Accelerating Viola-Jones Face Detection to FPGA-Level Using GPUs

Hefenbrock, Daniel; Oberg, Jason; Thanh, Nhat Tan Nguyen; Kastner, Ryan; Baden, Scott B.

doi:10.1109/fccm.2010.12

Cited by 83 publications

(50 citation statements)

References 6 publications

(19 reference statements)

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“…Sonraki çalışmalar, grafik işleme birimi (GPU) ile merkezî işlem biriminin (CPU) birlikte kullanılması sayesinde Viola-Jones algoritmasının hızının artabileceğini göstermiştir. [38][39][40]. Gerçekleştirilen çalışmada Nvidia GeForce GTX 860M grafik kartına sahip bir bilgisayar kullanılmıştır.…”

Section: Yüz Tespiti (Face Detection)unclassified

Bilgisayar Kullanıcılarına Yönelik Duygusal İfade Tespiti

Ayvaz¹,

Gürüler²

2017

Bilişim Teknolojileri Dergisi

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Özet-Bilgisayar kullanımının yaygınlaştığı günümüzde, insan-bilgisayar etkileşimi ile ilgili yenilikçi çalışmalar hız kazanmıştır. Bu yeniliklerden bir tanesi de bilgisayar kullanıcısı bireylerin duygusal durumlarının, makine ile öğrenilmesidir. Ofis ortamlarında bilgisayarla çalışan bireylerin duygu durumlarının tespit edilebilmesi, özellikle bu kişilerin moral durumu ile iş performansı ilişkisi hakkında anlamlı bilgiler sunabilir. Bu fikirden hareketle, bilgisayar kullanıcısının yüz ifadelerine dayalı anlık duygu tespiti gerçekleştiren prototip bir sistem geliştirilmiştir. Geliştirilen bu sistem sırasıyla; yüz tespiti, yüz işaretçilerinin tespiti, yüz işaretçilerine dayalı özniteliklerden oluşan eğitim veri setinin oluşturulması ve kural-tabanlı sınıflandırıcı ile anlık duygusal durum tespitini gerçekleştirmektedir. Çalışmanın özgünlüğünü ifade eden özniteliklerin ayırt edici karakteristiğini anlamak amacıyla mevcut eğitim veri seti destek vektör makineleri ile durağan bir şekilde sınıflandırılmıştır. Sonuç olarak, sistemin başarımı 10-katlı çapraz doğrulama ile %96,1 olarak tespit edilmiştir. Anahtar Kelimeler-İnsan-bilgisayar etkileşimi, duygusal ifade tespiti, veri madenciliği The Detection of Emotional Expression towards Computer UsersAbstract-In these days, with the widespread use of computers, innovative studies in human-computer interaction have accelerated. One of these innovations is to learn one"s emotional states with the help of a machine. Ascertaining the emotional states of a person working in the office environment can present meaningful information about the correlation between his/her morale and work performance. Based on this idea, an emotion detection system has been developed as a prototype. This system initially performs face and facial landmark detections. Training data set is prepared using these landmarks and then rule-based classifier detects instant emotional states. The training data set has been classified statically with support vector machines to understand the distinctive characteristics of the attributes that express the originality of this study. As a result, the success of the system was detected 96.1% with 10-fold crossvalidation.

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Section: Yüz Tespiti (Face Detection)unclassified

Bilgisayar Kullanıcılarına Yönelik Duygusal İfade Tespiti

Ayvaz¹,

Gürüler²

2017

Bilişim Teknolojileri Dergisi

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“…As a concrete example, an FPGA implementation of the Viola-Jones object detection algorithm is about four times faster and uses an order of magnitude less power than the same algorithm running on a GPU. 25 The high degree of customization afforded by FPGAs is both a blessing and a curse. It provides great flexibility in terms of the design of an application, yet it substantially increases the programming complexity.…”

Section: Hardware-assisted Image Analysis Acceleration Platformsmentioning

confidence: 99%

Strategies for Implementing Hardware-Assisted High-Throughput Cellular Image Analysis

Tse

Meng

Gossett

et al. 2011

JALA: Journal of the Association for Laboratory Automation

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R ecent advances in imaging technology for biomedicine, including high-speed microscopy, automated microscopy, and imaging flow cytometry are poised to have a large impact on clinical diagnostics, drug discovery, and biological research. Enhanced acquisition speed, resolution, and automation of sample handling are enabling researchers to probe biological phenomena at an increasing rate and achieve intuitive image-based results. However, the rich image sets produced by these tools are massive, possessing potentially millions of frames with tremendous depth and complexity. As a result, the tools introduce immense computational requirements, and, more importantly, the fact that image analysis operates at a much lower speed than image acquisition limits its ability to play a role in critical tasks in biomedicine such as real-time decision making. In this work, we present our strategy for high-throughput image analysis on a graphical processing unit platform. We scrutinized our original algorithm for detecting, tracking, and analyzing cell morphology in high-speed images and identified inefficiencies in image filtering and potential shortcut routines in the morphological analysis stage. Using our ''grid method'' for image enhancements resulted in an 8.54Â reduction in total run time, whereas origin centering allowed using a look up table for coordinate transformation, which reduced the total run time by 55.64Â. Optimization of parallelization and implementation of specialized image processing hardware will ultimately enable real-time analysis of high-throughput image streams and bring wider adoption of assays based on new imaging technologies. ( JALA 2011;16:422-30) INTRODUCTIONImaging is ubiquitous in industrial processing, medicine, environmental science, and cell biology. Given the diverse modes of imaging that exist, an image can contain a wealth of information about an object. Process quality control in semiconductor manufacturing and particle synthesis uses a number of spatial metrics from images from scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and optical microscopy. 1,2 Imaging tools including positron emission tomography, X-ray, magnetic resonance imaging, and computed tomography are widely used in medicine for diagnostic and prognostic purposes. Ocean and waterway monitoring, a critical charge of environmental science, can be performed with high-speed camera-coupled flow cytometry whereby the diversity and density of microscopic organisms, key indicators of ecosystem health, can be identified. 3,4 In cell biology, for example, cell size, morphology, and location can be extracted from brightfield or phase-contrast images. And, the presence or location of biomolecules within cells can be obtained from fluorescence images of chemically labeled cells, which has recently been implemented with automated fluid handling and imagers for high-content analysis.5 As technology improves, imaging rates and resolution increase and the cost of acquiring image sets decreases ...

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“…In the work of D. Hefenbrock [5] a GPU approach is proposed, which is programmed using CUDA [12] and tested on NVIDIAs Tesla processors. The final GPU implementation reached 15.2 FPS and is running on a desktop server containing 4 Tesla GPUs.…”

Section: Related Workmentioning

confidence: 99%