• Title/Summary/Keyword: Hand image processing

Search Result 233, Processing Time 0.036 seconds

Diagnosis of HSC Convective Flow Using a Digital Holographic Interferometry and PIV System (디지털 홀로그래픽 간섭계와 PIV를 이용한 Hele-Shaw Cell 내부 열유동 해석)

  • Kim, Seok;Lee, Sang-Joon
    • Transactions of the Korean Society of Mechanical Engineers B
    • /
    • v.28 no.4
    • /
    • pp.493-499
    • /
    • 2004
  • Variations of temperature and velocity fields in a Hele-Shaw convection cell (HSC) were investigated using a holographic interferometry and 2-D PIV system with varying Rayleigh number. To measure quasi-steady variation of temperature field, two different measurement methods of holographic interferometry, double-exposure method and real-time method, were employed. In the double-exposure method, unwanted waves were eliminated effectively using a digital image processing technique. The reconstructed images are clear, but transient flow cannot be reconstructed clearly. On the other hand, transient convective flow can be reconstructed well using the real-time method. However, the fringe patterns reconstructed by the real-time method contain more noises, compared with the double-exposure method. Experimental results show a steady flow pattern at low Rayleigh numbers and a time-dependent periodic flow structure at high Rayleigh numbers. The periodic flow pattern at high Rayleigh numbers obtained by the real-time holographic interferometer method is in a good agreement with the PIV results.

Evaluation of Bond Performance for AC overlay on PCC Pavement (AC / PCC 복합포장 경계면 재료의 부착 성능 평가)

  • Kim, Dong kyu;Hwang, Hyun sik;Christopher, Jabonero;Ryu, Sung woo;Cho, Yoon ho
    • International Journal of Highway Engineering
    • /
    • v.18 no.5
    • /
    • pp.1-9
    • /
    • 2016
  • PURPOSES : This study focuses on the evaluation of interface performance with varying surface texture and tack coat application in an asphalt overlay. METHODS : The evaluation is carried out in two phases: tracking test and interface bond strength test. Using an image processing tool, tracking test is conducted to evaluate the susceptibility of the tack coat material to produce excessive tracking during application. Using the pull-off test method, the bond strength test is performed to determine the ability of the interface layer to resist failure. RESULTS : Results show that the underseal application yields less tracking compared to other applications. However, the bond strength is barely within the minimum acceptable value. On the other hand, RSC-4 produces higher bond strength for all surface types, but the drying time is long, which produces excessive tracking. CONCLUSIONS : While underseal application may be suitable for a trackless condition, the bond strength is less appealing compared to the rest of the tack applications available. RSC-4 demonstrated a high and consistent bond strength performance, but more time is required for drying to avoid excessive tracking. Tack coat application and surface type combination produce varying results. Therefore, these should be considered when selecting suitable future tack coat application options.

Text Region Extraction and OCR on Camera Based Images (카메라 영상 위에서의 문자 영역 추출 및 OCR)

  • Shin, Hyun-Kyung
    • The KIPS Transactions:PartD
    • /
    • v.17D no.1
    • /
    • pp.59-66
    • /
    • 2010
  • Traditional OCR engines are designed to the scanned documents in calibrated environment. Three dimensional perspective distortion and smooth distortion in images are critical problems caused by un-calibrated devices, e.g. image from smart phones. To meet the growing demand of character recognition of texts embedded in the photos acquired from the non-calibrated hand-held devices, we address the problem in three categorical aspects: rotational invariant method of text region extraction, scale invariant method of text line segmentation, and three dimensional perspective mapping. With the integration of the methods, we developed an OCR for camera-captured images.

Binary Classification of Hypertensive Retinopathy Using Deep Dense CNN Learning

  • Mostafa E.A., Ibrahim;Qaisar, Abbas
    • International Journal of Computer Science & Network Security
    • /
    • v.22 no.12
    • /
    • pp.98-106
    • /
    • 2022
  • A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

A Study on the Tracking Algorithm for BSD Detection of Smart Vehicles (스마트 자동차의 BSD 검지를 위한 추적알고리즘에 관한 연구)

  • Kim Wantae
    • Journal of Korea Society of Digital Industry and Information Management
    • /
    • v.19 no.2
    • /
    • pp.47-55
    • /
    • 2023
  • Recently, Sensor technologies are emerging to prevent traffic accidents and support safe driving in complex environments where human perception may be limited. The UWS is a technology that uses an ultrasonic sensor to detect objects at short distances. While it has the advantage of being simple to use, it also has the disadvantage of having a limited detection distance. The LDWS, on the other hand, is a technology that uses front image processing to detect lane departure and ensure the safety of the driving path. However, it may not be sufficient for determining the driving environment around the vehicle. To overcome these limitations, a system that utilizes FMCW radar is being used. The BSD radar system using FMCW continuously emits signals while driving, and the emitted signals bounce off nearby objects and return to the radar. The key technologies involved in designing the BSD radar system are tracking algorithms for detecting the surrounding situation of the vehicle. This paper presents a tracking algorithm for designing a BSD radar system, while explaining the principles of FMCW radar technology and signal types. Additionally, this paper presents the target tracking procedure and target filter to design an accurate tracking system and performance is verified through simulation.

