• Journal of IKEEE
• /
• v.27 no.4
• /
• pp.486-493
• /
• 2023

In this paper, we propose an automatic hand gesture area extraction and recognition technique using FMCW radar-based point cloud and LSTM. The proposed technique has the following originality compared to existing methods. First, unlike methods that use 2D images as input vectors such as existing range-dopplers, point cloud input vectors in the form of time series are intuitive input data that can recognize movement over time that occurs in front of the radar in the form of a coordinate system. Second, because the size of the input vector is small, the deep learning model used for recognition can also be designed lightly. The implementation process of the proposed technique is as follows. Using the distance, speed, and angle information measured by the FMCW radar, a point cloud containing x, y, z coordinate format and Doppler velocity information is utilized. For the gesture area, the hand gesture area is automatically extracted by identifying the start and end points of the gesture using the Doppler point obtained through speed information. The point cloud in the form of a time series corresponding to the viewpoint of the extracted gesture area is ultimately used for learning and recognition of the LSTM deep learning model used in this paper. To evaluate the objective reliability of the proposed technique, an experiment calculating MAE with other deep learning models and an experiment calculating recognition rate with existing techniques were performed and compared. As a result of the experiment, the MAE value of the time series point cloud input vector + LSTM deep learning model was calculated to be 0.262 and the recognition rate was 97.5%. The lower the MAE and the higher the recognition rate, the better the results, proving the efficiency of the technique proposed in this paper.

• The Journal of Korea Robotics Society
• /
• v.11 no.3
• /
• pp.115-126
• /
• 2016

This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.

• Broadcasting and Media Magazine
• /
• v.26 no.2
• /
• pp.18-30
• /
• 2021

본 고에서는 최근 국제표준으로 완성된 MPEG Video-based Point Cloud Compression(V-PCC) 표준 기술에 대해 소개하고자 한다. AR/VR 등 새로운 미디어 응용의 출현과 함께 그 관심이 3D 그래픽 데이터에 더 많이 모아지는 가운데, 지금까지는 효율적인 압축에 관심이 높지 않았던 포인트 클라우드 데이터의 표준 압축 기술로 만들어진 V-PCC 표준의 표준화 현황과 주요 응용분야, 그리고 주요 압축 기술에 대하여 살펴보고자 한다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.179-182
• /
• 2007

In this study, we proposed a way to detect trees using virtual grid and to discriminate vertical wall points from building tops based on effective segmentation of LIDAR point cloud utilizing scan line characteristics. Trees were detected by their surface roughness value calculated based on virtual grid and vertical building wall points were discriminated from building tops with one dimensional filtering of scan line during segmenting point cloud. In results, we could distinguish trees from buildings and bind virtical wall points to prevent them from faltly acting on point segmentation process.

• Korean Journal of Computational Design and Engineering
• /
• v.12 no.2
• /
• pp.146-152
• /
• 2007

Presented in the paper is an algorithm to generate a section curve by slicing a point cloud which may include tens of thousands of points. Although there are couple of previous results, they are very sensitive on the density variations and local noising points. In the paper, three technological requirements are identified; 1) dominant point sampling, 2) avoiding local vibration, and 3) robustness on the density changes. To satisfy these requirements, we propose a new slicing algorithm which is based on a node-circle diagram. The algorithm has been implemented and tested with various examples.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.680-681
• /
• 2015

This study develops an algorithm that automatically performs reverse engineering on three-dimensional (3D) sweeping shapes using a user's pre-defined feature templates and 3D point cloud data (PCD) of sweeping shapes. Existing methods extract 3D sweeping shapes by extracting points on a PCD cross section together with the center point in order to perform curve fitting and connect the center points. However, a drawback of existing methods is the difficulty of creating a 3D sweeping shape in which the user's preferred feature center points and parameters are applied. This study extracts shape features from cross-sectional points extracted automatically from the PCD and compared with pre-defined feature templates for similarities, thereby acquiring the most similar template cross-section. Fitting the most similar template cross-section to sweeping shape modeling makes the reverse engineering process automatic.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.6
• /
• pp.157-166
• /
• 2022

Structural supervision on the construction site has been performed based on visual inspection, which is highly labor-intensive and subjective. In this study, the remote technique was developed to improve the efficiency of the measurements on rebar spacing using a 360° camera and reconstructed 3D models. The proposed method was verified by measuring the spacings in reinforced concrete structure, where the twelve locations in the construction site (265 m²) were scanned within 20 seconds per location and a total of 15 minutes was taken. SLAM, consisting of SIFT, RANSAC, and General framework graph optimization algorithms, produces RGB-based 3D and 3D point cloud models, respectively. The minimum resolution of the 3D point cloud was 0.1mm while that of the RGB-based 3D model was 10 mm. Based on the results from both 3D models, the measurement error was from 10.8% to 0.3% in the 3D point cloud and from 28.4% to 3.1% in the RGB-based 3D model. The results demonstrate that the proposed method has great potential for remote structural supervision with respect to its accuracy and objectivity.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.6
• /
• pp.542-553
• /
• 2020

Virtual world and simulation need large scale map rendering. However, rendering too many vertices is a computationally complex and time-consuming process. Some game development companies have developed 3D LOD objects for high-speed rendering based on distance between camera and 3D object. Terrain physics simulation researchers need a way to recognize the original object shape from 3D LOD objects. In this paper, we proposed simply automatic LOD framework using point cloud data (PCD). This PCD was created using a 6-direct orthographic ray. Various experiments are performed to validate the effectiveness of the proposed method. We hope the proposed automatic LOD generation framework can play an important role in game development and terrain physic simulation.

• Journal of the Korean Chemical Society
• /
• v.59 no.5
• /
• pp.407-412
• /
• 2015

A preconcentration procedure for determination of palladium by laser thermal lens spectrometry (TLS) is proposed. It is based on cloud point extraction of palladium(II) ions as 2-(3,5-dichloro-2-pyridylazo)-5-dimethylaminoaniline (3,5-diCl-PADMA) complexes using octylphenoxypolyethoxyethanol (Triton X-114) as surfactant. The effects of various experimental conditions such as pH, concentration of ligand and surfactant, equilibration temperature and time on cloud point extraction were studied. Under the optimized conditions, the calibration graph was linear in the range of 0.15~6 ng mL⁻¹, and the detection limit was 0.04 ng mL⁻¹ with an enrichment factor of 22. The sensitivity was enhanced by 846 times when compared with the conventional spectrophotometric method. The recovery of palladium was in the range of 96.6%~104.0%. The proposed method was applied to the determination of palladium in water samples.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.12
• /
• pp.77-86
• /
• 2015

It is difficult to extract small valve automatically in noisy 3D point cloud obtained from LIDAR because small object is affected by noise considerably. In this paper, we assume that the valve is a complex model consisting of torus, cylinder and plane represents handle, rib and center plane to extract a pose of the valve. And to extract the pose, we received additional input: center of the valve. We generated histogram of distance between the center and each points of point cloud, and obtain pose of valve by extracting parameters of handle, rib and center plane. Finally, the valve is reconstructed.