• Title/Summary/Keyword: Heatmap

Search Result 51, Processing Time 0.045 seconds

A Study on the Prediction of Buried Rebar Thickness Using CNN Based on GPR Heatmap Image Data (GPR 히트맵 이미지 데이터 기반 CNN을 이용한 철근 두께 예측에 관한 연구)

  • Park, Sehwan;Kim, Juwon;Kim, Wonkyu;Kim, Hansun;Park, Seunghee
    • Journal of the Korea institute for structural maintenance and inspection
    • /
    • v.23 no.7
    • /
    • pp.66-71
    • /
    • 2019
  • In this paper, a study was conducted on the method of using GPR data to predict rebar thickness inside a facility. As shown in the cases of poor construction, such as the use of rebars below the domestic standard and the construction of reinforcement, information on rebar thickness can be found to be essential for precision safety diagnosis of structures. For this purpose, the B-scan data of GPR was obtained by gradually increasing the diameter of rebars by making specimen. Because the B-scan data of GPR is less visible, the data was converted into the heatmap image data through migration to increase the intuition of the data. In order to compare the results of application of commonly used B-scan data and heatmap data to CNN, this study extracted areas for rebars from B-scan and heatmap data respectively to build training and validation data, and applied CNN to the deployed data. As a result, better results were obtained for the heatmap data when compared with the B-scan data. This confirms that if GPR heatmap data are used, rebar thickness can be predicted with higher accuracy than when B-scan data is used, and the possibility of predicting rebar thickness inside a facility is verified.

QCanvas: An Advanced Tool for Data Clustering and Visualization of Genomics Data

  • Kim, Nayoung;Park, Herin;He, Ningning;Lee, Hyeon Young;Yoon, Sukjoon
    • Genomics & Informatics
    • /
    • v.10 no.4
    • /
    • pp.263-265
    • /
    • 2012
  • We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.

Study of the Gaussian Mixture Joint-Adaptive Heatmap Regression for Top-Down Human Pose Estimation (관절 적응형 Gaussian Mixture 히트맵 회귀법을 이용한 하향식 사람 자세 추정에 관한 연구)

  • Ong, Zhun-Gee;Cho, Jungchan;Choi, Sang-il
    • Proceedings of the Korean Society of Computer Information Conference
    • /
    • 2022.07a
    • /
    • pp.35-36
    • /
    • 2022
  • 본 논문은 딥러닝 사람 자세 추정 모델이 사람의 관절 키포인트를 예측하는데 관절의 2차원 면적에 의해 키포인트별 𝜎, 즉, 표준 편차를 가지는 가우시안 커널(Gaussian Kernel)을 예측하는 방법을 제안한다. 각 관절 키포인트에 대해 다른 𝜎를 가지는 정답 히트맵(Ground Truth Heatmap)과 제안한 Gaussian Mixture Block를 모델에 추가해서 관절의 크기를 맞는 히트맵을 예측한다.

  • PDF

Design of a MOT model based on Heatmap Detection and Transformer to improve object tracking performance (객체 추적 성능향상을 위한 Heatmap Detection 및 Transformer 기반의 MOT 모델 설계)

  • Hyun-Sung Yang;Chun-Bo Sim;Se-Hoon Jung
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2023.05a
    • /
    • pp.461-463
    • /
    • 2023
  • 본 연구는 실시간 MOT(Multiple-Object-Tracking)의 성능을 향상시키기 위해 다양한 기법을 적용한 MOT 모델을 설계한다. 연구에서 사용하는 Backbone 모델은 TBD(Tracking-by-Detection) 기반의 Tracking 모델을 사용한다. Heatmap Detection을 통해 객체를 검출하고 Transformer 기반의 Feature를 연결하여 Tracking 한다. 제안하는 방법은 Anchor 기반의 Detection의 장시간 문제와 추적 객체 정보 전달손실을 감소하여 실시간 객체 추적에 도움이 될 것으로 사료된다.