Measurement methodology for the alignment accuracy of wafer stepper (웨이퍼 스텝퍼의 정렬정확도 측정에 관한 연구)

  • Lee, Jong-Hyun;Jang, Won-Ick;Lee, Yong-Il;Kim, Doh-Hoon;Choi, Boo-Yeon;Nam, Byung-Ho;Kim, Sang-Cheol;Kim, Jin-Hyuk
    • Journal of the Korean Society for Precision Engineering
    • /
    • v.11 no.1
    • /
    • pp.150-156
    • /
    • 1994
  • To meet the process requirement of semiconductor device manufacturing, it is necessary to improve the alignment accuracy in exposure equipments. We developed the excimer laser stepper and will describe the methodology for alignment measurement and experimental results. Our wafer alignment system consists of off-axis optics, TTL(Through The Lens) optics and high precision stage. Off-axis alignment utilizes the image processing and /or diffraction from thealign marks of off-centered chip area. On the other hand, TTL alignment can be used for the die-by-die alignment using dual beam interferometry. When only off-axis alignment was used, the experimental alignment error(lml+3 .sigma. ) was 0.26-0.29 .mu. m, and will be reduced down to 0.15 .mu. m by adding TTL alignment.

  • PDF

Optimization of Memristor Devices for Reservoir Computing (축적 컴퓨팅을 위한 멤리스터 소자의 최적화)

  • Kyeongwoo Park;HyeonJin Sim;HoBin Oh;Jonghwan Lee
    • Journal of the Semiconductor & Display Technology
    • /
    • v.23 no.1
    • /
    • pp.1-6
    • /
    • 2024
  • Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.

  • PDF

Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, Target Path Planning and Deep Reinforcement Learning (RGB-D 환경인식 시각 지능, 목표 사물 경로 탐색 및 심층 강화학습에 기반한 사람형 로봇손의 목표 사물 파지)

  • Ryu, Ga Hyeon;Oh, Ji-Heon;Jeong, Jin Gyun;Jung, Hwanseok;Lee, Jin Hyuk;Lopez, Patricio Rivera;Kim, Tae-Seong
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.11 no.9
    • /
    • pp.363-370
    • /
    • 2022
  • Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.

Performance Improvement of the SVM by Improving Accuracy of Estimating Vanishing Points (소실점 추정 정확도 개선을 통한 SVM 성능 향상)

  • Ahn, Sang-Geun;Seo, Tae-Kyu;Jeon, Gwang-Gil;Cho, Joong-Hwee
    • IEMEK Journal of Embedded Systems and Applications
    • /
    • v.11 no.6
    • /
    • pp.361-367
    • /
    • 2016
  • In this paper, we propose an improved single view metrology (SVM) algorithm to accurately measure the height of objects. In order to accurately measure the size of objects, vanishing points have to be correctly estimated. There are two methods to estimate vanishing points. First, the user has to choose some horizontal and vertical lines in real world. Then, the user finds the cross points of the lines. Second, the user can obtain the vanishing points by using software algorithm such as [6-9]. In the former method, the user has to choose the lines manually to obtain accurate vanishing points. On the other hand, the latter method uses software algorithm to automatically obtain vanishing points. In this paper, we apply image resizing and edge sharpening as a pre-processing to the algorithm in order to improve performance. The estimated vanishing points algorithm create four vanishing point candidates: two points are horizontal candidates and the other two points are vertical candidates. However, a common image has two horizontal vanishing points and one vertical vanishing point. Thus, we eliminate a vertical vanishing point candidate by analyzing the histogram of angle distribution of vanishing point candidates. Experimental results show that the proposed algorithm outperforms conventional methods, [6] and [7]. In addition, the algorithm obtains similar performance with manual method with less than 5% of the measurement error.

Active Object Tracking based on stepwise application of Region and Color Information (지역정보와 색 정보의 단계적 적용에 의한 능동 객체 추적)

  • Jeong, Joon-Yong;Lee, Kyu-Won
    • The KIPS Transactions:PartB
    • /
    • v.19B no.2
    • /
    • pp.107-112
    • /
    • 2012
  • An active object tracking algorithm using Pan and Tilt camera based in the stepwise application of region and color information from realtime image sequences is proposed. To reduce environment noises in input sequences, Gaussian filtering is performed first. An image is divided into background and objects by using the adaptive Gaussian mixture model. Once the target object is detected, an initial search window close to an object region is set up and color information is extracted from the region. We track moving objects in realtime by using the CAMShift algorithm which enables to trace objects in active camera with the color information. The proper tracking is accomplished by controlling the amount of pan and tilt to be placed the center position of object into the middle of field of view. The experimental results show that the proposed method is more effective than the hand-operated window method.