Wyniki wyszukiwania - BazTech

1

Robotic Mobile Holder (For CAR Dashboards)

Madhunala Srilatha, Kanneti Bhavya, Anathula Priya

International Journal of Electronics and Telecommunications

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2023

|

Vol. 69, No. 1

89--98

EN

In the current smart tech world, there is an immense need of automating tasks and processes to avoid human intervention, save time and energy. Nowadays, mobile phones have become one of the essential things for human beings either to call someone, connect to the internet, while driving people need mobile phones to receive or make a call, use google maps to know the routes and many more. Normally in cars, mobile holders are placed on the dashboard to hold the mobile and the orientation of the phone needs to be changed according to the driver's convenience manually, but the driver may distract from driving while trying to access mobile phone which may lead to accidents. To solve this problem, an auto adjustable mobile holder is designed in such a way that it rotates according to the movement of the driver and also it can even alert the driver when he feels drowsiness. Image Processing is used to detect the movement of the driver which is then processed using LabVIEW software and NI myRIO hardware. NI Vision development module is used to perform face recognition and servo motors are used to rotate the holder in the required position. Simulation results show that the proposed system has achieved maximum accuracy in detecting faces, drowsiness and finding the position coordinates.

2

Efficient face detection based crowd density estimation using convolutional neural networks and an improved sliding window strategy

Kian Ara Rouhollah, Matiolanski Andrzej, Grega Michał, Dziech Andrzej, Baran Remigiusz

International Journal of Applied Mathematics and Computer Science

|

2023

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Vol. 33, no. 1

7--20

EN

Counting and detecting occluded faces in a crowd is a challenging task in computer vision. In this paper, we propose a new approach to face detection-based crowd estimation under significant occlusion and head posture variations. Most state-of-the-art face detectors cannot detect excessively occluded faces. To address the problem, an improved approach to training various detectors is described. To obtain a reasonable evaluation of our solution, we trained and tested the model on our substantially occluded data set. The dataset contains images with up to 90 degrees out-of-plane rotation and faces with 25%, 50%, and 75% occlusion levels. In this study, we trained the proposed model on 48,000 images obtained from our dataset consisting of 19 crowd scenes. To evaluate the model, we used 109 images with face counts ranging from 21 to 905 and with an average of 145 individuals per image. Detecting faces in crowded scenes with the underlying challenges cannot be addressed using a single face detection method. Therefore, a robust method for counting visible faces in a crowd is proposed by combining different traditional machine learning and convolutional neural network algorithms. Utilizing a network based on the VGGNet architecture, the proposed algorithm outperforms various state-of-the-art algorithms in detecting faces ‘in-the-wild’. In addition, the performance of the proposed approach is evaluated on publicly available datasets containing in-plane/out-of-plane rotation images as well as images with various lighting changes. The proposed approach achieved similar or higher accuracy.

3

The vehicle driver safety prediction system

Haller Piotr, Wróbel Radosław, Sierzputowski Gustaw, Dimitrov Radostin, Mihaylow Veselin

Combustion Engines

|

2022

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R. 61, nr 3

11--17

EN

The article presents analysis of road crash accidents. It presents the evolution of safety systems, starting from a description of the currently used vehicle-based systems, with particular emphasis on the prediction of the driver falling asleep. The article also proposes a proprietary system of sleep prediction based on the face detection of drivers. The detection of facial landmarks is presented as a two-step process: an algorithm finds faces in general, and then needs to localize key facial structures within the face region of interest.

4

A Flexible Approach for Automatic Door Lock Using Face Recognition

Vu Hong-Son, Ly Van-Dat

Annals of Computer Science and Information Systems

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2022

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Vol. 33

157--163

EN

The model of smart door lock using face recognition based on hardware is the Jetson TX2 embedded computer proposed in this paper. In order to recognize the faces, face detection is a very important step. This paper studies and evaluates two methods of face detection, namely Histograms of Oriented Gradients (HOG) method which represents the approach using facial features and Multi-task Cascaded Convolutional Neural Networks method (MTCNN) represents using of deep learning and neural networks. To evaluate these two methods, the experimental model is used to verify the hardware platform, which is the Jetson TX2 embedded computer. The face angle parameter is used to rate the detection level and accuracy for each method. In addition, the experimental model also evaluates the speed of face detection from the camera of these methods. Experimental results show that the average time for face detection by HOG and MTCNN method are respectively 0.16s and 0.58s. For face-to-face frames, both methods detect very well with an accuracy rate of 100\%. However, with various face angles of 30o, 60o, 90o, the MTCNN method gives more accurate results, which is also consistent with published studies. The smart door lock model uses the MTCNN face detection method combined with the Facenet algorithm along with a data set of 200 images for 1 face with accuracy of 99\%.

