of extracting Vanishing Point without being affected by other side effects.
Abstract: In the intelligent transportation system, the geometry for the street is an important factor in vehicle monitoring. It helps to point out areas of interest, reduce computing costs, increased accuracy in detecting and identifying objects and facilitate data collection. In this paper, a new robust method of extracting the geometric model of the road is presented. The method is based on vehicle motion data to show the exact shape of the street without depending on the edge elements. First, the special features of consecutive frames are extracted and matched together. Second, by stretching the lines from matching these respective keypoints, intersections are indicated. The vanishing point is achieved by calculating the center of these intersections. Third, combining the infinity and the extremes of the motion data to tangent to the boundary of the geometry. Finally, by charting the area with the greatest matching ratio between the keypoints in the adjacent frame we reach the area of interest. Vietnam traffic dataset is used to verify the effectiveness and accuracy of the proposed method. Experimental results show that the proposed method has shown the correct geometry of the path. The use of motion data is a sustainable method of extracting Vanishing Point without being affected by other side effects.

2 citations

Proceedings ArticleDOI
01 Jun 2017
TL;DR: Experimental results show that the moving vehicles such as motorcycles, cars, buses and trucks have been detected and classified with high accuracy and the use of combined geometry and background models has significantly reduced the number of times sliding windows on each photo frame.
Abstract: This paper presents a new method for detecting moving vehicle based on Geometrical Model and Histograms of Oriented Gradient (HOG) features To do so, a geometrical model is built, which is used to reduce the size of the focus region and processing time This model has two components which are Road boundary and Background model Road boundary to redefine the limits of the focus region where the probability of vehicle detection on the road is the highest Background model helps indicate the candidate moving vehicle in the focus region The background is extracted and updated automatically by using the median method From the background and a new frame of video, the foreground is extracted as the candidate of the moving object Followed by, HOG feature pyramid is extracted from these region candidates Based on the score of the filters at different positions and scales, objects are detected and classified Finally, the Vietnam traffic dataset is used to verify the effectiveness and accuracy of the proposed method Experimental results show that the moving vehicles such as motorcycles, cars, buses and trucks have been detected and classified with high accuracy The use of combined geometry and background models has significantly reduced the number of times sliding windows on each photo frame

Cited by
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Journal ArticleDOI
TL;DR: In this paper , a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. And the proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.
Abstract: The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy’s effects become more apparent. When MPR ranges between 40% ~ 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.

48 citations

Journal ArticleDOI
TL;DR: A vision-based non-contact gap andflush measurement system that can deal with complex surface in noisy industrial environment and achieve higher specifications compared with current gap and flush measurement sensors is developed.
Abstract: The accurate fitting of various parts inspected by measuring the width of the gap between two adjacent panels and the alignment of the two surfaces, known as flushness, is an important task in assembling vehicles. The optimal solution requires high accuracy and fast measurement. Toward this end, we develop a vision-based non-contact gap and flush measurement. The vision system consists of a high-resolution camera and a multi-line laser generator. The proposed gap and flush measurement sensor projects laser lines onto the panels that are observed by the high-resolution camera. The measurement is initiated when the operator brings the device closer to the surface until it is within operating range. During the process, the line features are digitized by using proposed approach, the desired calculations are made, the non-conforming images are discarded, and the remaining images are used to perform the gap and flush measurement. The measurement system can deal with complex surface in noisy industrial environment and achieve higher specifications compared with current gap and flush measurement sensors. The usefulness of the proposed system has been demonstrated using real tests with accurate know-size patterns and a real inline vehicle assembly system in Korea.

22 citations

Journal ArticleDOI
TL;DR: A multi-directional scanning strategy is proposed where the AUV determines the direction of the next scan by analyzing the 3-D data of the object until the scanning is complete, which enables adaptive scanning based on the shape of the target object while reducing the amount of scanning time.
Abstract: In this study, we propose an autonomous underwater vehicle (AUV)-based multi-directional scanning method of underwater objects using forward scan sonar (FSS). Recently, a method was developed that can generate a 3-D point cloud of an underwater object using FSS. However, the data comprised sparse and noisy characteristics that made it difficult for 3-D recognition. Another limitation was the absence of back and side surface information of an object. These limitations degraded the results of the 3-D reconstruction. We propose a multi-directional scanning strategy to improve the 3-D point cloud and reconstruction results where the AUV determines the direction of the next scan by analyzing the 3-D data of the object until the scanning is complete. This enables adaptive scanning based on the shape of the target object while reducing the amount of scanning time. Based on the scanning strategy, a polygonal approximation method for real-time 3-D reconstruction is developed to process scanned data groups of the 3-D point cloud. This process can efficiently handle multiple 3-D point cloud data for real-time operation and reduce its uncertainty. To verify the performance of our proposed method, simulations were performed with various objects and conditions. In addition, experiments were conducted in an indoor water tank, and the results were compared with the simulation results. Field experiments were conducted to verify the proposed method for more diverse environments and objects.

17 citations

Book ChapterDOI
23 Mar 2020
TL;DR: Different from most existing image matching methods, the proposed method extracts image features using deep learning approach directly estimates locations of features between pairwise constraint of images by maximizing an image- patch similarity metric between images.
Abstract: Image stitching is an important task in image processing and computer vision. Image stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama, resolution image. It is widely used in object reconstruction, panoramic creating. In this paper, we present an approach based on deep learning for image stitching, which are applied to generate high resolution panoramic image supporting for virtual tour interaction. Different from most existing image matching methods, the proposed method extracts image features using deep learning approach. Our approach directly estimates locations of features between pairwise constraint of images by maximizing an image- patch similarity metric between images. A large dataset high resolution images and videos from natural tourism scenes were collected for training and evaluation. Experimental results illustrated that the deep feature approach outperforms.

17 citations

Journal ArticleDOI
TL;DR: A method for estimating the vision-based 3-D motion of a vehicle with several parts that requires only two corresponding points of consecutive images to estimate the vehicle motion and applies the bundle adjustment-based quasiconvex optimization.
Abstract: Recently, there have been several studies on vision-based motion estimation under a supposition that planar motion follows a nonholonomic constraint This allows reducing computational time However, the vehicle motion in an outdoor environment does not accept this assumption This paper presents a method for estimating the vision-based 3-D motion of a vehicle with several parts as follows First, the Ackermann steering model is applied to reduce constraint parameters of the 3-D motion In difference to the previous contribution, the proposed approach requires only two corresponding points of consecutive images to estimate the vehicle motion Second, motion parameters are extracted based on a closed-form solution on geometric constraints Third, the estimation approach applies the bundle adjustment-based quasiconvex optimization This task aims to take into account advantage of omnidirectional vision-based features for reducing errors The omnidirectional vision supports for landmarks tracking in long travel and large rotation, which is appropriate for a bundle adjustment technique Evaluated results show that the proposed method is applicable in the practical condition of outdoor environments

15 citations