Apple detection dataset with visibility and deep learning detection using adaptive heatmap regression (가시성을 표시한 사과 검출 데이터셋과 적응형 히트맵 회귀를 이용한 딥러닝 검출)

  • Tae-Woong Yoo;Dasom Seo;Minwoo Kim;Seul Ki Lee;Il-Seok, Oh
    • Smart Media Journal
    • /
    • v.12 no.10
    • /
    • pp.19-28
    • /
    • 2023
  • In the fruit harvesting field, interest in automatic robot harvesting is increasing due to various seasonality and rising harvesting costs. Accurate apple detection is a difficult problem in complex orchard environments with changes in light, vibrations caused by wind, and occlusion of leaves and branches. In this paper, we introduce a dataset and an adaptive heatmap regression model that are advantageous for robot automatic apple harvesting. The apple dataset was labeled with not only the apple location but also the visibility. We propose a method to detect the center point of an apple using an adaptive heatmap regression model that adjusts the Gaussian shape according to visibility. The experimental results showed that the performance of the proposed method was applicable to apple harvesting robots, with MAP@K of 0.9809 and 0.9801 when K=5 and K=10, respectively.

Table Structure Recognition using Borderline Heatmap Regression (딥러닝 기반의 표 경계선 히트맵 회귀를 이용한 표의 구조 인식)

  • Lee, EunJi;Park, Jaewoo;Koo, Hyung Il;Cho, Nam Ik
    • Proceedings of the Korean Society of Broadcast Engineers Conference
    • /
    • fall
    • /
    • pp.84-87
    • /
    • 2021
  • 본 논문에서는 딥러닝을 기반으로 문서영상에서 표 안의 셀 경계선을 히트맵 회귀(heatmap regression)로 추정함으로써 표의 구조를 인식하는 방법을 제안한다. 표는 기본적으로 행과 열로 이루어져 있기 때문에, 제안하는 방법에서는 먼저 1 차원 벡터 형태로 세로/가로 방향의 행/열 경계선 위치를 찾고, 이에 병합된 셀을 처리하기 위해 경계선이 그어져야 할 위치를 2 차원으로 추정한 결과를 적용하여 온전한 표의 경계선을 구한다. 이러한 구조를 통해 제안하는 방법은 표의 행과 열에 대한 정보를 효과적으로 이용함과 동시에, 복잡한 후처리 없이 병합된 셀을 처리할 수 있는 이점을 보인다. 실험은 1 차원의 행/열 경계선 위치를 반영하는 두 가지 방식에 대해 PubTabNet[11]에 대해 진행하여 결과를 보였다.

  • PDF

Evaluation of temperature effects on brake wear particles using clustered heatmaps

  • Shin, Jihoon;Yim, Inhyeok;Kwon, Soon-Bark;Park, Sechan;Kim, Min-soo;Cha, YoonKyung
    • Environmental Engineering Research
    • /
    • v.24 no.4
    • /
    • pp.680-689
    • /
    • 2019
  • Temperature effects on the generation of brake wear particles from railway vehicles were generated, with a particular focus on the generation of ultrafine particles. A real scale brake dynamometer test was repeated five times under low and high initial temperatures of brake discs, respectively, to obtain generalized results. Size distributions and temporal patterns of wear particles were analyzed through visualization using clustered heatmaps. Our results indicate that high initial temperature conditions promote the generation of ultrafine particles. While particle concentration peaked within the range of fine sized particles under both low and high initial temperature, an additional peak occurred within the range of ultrafine sized particles only under high initial temperature. The timing of peak occurrence also differed between low and high initial temperature conditions. Under low initial temperature fine sized particles were generated intensively at the latter end of braking, whereas under high initial temperature both fine and ultrafine particles were generated more dispersedly along the braking period. The clustered correlation heatmap divided particle sizes into two groups, within which generation timing and concentration of particles were similar. The cut-off point between the two groups was approximately 100 nm, confirming that the governing mechanisms for the generation of fine particles and ultrafine particles are different.