5

Detection of human faces in thermal infrared images

Kowalski Marcin Ł., Grudzień Artur, Ciurapiński Wiesław

Metrology and Measurement Systems

|

2021

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Vol. 28, nr 2

307--321

EN

The presented study concerns development of a facial detection algorithm operating robustly in the thermal infrared spectrum. The paper presents a brief review of existing face detection algorithms, describes the experiment methodology and selected algorithms. For the comparative study of facial detection three methods presenting three different approaches were chosen, namely the Viola-Jones, YOLOv2 and Faster-RCNN. All these algorithms were investigated along with various configurations and parameters and evaluated using three publicly available thermal face datasets. The comparison of the original results of various experiments for the selected algorithms is presented.

6

REGA: Real-Time Emotion, Gender, Age Detection Using CNN - A Review

Sharma Dibya Jyoti, Dutta Sachin, Bora Dibya Jyoti

Annals of Computer Science and Information Systems

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2020

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Vol. 24

115--118

EN

In this paper we describe a methodology and an algorithm to estimate the real-time age, gender, and emotion of a human by analyzing of face images on a webcam. Here we discuss the CNN based architecture to design a real-time model. Emotion, gender and age detection of facial images in webcam play an important role in many applications like forensics, security control, data analysis,video observation and human-computer interaction. In this paper we present some method \& techniques such as PCA,LBP, SVM, VIOLA-JONES, HOG which will directly or indirectly used to recognize human emotion, gender and age detection in various conditions.

7

Detecting gaze direction using robot-mounted and mobile-device cameras

Jarosz Mateusz, Nawrocki Piotr, Placzkiewicz Leszek, Sniezynski Bartłomiej, Zielinski Marcin, Indurkhya Bipin

Computer Science

|

2019

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Vol. 20 (4)

453--474

EN

Two common channels through which humans communicate are speech and gaze. Eye gaze is an important mode of communication: it allows people tobetter understand each others’ intentions, desires, interests, and so on. The goal of this research is to develop a framework for gaze triggered events that can be executed on a robot and mobile devices and allows to perform experiments. We experimentally evaluate the framework and techniques for extracting gaze direction based on a robot-mounted camera or a mobile-device camera that are implemented in the framework. We investigate the impact of light on the accuracy of gaze estimation, and also how the overall accuracy depends on user eye and head movements. Our research shows that light intensity is important, and the placement of a light source is crucial. All the robot-mounted gaze detection modules we tested were found to be similar with regard to their accuracy. The framework we developed was tested in a human-robot interaction experiment involving a job-interview scenario. The flexible structure of this scenario allowed us to test different components of the framework in varied real-world scenarios, which was very useful for progressing towards our long-term research goal of designing intuitive gaze-based interfaces for human robot communication.

8

Evaluation of face detection algorithms for the bank client identity verification

Szczodrak M., Czyżewski A.

Foundations of Computing and Decision Sciences

|

2017

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Vol. 42, No. 2

137--148

EN

Results of investigation of face detection algorithms efficiency in the banking client visual verification system are presented. The video recordings were made in real conditions met in three bank operating outlets employing a miniature industrial USB camera. The aim of the experiments was to check the practical usability of the face detection method in the biometric bank client verification system. The main assumption was to provide a simplified as much as possible user interaction with the application. Applied algorithms for face detection are described and achieved results of face detection in the real bank environment conditions are presented. Practical limitations of the application based on encountered problems are discussed.

9

Face recognition using the Haar classifier cascade and face detection based on detection of skin color areas

Kątek G., Holik A., Zabłocki T., Dobrzyńska P.

Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy

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2016

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z. 19

29--36

EN

The article presents two methods of face detection. The first of these is a method Haar classifier cascade. The second is a face detection method based on detection of skin color areas. They propose a face detection algorithm based on skin color. The main emphasis lies on the effectiveness of the algorithm in order to properly recognize a human face. The results allowed to evaluate the effectiveness of the proposed method.

PL

W artykule przedstawiono dwie metody detekcji twarzy. Pierwsza z nich to metoda kaskady klasyfikatorów Haara. W metodzie tej ważne jest położenie twarzy w stosunku do kąta obrócenia zdjęcia. Rozpoznawane są tylko „pionowe” twarze. Drugą stanowi metoda detekcji twarzy w oparciu o wykrywanie obszarów o kolorze skóry. Zaproponowano algorytm detekcji twarzy w oparciu o kolor skóry. Główny nacisk położono na skuteczność algorytmu w celu poprawnego rozpoznania ludzkiej twarzy. Otrzymane wyniki pozwoliły ocenić skuteczność zaproponowanej metody.

10

Metoda automatycznej identyfikacji koloru sukien

Grega M.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2016

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nr 6

332--335, CD

PL

Algorytmy służące automatycznej generacji treści multimedialnych zyskują na znaczeniu i popularności, jednakże wymagają dobrze ustrukturyzowanych źródeł danych, szczególnie w scenariuszach generowania treści na żądanie. W tym artykule zaproponowano metodę automatycznej identyfikacji koloru sukien. Prezentowane rozwiązanie używa cech Haara do detekcji twarzy i oczu a następnie korzysta z cech proporcji ludzkiego ciała by wyznaczyć obszar próbkowania koloru. Algorytm przetestowano na bazie składającej się z 50 obrazów testowych. Osiągnięto 79% skuteczność rozpoznawania koloru. W pozostałych 21% przypadków przyczynami niepowodzenia były problemy z prawidłową detekcją twarzy, lokalizacją twarzy i prawidłową identyfikacją koloru.

EN

Recently a new trend in media can be observed – automated generation of content. Algorithms used for this process utilize commonly accessible and well structured data sources in order to produce on-demand content for the user. Good description and structure is required also for multimedia content. In this paper we propose a method for automated identification of celebrity dress colour. Our solution is based on Haar features for eye and face detection and smart selection of sampling region based on the proportions of the human body. We have tested the method on a database of 50 images and have obtained 79% accuracy in colour detection. In the 21% cases the problems encountered were with face detection, proper face location and proper colour identification.

11

FPGA Implementation of Multi-scale Face Detection Using HOG Features and SVM Classifier

Drożdż M., Kryjak T.

Image Processing & Communications

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2016

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Vol. 21, no. 3

27--44

EN

In this paper an FPGA based embedded vision system for face detection is presented. The sliding detection window, HOG+SVM algorithm and multi-scale image processing were used and extensively described. The applied computation parallelizations allowed to obtain real-time processing of a 1280 × 720 @ 50Hz video stream. The presented module has been verified on the Zybo development board with Zynq SoC device from Xilinx. It can be used in a vast number of vision systems, including diver fatigue monitoring.

12

Silhouette Identification for Apparelled Bodies

Tao C., Zhou J., Yin M.

Fibres & Textiles in Eastern Europe

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2016

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Vol. 24, no. 5 (119)

119--124

EN

This paper presents an approach to identify apparel silhouettes. A feature region of the human face was first proposed for conducting face detection in fashion pictures with the AdaBoost method, and the head was then located with its positional relation to the facial feature region. The linear relationship between the ratio of the body height to head length and the length of the lower body was ensured by restricting the RBH to a specific range. Under this condition, the apparelled body was divided into several parts, and the boundary of apparel on the lower body was determined considering the influence of the hemline. Based on the widths of the body parts and the apparel on the lower body, shape factors were established to express the extent to which the apparel silhouette approached a certain shape. A computer program was developed for implementation and demonstrated high accuracy in silhouette identification of an appareled body.

