GMap.NET 기반 무인자동차 자율 주행 시뮬레이터

박의제(Ui-je Park),이준하(Junha Lee),한호택(Hotaek Han),정선영(Sunyoung Jeong),양지훈(Jihoon Yang),박성용(Sungyong Park),박수용(Sooyong Park),최범규(Bumkyoo Choi),김영록(Younglok Kim),박운상(Unsang Park) 한국자동차공학회 2013 한국자동차공학회 학술대회 및 전시회 Vol.2013 No.11

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

Park, Jeong-Seon,Lee, Sang-Woong,Park, Unsang Hindawi 2017 Journal of healthcare engineering Vol.2017 No.-

<P>Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.</P>

Age-Invariant Face Recognition

Unsang Park,Yiying Tong,Jain, A.K. IEEE 2010 IEEE transactions on pattern analysis and machine Vol.32 No.5

<P>One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). These shape and texture changes degrade the performance of automatic face recognition systems. However, facial aging has not received substantial attention compared to other facial variations due to pose, lighting, and expression. We propose a 3D aging modeling technique and show how it can be used to compensate for the age variations to improve the face recognition performance. The aging modeling technique adapts view-invariant 3D face models to the given 2D face aging database. The proposed approach is evaluated on three different databases (i.g., FG-NET, MORPH, and BROWNS) using FaceVACS, a state-of-the-art commercial face recognition engine.</P>

Dynamic Fringe Pattern Generation Using an Electrically Tunable Liquid Crystal Fabry-Perot Cell for a Miniaturized Optical 3-D Surface Scanning Profilometer

Park, Chang-Sub,Park, Kyung-Woo,Jung, Unsang,Kim, Jeehyun,Kang, Shin-Won,Kim, Hak-Rin Informa UK (TaylorFrancis) 2010 Molecular Crystals and Liquid Crystals Vol.526 No.1

Periocular Biometrics in the Visible Spectrum

Unsang Park,Jillela, Raghavender Reddy,Ross, A,Jain, A K IEEE 2011 IEEE transactions on information forensics and sec Vol.6 No.1

<P>The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.</P>

Face Matching and Retrieval Using Soft Biometrics

Unsang Park,Jain, A K IEEE 2010 IEEE transactions on information forensics and sec Vol.5 No.3

<P>Soft biometric traits embedded in a face (e.g., gender and facial marks) are ancillary information and are not fully distinctive by themselves in face-recognition tasks. However, this information can be explicitly combined with face matching score to improve the overall face-recognition accuracy. Moreover, in certain application domains, e.g., visual surveillance, where a face image is occluded or is captured in off-frontal pose, soft biometric traits can provide even more valuable information for face matching or retrieval. Facial marks can also be useful to differentiate identical twins whose global facial appearances are very similar. The similarities found from soft biometrics can also be useful as a source of evidence in courts of law because they are more descriptive than the numerical matching scores generated by a traditional face matcher. We propose to utilize demographic information (e.g., gender and ethnicity) and facial marks (e.g., scars, moles, and freckles) for improving face image matching and retrieval performance. An automatic facial mark detection method has been developed that uses (1) the active appearance model for locating primary facial features (e.g., eyes, nose, and mouth), (2) the Laplacian-of-Gaussian blob detection, and (3) morphological operators. Experimental results based on the FERET database (426 images of 213 subjects) and two mugshot databases from the forensic domain (1225 images of 671 subjects and 10 000 images of 10 000 subjects, respectively) show that the use of soft biometric traits is able to improve the face-recognition performance of a state-of-the-art commercial matcher.</P>

Robust Keypoint Detection Using Higher-Order Scale Space Derivatives: Application to Image Retrieval

Unsang Park,Jongseung Park,Jain, Anil K. IEEE Signal Processing Society 2014 IEEE signal processing letters Vol. No.

<P>Image retrieval has been extensively studied over the last two decades due to the increasing demands for the effective use of multimedia data. Among various approaches to image retrieval, scale space representation and local keypoint descriptors have been shown to be a promising approach. Even though the concept of scale space representation has been known for a long time, it has now gained prominence as a powerful method for image retrieval mostly due to the invention of the Scale Invariant Feature Transform (SIFT). We will review the characteristics of the scale space operation and provide an extended method of scale space operation that significantly improves the image matching accuracy in the context of image retrieval. We use an operational tattoo image database containing 1,000 near duplicate images to show the superior retrieval performance of the proposed method compared to SIFT keypoints.</P>

A simple new sparsity check method for L-infinity Attack

JuHoon Park,DongHee Han,UnSang Park 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10

There are various adversarial attacks on neural networks. Many previous studies have reported that neural networks are vulnerable to those adversarial attacks. In the perspective of safety, we have to evaluate a model's robustness against those attacks. However, it is not simple to determine whether a model is robust to adversarial attacks since there exist multiple properties for a model. Mostly, we have measured robustness with accuracy against those attacks so far. In a recent study, it was shown that this measurement methodology is not sufficient to capture all the robustness properties. In this paper, we present a simple new metric that captures sparsity, a robustness property against L-infinity sparse attacks. Through several experiments we show that there is necessity to use diverse metric to evaluate a model's robustness and our new metric works well.

스케일 공간 고차 미분의 정규화를 통한 특징점 검출 기법

박종승(Jongseung Park),박운상(Unsang Park) 한국정보과학회 2015 정보과학회논문지 Vol.42 No.1

이미지 검색 및 매칭에 사용되는 SIFT 기법은 다양한 이미지 변화 요인들에 대하여 강인한 특성을 가지고 있는 것으로 알려져 있다. SIFT 기법은 기존의 픽셀 단위의 변화량에 의존한 특징점 추출방식을 확장하여 스케일 공간에서의 변화량 분석을 통한 특징점 추출 방식을 제시하였으며, 이렇게 추출된 특징점들의 강인함은 그 동안 여러 실험을 통하여 입증되었다. 또한, 최근에는 스케일 공간 변화량 분석에 있어서 기존의 SIFT 기법을 확장하여 고차 미분 계수를 이용한 특징점 추출 방법도 소개되었다. 본 논문에서는 이러한 스케일 공간의 고차 미분에서의 정규화를 통한 보다 강인한 특징점 추출 기법을 소개하고 이러한 특징점들의 강인함을 이미지 검색 실험을 통하여 입증한다. The SIFT method is well-known for robustness against various image transformations, and is widely used for image retrieval and matching. The SIFT method extracts keypoints using scale space analysis, which is different from conventional keypoint detection methods that depend only on the image space. The SIFT method has also been extended to use higher-order scale space derivatives for increasing the number of keypoints detected. Such detection of additional keypoints detected was shown to provide performance gain in image retrieval experiments. Herein, a sigma based normalization method for keypoint detection is introduced using higher-order scale space derivatives.

GAN 기반 이미지 생성 모델 분석

박병건(Byeonggeon Park),이성준(Sungjun Lee),이재우(Jaewoo Lee),박운상(Unsang Park) 대한전기학회 2020 대한전기학회 학술대회 논문집 Vol.2020 No.7