An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

He, Li; Yang, Jing; Kong, Bin; Wang, Can

doi:10.3390/app7060517

Cited by 12 publications

(3 citation statements)

References 25 publications

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“…Many studies have been conducted on body tracking and motion analysis using depth images [20][21][22][23]. There are two methods for creating depth images: extracting features from two-dimensional images and inferring depth through learning [24][25][26][27] or shooting with a 3D depth camera [28][29][30]. The former method has disadvantages in that an additional process is required to extract and learn features of an image, it takes a lot of time, and the accuracy is low.…”

Section: Motion Capture Systemmentioning

confidence: 99%

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

Kim

Nam

2021

Applied Sciences

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The proportion of welding work in total man-hours required for shipbuilding processes has been perceived to be significant, and welding man-hours are greatly affected by working posture. Continuous research has been conducted to identify the posture in welding by utilizing the relationship between man-hours and working posture. However, the results that reflect the effect of the welding posture on man-hours are not available. Although studies on posture recognition based on depth image analysis are being positively reviewed, welding operation has difficulties in image interpretation because an external obstacle caused by arcs exists. Therefore, any obstacle element must be removed in advance. This study proposes a method to acquire work postures using a low-cost RGB-D camera and recognize the welding position through image analysis. It removes obstacles that appear as depth holes in the depth image and restores the removed part to the desired state. The welder’s body joints are extracted, and a convolution neural network is used to determine the corresponding welding position. The restored image showed significantly improved recognition accuracy. The proposed method acquires, analyzes, and automates the recognition of welding positions in real-time. It can be applied to all areas where image interpretation is difficult due to obstacles.

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Section: Motion Capture Systemmentioning

confidence: 99%

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

Kim

Nam

2021

Applied Sciences

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“…Many passive stereo vision technologies have been proposed [6,7], but active stereo vision using structured light is one of the most popular optical 3D measurement techniques. It has high 3D scan data accuracy and fast data processing speed, but it has difficulties in expressing fine surface features.…”

Section: Introductionmentioning

confidence: 99%

A Photometric Stereo Using Re-Projected Images for Active Stereo Vision System

Jung

Kim

et al. 2017

Applied Sciences

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Abstract:In optical 3D shape measurement, stereo vision with structured light can measure 3D scan data with high accuracy and is used in many applications, but fine surface detail is difficult to obtain. On the other hand, photometric stereo can capture surface details but has disadvantages, in that its 3D data accuracy drops and it requires multiple light sources. When the two measurement methods are combined, more accurate 3D scan data and detailed surface features can be obtained at the same time. In this paper, we present a 3D optical measurement technique that uses re-projection of images to implement photometric stereo without an external light source. 3D scan data is enhanced by combining normal vector from this photometric stereo method, and the result is evaluated with the ground truth.

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“…Though shape from motion (SFM) [5], shape from shading (SFS) [6] and depth from focus or defocus (DFF/DFD) [7,8] are considered to be classical algorithms for monocular depth estimation, these methods are not widely used due to the device cost, high standard requirement for taking images, and the result is susceptible to occlusion and correspondence matching and so on. With the continuous improvement of computer performance and the gradual maturity of deep learning theory, deep learning is widely used in various fields [9,10], and it is possible to get depth information from a simple image like a binocular view [11,12]. Currently, to obtain the depth information of 2D images methods fall mainly into two major categories based on deep neural network according to whether vast quantities of corresponding ground truth depth data for training are required: one is the monocular depth estimation based supervised learning, another is the monocular depth estimation based unsupervised learning.In recent years, supervised deep learning approaches have demonstrated promising results for single image depth prediction, which appear to capture the statistical relationship between appearance and distance to objects well.…”

mentioning

confidence: 99%

Unsupervised Monocular Depth Estimation Based on Residual Neural Network of Coarse–Refined Feature Extractions for Drone

et al. 2019

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To take full advantage of the information of images captured by drones and given that most existing monocular depth estimation methods based on supervised learning require vast quantities of corresponding ground truth depth data for training, the model of unsupervised monocular depth estimation based on residual neural network of coarse–refined feature extractions for drone is therefore proposed. As a virtual camera is introduced through a deep residual convolution neural network based on coarse–refined feature extractions inspired by the principle of binocular depth estimation, the unsupervised monocular depth estimation has become an image reconstruction problem. To improve the performance of our model for monocular depth estimation, the following innovations are proposed. First, the pyramid processing for input image is proposed to build the topological relationship between the resolution of input image and the depth of input image, which can improve the sensitivity of depth information from a single image and reduce the impact of input image resolution on depth estimation. Second, the residual neural network of coarse–refined feature extractions for corresponding image reconstruction is designed to improve the accuracy of feature extraction and solve the contradiction between the calculation time and the numbers of network layers. In addition, to predict high detail output depth maps, the long skip connections between corresponding layers in the neural network of coarse feature extractions and deconvolution neural network of refined feature extractions are designed. Third, the loss of corresponding image reconstruction based on the structural similarity index (SSIM), the loss of approximate disparity smoothness and the loss of depth map are united as a novel training loss to better train our model. The experimental results show that our model has superior performance on the KITTI dataset composed by corresponding left view and right view and Make3D dataset composed by image and corresponding ground truth depth map compared to the state-of-the-art monocular depth estimation methods and basically meet the requirements for depth information of images captured by drones when our model is trained on KITTI.

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An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

Cited by 12 publications

References 25 publications

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

A Photometric Stereo Using Re-Projected Images for Active Stereo Vision System

Unsupervised Monocular Depth Estimation Based on Residual Neural Network of Coarse–Refined Feature Extractions for Drone

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