PL

Przedstawiono próbę identyfikacji sylwetki ubranego modelu. Opracowano system właściwości charakteryzujących twarz człowieka dla możliwości dalszego wyodrębnienia twarzy ze zdjęć modeli. Zastosowano metodę AdaBoost. Umożliwiło to usytuowanie głowy w stosunku do innych elementów ubioru. Zidentyfikowano liniowe zależności pomiędzy wysokościami całości sylwetki, jej dolnej części, i głowy. Na tej podstawie ubrana sylwetka człowieka była dzielona na szereg części i określano granice ubioru w stosunku do dolnej części sylwetki, uwzględniając wpływ dolnej krawędzi ubioru. Opierając się na szerokościach poszczególnych części ciała człowieka i ubioru dolnej części sylwetki, ustalono czynniki kształtu dla możliwości kwalifikacji sylwetki do odpowiedniego typu. Opracowano program komputerowy umożliwiający dużą dokładność identyfikacji sylwetki ubranego modelu.

13

Methods of face localization in thermograms

Marzec M., Koprowski R., Wróbel Z.

Biocybernetics and Biomedical Engineering

|

2015

|

Vol. 35, no. 2

138--146

EN

This paper presents an algorithm for determination of the head centre in thermograms. The paper includes a comparison of the method developed by the authors with the known methods presented in the literature for locating the head in thermal images. The proposed method enables automatic localization of the head centre, which is essential for practical applications when there is a need to locate the head in an image. Application areas may include the process of face recognition in biometrics, recognition of emotions, the creation of a human–computer interface. The presented method is reproducible and enables to obtain correct results in cases of large interindividual variability of the test subjects.

14

Przetwarzanie wstępne i analiza obrazu na użytek lokalizacji twarzy

Majkowski A., Kołodziej M., Rak R. J., Nasternak M

Pomiary Automatyka Kontrola

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2014

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R. 60, nr 3

132--135

PL

W artykule zaprezentowany jest algorytm automatycznej detekcji twarzy w obrazie statycznym. Detektor ma osiągać najwyższą skuteczność przy znajdowaniu twarzy możliwie niepochylonych i patrzących na wprost kamery. Wielkość wykrywanych twarzy musi być (z pewnymi odchyleniami) zgodna z rozmiarem twarzy zawartych na obrazach zastosowanych do uczenia klasyfikatora. Obrazy wejściowe mogą być kolorowe lub czarno-białe. Nie ma limitu co do liczby twarzy znajdujących się na obrazie.

EN

The aim of this work is to design and implement a face detection algorithm in static images. The detector have to achieve the best results in finding possible not inclined faces of people looking directly at the camera. The authors have proposed an algorithm which operation is based on the appearance (features) of the face. Block diagram of the proposed face detector is given in Fig. 1. In the first stage, the image containing the face is subjected to preprocessing in which normalization is the most important. Normalization aims to unify a variety of analyzed images. We have used here a conversion of colors to gray levels and stretching and equalization of image histogram. Thus prepared image is processed by the appropriate face detection algorithm, which consists of pre-selection and classification. In order to train the classifier the authors created a database of images consisting of two major categories: containing faces and do not contain faces. As a collection of images that include faces there have been used Olivetti DB ORL database [1]. Final processing step is to get rid of the multiple detection of the same faces. As a result of the algorithm we obtain the location of all faces in the input image (Fig. 4). The size of detected faces should be (with some variations) in accordance with the size of images used to train the classifier. Input images can be color or black and white. There is no limit to the number of faces in an image.

15

Face detection in color images using skin segmentation

Hajiarbabi M., Agah A.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2014

|

Vol. 8, No. 3

41--51

EN

Face detection which is a challenging problem in computer vision, can be used as a major step in face recognition. The challenges of face detection in color images include illumination differences, various cameras characteristics, different ethnicities, and other distinctions. In order to detect faces in color images, skin detection can be applied to the image. Numerous methods have been utilized for human skin color detection, including Gaussian model, rule-based methods, and artificial neural networks. In this paper, we present a novel neural network-based technique for skin detection, introducing a skin segmentation process for finding the faces in color images.

16

Implementacja algorytmu detekcji twarzy w obrazach cyfrowych z układem SoC Zynq

Wujek P., Pełka R.

Pomiary Automatyka Kontrola

|

2013

|

R. 59, nr 8

809--811

PL

W artykule przedstawiono koncepcję i projekt mikrosystemu do detekcji twarzy w obrazach cyfrowych z użyciem układu programowalnego SoC z rodziny Zynq firmy Xilinx [1]. Algorytm detekcji twarzy polega na wyodrębnieniu podstawowych cech twarzy i określeniu ich położenia w obrazie. Przedstawiono wyniki implementacji programowej w środowisku MATLAB/PC oraz implementacji sprzętowej. Obie implementacje przebadano pod względem złożoności oraz szybkości działania. W realizacji sprzętowej uzyskano porównywalną szybkość detekcji/lokalizacji twarzy i ponad 10-krotnie krótszy czas wyodrębniania cech twarzy.