A Visualization System for Multiple Heterogeneous Network Security Data and Fusion Analysis

  • Zhang, Sheng;Shi, Ronghua;Zhao, Jue
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.10 no.6
    • /
    • pp.2801-2816
    • /
    • 2016
  • Owing to their low scalability, weak support on big data, insufficient data collaborative analysis and inadequate situational awareness, the traditional methods fail to meet the needs of the security data analysis. This paper proposes visualization methods to fuse the multi-source security data and grasp the network situation. Firstly, data sources are classified at their collection positions, with the objects of security data taken from three different layers. Secondly, the Heatmap is adopted to show host status; the Treemap is used to visualize Netflow logs; and the radial Node-link diagram is employed to express IPS logs. Finally, the Labeled Treemap is invented to make a fusion at data-level and the Time-series features are extracted to fuse data at feature-level. The comparative analyses with the prize-winning works prove this method enjoying substantial advantages for network analysts to facilitate data feature fusion, better understand network security situation with a unified, convenient and accurate mode.

Unleashing the Potential of Vision Transformer for Automated Bone Age Assessment in Hand X-rays (자동 뼈 연령 평가를 위한 비전 트랜스포머와 손 X 선 영상 분석)

  • Kyunghee Jung;Sammy Yap Xiang Bang;Nguyen Duc Toan;Hyunseung Choo
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2023.05a
    • /
    • pp.687-688
    • /
    • 2023
  • Bone age assessment is a crucial task in pediatric radiology for assessing growth and development in children. In this paper, we explore the potential of Vision Transformer, a state-of-the-art deep learning model, for bone age assessment using X-ray images. We generate heatmap outputs using a pre-trained Vision Transformer model on a publicly available dataset of hand X-ray images and show that the model tends to focus on the overall hand and only the bone part of the image, indicating its potential for accurately identifying the regions of interest for bone age assessment without the need for pre-processing to remove background noise. We also suggest two methods for extracting the region of interest from the heatmap output. Our study suggests that Vision Transformer holds great potential for bone age assessment using X-ray images, as it can provide accurate and interpretable output that may assist radiologists in identifying potential abnormalities or areas of interest in the X-ray image.

Application of Primary Rat Corneal Epithelial Cells to Evaluate Toxicity of Particulate Matter 2.5 to the Eyes (눈에 대한 미세먼지의 독성 평가를 위한 쥐 각막 상피 세포의 적용)

  • Kim, Da Hye;Hwangbo, Hyun;Lee, Hyesook;Cheong, Jaehun;Choi, Yung Hyun
    • Journal of Life Science
    • /
    • v.32 no.9
    • /
    • pp.712-720
    • /
    • 2022
  • The purpose of this study was to investigate the efficacy of rat corneal-derived epithelial cells as an in vitro model to evaluate the harmfulness of the cornea caused by particulate matter 2.5 (PM2.5). To establish an experimental model for the effect of PM2.5 on corneal epithelial cells, it was confirmed that primary cultured cells isolated from rat eyes were corneal epithelial cells through pan-cytokeratin staining. Our results showed that PM2.5 treatment reduced cell viability of primary rat corneal epithelial (RCE) cells, which was associated with the induction of apoptosis. PM2.5 treatment also increased the generation of reactive oxygen species due to mitochondrial dysfunction. In addition, the production of nitric oxide and inflammatory cytokines was increased in PM2.5-treated RCE cells. Furthermore, through heatmap analysis showing various expression profiling between PM2.5-exposed and unexposed RCE cells, we proposed five genes, including BLNK, IL-1RA, Itga2b, ABCb1a and Ptgs2, as potential targets for clinical treatment of PM-related ocular diseases. These findings indicate that the primary RCE cell line is a useful in vitro model system for the study of PM2.5-mediated pathological mechanisms and that PM2.5-induced oxidative and inflammatory responses are key factors in PM2.5-induced ocular surface disorders.