• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.483-492
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• 2013

SURF is an algorithm which extracts feature points and generates their descriptors from input images, and it is being used for many applications such as object recognition, tracking, and constructing panorama pictures. Although SURF is known to be robust to changes of scale, rotation, and view points, it is hard to implement it in real time due to its complex and repetitive computations. Using 3.3 GHz Pentium, in our experiment, it takes 240ms to extract feature points and create descriptors in a VGA image containing about 1,000 feature points, which means that software implementation cannot meet the real time requirement, especially in embedded systems. In this paper, we present a hardware architecture that can compute the SURF algorithm very fast while consuming minimum hardware resources. Two key concepts of our architecture are parallelism (for repetitive computations) and efficient line memory usage (obtained by analyzing memory access patterns). As a result of FPGA synthesis using Xilinx Virtex5LX330, it occupies 101,348 LUTs and 1,367 KB on-chip memory, giving performance of 30 frames per second at 100 MHz clock.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.47-56
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• 2005

With the development of wearable(ubiquitous) computers, those traditional interfaces between human and computers gradually become uncomfortable to use, which directly leads to a requirement for new one. In this paper, we study on a new interface in which computers try to recognize the gesture of human through a digital camera. Because the method of recognizing hand gesture through camera is affected by the surrounding environment such as lighting and so on, the detector should be a little sensitive. Recently, Viola's detector shows a favorable result in face detection. where Adaboost learning algorithm is used with the Haar features from the integral image. We apply this method to hand area detection and carry out comparative experiments with the classic method using skin color. Experimental results show Viola's detector is more robust than the detection method using skin color in the environment that degradation may occur by surroundings like effect of lighting.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.311-318
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• 2013

For face detection techniques, the correctness of detection decreases with different lightings and backgrounds so such requires new methods and techniques. This study has aimed to obtain data for reasoning human emotional information by analyzing the components of the eyes and mouth that are critical in expressing emotions. To do this, existing problems in detecting face are addressed and a detection method that has a high detection rate and fast processing speed good at detecting environmental elements is proposed. This method must detect a specific part (eyes and a mouth) by using Haar-like Feature technique with the application of an integral image. After which, binaries detect elements based on color information, dividing the face zone and skin zone. To generate correct shape, the shape of detected elements is generated by using a bezier curve-a curve generation algorithm. To evaluate the performance of the proposed method, an experiment was conducted by using data in the Face Recognition Homepage. The result showed that Haar-like technique and bezier curve method were able to detect face elements more elaborately.

• The Korean Journal of Nuclear Medicine
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• v.35 no.6
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• pp.378-388
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• 2001

Purpose: In this study, we developed a new method for the determination of renal depth with anterior and posterior renal scintigrams in a dual-head gamma camera, considering the attenuation factor $e^{-{\mu}x}$ of the conjugate-view method. Material and Method: We developed abdomen and kidney phantoms to perform experiments using Technetium-99m dimercaptosuccinic acid ($^{99m}Tc$-DMSA). The phantom images were obtained by dual-head gamma camera equipped with low-energy, high-resolution, parallel-hole collimators (ICONf, Siemens). The equation was derived from the linear integration of omission ${\gamma}$-ray considering attenuation from the posterior abdomen to the anterior abdomen phantom surface. The program for measurement was developed by Microsoft Visual C++ 6.0. Results : Renal depths of the phantoms were derived from the derived equations and compared with the exact geometrical values. Differences between the measured and the calculated values were the range of 0.1 to 0.7 cm ($0.029{\pm}0.15cm,\;mean{\pm}S.D.$). Conclusion: The present study showed that the use of the derived equations for renal depth measurements, combined with quantitative planar imaging using dual-head gamma camera, could provide more accurate results for individual variation than the conventional method.