EN

In this paper there is presented the design of an integrated microsystem for face detection in digital images, based on a new SoC Zynq from Xilinx [1]. Zynq is a new class of SoCs which combines an industry-standard ARM dual-core Cortex-A9 processing system with 28 nm programmable logic. This processor-centric architecture delivers a comprehensive platform that offers ASIC levels of performance and power consumption, the ease of programmability and the flexibility of a FPGA. The proposed algorithm for face detection operates on images having the resolution of 640x480 pixels and 24-bit color coding. It uses three-stage processing: normalization, face detection/location [2] and feature extraction. We implemented the algorithm in a twofold way: (1) using MATLAB/PC, and (2) hardware platform based on ZedBoard from Avnet [3] with Zynq XC7Z020 SoC. Both implementations were examined in terms of complexity and speed. The hardware implementation achieved a comparable speed of face detection/location but was over 10-times faster while extracting the features of faces in digital images. A significant speedup of feature extraction results from the parallelized architecture of a hardware accelerator for calculation of mouth and eyes locations. The proposed microsystem may be used in low-cost, mobile applications for detection of human faces in digital images. Since the system is equipped with the Linux kernel, it can be easily integrated with other mobile applications, including www services running on handheld terminals with the Android operating system.

17

Analiza efektywności sprzętowych implementacji algorytmów detekcji twarzy w obrazach cyfrowych

Wujek P., Pełka R.

Pomiary Automatyka Kontrola

|

2012

|

R. 58, nr 7

581-583

PL

W artykule przedstawiono i porównano wyniki implementacji przykładowego algorytmu detekcji twarzy w obrazach cyfrowych na trzech platformach sprzętowych: z użyciem CPU (Matlab), w strukturze programowalnej FPGA z procesorem sprzętowym PowerPC [1], oraz z wykorzystaniem CPU z akceleracją GPU. Powyższe implementacje przebadano eksperymentalnie pod względem złożoności implementacji i szybkości działania poszczególnych fragmentów algorytmu. Porównano je ze sobą oraz przedstawiono najlepsze obszary zastosowań poszczególnych z nich.

EN

This paper describes comparison of hardware implementations of a face detection algorithm using three different platforms: (1) classic CPU implementation (Matlab), (2) implementation with use of programmable logic - FPGA with hardware processor PowerPC [1], and (3) CPU based version with GPU acceleration. These tree versions have been experimentally tested and compared in terms of the required hardware resources and operating speed, which is of great importance in most practical applications. We also discuss advantages and drawbacks of these three approaches to hardware implementation of face detection algorithms. In particular, we formulate some important conditions that the analyzed image must meet to obtain the optimum effectiveness of the face detection algorithm implemented on each platform. Finally, we show that use of GPU acceleration can take advantage of the classic CPU and parallel computing accessible to FPGA. The proposed solution of skin color detection time for the CPU with GPU acceleration is over 100 times shorter than that for the solution with the classical CPU. As a programmable device we have used FPGA Virtex-4 chip from Xilinx, and as a GPU accelerator we have utilized graphic card nVidia GeForce 8600 GT.

18

Acces control system using face image

Bobulski J.

Control and Cybernetics

|

2012

|

Vol. 41, no. 3

691-703

EN

Ensuring safety requires the use of access control systems. Traditional systems typically use proximity cards. Modern systems use biometrics to identify the user. Using biological characteristics for identification ensures a high degree of safety. In addition, biological characteristics cannot be neither lost nor stolen. This paper presents proposals for the access control system Rusing face image. The system operates in real time using camera image.

19

Wybrane zagadnienia pomiarów termowizyjnych

Wyżgolik R., Budzan S.

Pomiary Automatyka Kontrola

|

2011

|

R. 57, nr 10

1256-1259

PL

W artykule przedstawiono wybrane parametry metrologiczne kamery termowizyjnej ThermoPro TP8, wykorzystywanej w badaniach własnych w Instytucie Automatyki Politechniki Śląskiej. Parametry te wyznaczone zostały z wykorzystaniem technicznych ciał czarnych oraz wzorcowego termometru rezystancyjnego. Ograniczono się do wąskiego zakresu temperatur, w granicach temperatury otoczenia i temperatury ludzkiego ciała. Omówiono system pomiarowy pozwalający na przeprowadzenie badań uszkodzeń podpowierzchniowych metodą aktywnej termografii impulsowej. Badania przeprowadzono w zakresie wyboru optymalnego źródła fali cieplnej, czasu nagrzewania płyt testowych, jak również opracowania algorytmu, który pozwolił przeprowadzić eksperyment oraz dokonać detekcji. Ponadto omówiono algorytm do detekcji twarzy oraz oczu. Badanie przeprowadza się w celu dokładnego określenia położenia oraz rozmiarów twarzy oraz oczu, w których następnie ustala się statystykę temperatury (temp. minimalna, maksymalna, średnia).

EN

In the paper selected metrological parameters of a ThermoPro TP8 infrared camera are presented. The camera is used in research in the Institute of Automatic Control of the Silesian University of Technology. The metrological parameters were investigated based on the reference black bodies and a reference platinum resistance thermometer for precise temperature measurements of the black bodies. The temperature range was limited to the ambient and human body temperature. The ThermoPro TP8 infrared camera is a typical industrial - inspection camera, and requires the validation for biomedical applications. The stability, temperature error and "object in scene" tests were performed. The results show that the stability after 75 minutes was within ±0,1°C (a very good result), but the temperature error in the temperature range of interest was significant. In addition, two applications of infrared cameras are presented. The first is the active thermography for non destructive testing, mainly the pulsed ther-mography. A simple measuring stand for investigations of the defects in plexi (PMMA) tiles with the reference holes (±0,05 mm accuracy in diameter and depth) was constructed. The research conducted was aimed at selection of the optimal source of heat waves, the warm-up time for test plates, as well as developing the algorithm for detection of defects in the plexi test plates, with simultaneous determination of the defect position and diameter. The third application of infrared cameras for which the research was carried out is the human face detection system. A number of algorithms was tested. One of them is presented - the algorithm which uses patterns to detect the face and eyes. The experiment was performed to determine the precise location and size of the face and eyes, and then the determined temperature statistics (the minimum, maximum and average temperature).

20

Układ SoC - FPGA do detekcji twarzy w obrazach cyfrowych

Wujek P., Pełka R.

Pomiary Automatyka Kontrola

|

2011

|

R. 57, nr 8

889-891

PL

W artykule przedstawiono wyniki badań dotyczących sprzętowej implementacji algorytmu detekcji twarzy w obrazach cyfrowych z wykorzystaniem układów programowalnych FPGA (Xilinx). Przeprowadzono symulację algorytmu w środowisku PC - Matlab. Przebadany wstępnie algorytm zaimplementowano w układzie FPGA Virtex-4. Wykonano badania eksperymentalne, w których porównano szybkość działania algorytmu w wersji programowej i sprzętowej oraz określono zajętość zasobów układu FPGA.

EN

In this paper there are presented recent results of the authors' work on implementation of face detection algorithms in digital images based on FPGA technology from Xilinx. There was considered a number of existing face detection methods, described in papers [1-3] to find out which one is the best for implementation in a single FPGA device. Then the authors proposed a modified algorithm for face detection that was tested using PC - MATLAB environment. The results of software simulations were used for appropriate adjusting of some essential parameters, according to the requirements of FPGA implementation (the basic limitation is a total number of FPGA resources). The main results of simulations are shown in Tab. 1. The final version of the algorithm was im-plemented in a Virtex-4 FPGA device and tested using a set of example digital images. An important advantage of the proposed SoC for face detection is its speed (2-4 times higher than that for software implementation, as it is shown in Tab. 2). Furthermore, this speed does not depend on the window size used in image analysis. There was also reported the final utilization of FPGA resources (Tab. 3). The experimental results obtained from laboratory tests of the proposed face detection algorithm implemented in a single FPGA device show that the hardware approach to face detection problem has important advantages: high speed, flexibility and relatively low requirements on the total number of FPGA resources.