• Korean Journal of Digital Imaging in Medicine
• /
• v.14 no.1
• /
• pp.37-42
• /
• 2012

Pediatric head and neck phantom, using the rate by focusing distance and grid images, Image J using the Quality Assessment and Dose Area Product compared. X-ray laboratory equipment due to the Philips Digital DIAGNOST a 110 cm FFD set and using ACE Non-grid, focusing distance 110 cm (12 : 1), 140 cm (12 : 1), 180 cm (8 : 1) Focused grid, Acryl Phantom (Fluke Model 76-2 Series Phantom) 15.24 cm, by resolution chart image acquisition, image evaluation program (Image J Ver. 1.4.3.67, USA) imaging experiments were analyzed using. Dose Area Product in the Non Grid 0.028 $mGy{\cdot}cm^2$, focusing distance 110 cm (12 : 1), the 0.129 $mGy{\cdot}cm^2$, 140 cm (12 : 1), the 0.135 $mGy{\cdot}cm^2$, 180 cm (8 : 1) was measured with a 0.110 $mGy{\cdot}cm^2$ Non Grid, focusing distance 110 cm (12 : 1), 140 cm (12 : 1), 180 cm (8 : 1) Image obtained when grid using the image J program focusing distance 110 cm with grid based on the measured SNR and PSNR Non Grid if the SNR the 17.307 dB, PSNR of the 20.002 dB, if the SNR 28.755 dB, PSNR was measured by the 31.451 dB. Image J image analysis through the streets, rather than focusing on grid by the rate that could see an increase in dose. Select the grid by a small dose rate reduction is possible.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1984.12a
• /
• pp.78-82
• /
• 1984

The purposed of this paper is to realize two dimensional acoustic image using focusing method. In the experiment, it consists of phase delay part, 14 analog multiplexers, multi-amplifiers, A/D converter, DMA interfacing parts, two linear arrays(resonance frequency 25 KHz, radius 0.75 Cm diac type). Finally in this experimental result, both electrical deflection and dynamic focusing are realized and gets two dimensional acoustic image using focusing method.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.4
• /
• pp.90-96
• /
• 2010

The focusing system is an important factor to determine the imaging quality of a digital imaging system. The focusing system consist of measuring the focusing index with high frequency energy of an image and controlling the movement of the focusing lens based on the computed focusing index. The computation of the focusing index is a key aspect in implementing the focusing system and the noise of the image cause the error in the sharpness evaluation of the image. To reduce this error, the noise under the low illumination condition is considered. A noise insensitive focusing index using adaptive weights is proposed in this paper. This measure determines the sharpness of an image using the spatially adaptive weights based on the local statistics of the image and noise. Experimental results under the condition without and with the noise verify the performance of the proposed method.

• IEIE Transactions on Smart Processing and Computing
• /
• v.1 no.3
• /
• pp.133-142
• /
• 2012

This paper presents a salient region detection algorithm for auto-focusing based on the characteristics of a human's visual attention. To describe the saliency at the local, regional, and global levels, this paper proposes a set of novel features including multi-scale local contrast, variance, center-surround entropy, and closeness to the center. Those features are then prioritized to produce a saliency map. The major advantage of the proposed approach is twofold; i) robustness to changes in focus and ii) low computational complexity. The experimental results showed that the proposed method outperforms the existing low-level feature-based methods in the sense of both robustness and accuracy for auto-focusing.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.3
• /
• pp.74-82
• /
• 2004

This paper proposes a fast zooming and focusing technique for implementing a real-time surveillance camera system which can capture a face image in less than 1 second. It determines the positions of zooming and focusing lenses using two-step algorithm. In the first step, it moves the zooming and focusing lenses simultaneously to the positions calculated using the lens equations for achieving the predetermined magnification. In the second step the focusing lens is adjusted so that it is positioned at the place where the focus measure is the maximum. The camera system implemented for the experiments has shown that the proposed algorithm spends about 0.56 second on average fur obtaining a focused image.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.7-10
• /
• 2003

This paper proposes the multiple objects focusing algorithm. Given multiple objects at different distances from a camera, we assume that one object is well-focused and the others are out-of-focused. The proposed auto-focusing algorithm is summarized as follows: (i) detects edges from an input image, (ⅱ) estimates the radius of PSF (Point Spread Function) across the edge, (ⅲ) gather edge points having same radius of PSF, (ⅳ) segments the image into regions with the same radius of PSF, and (ⅴ) restores the each segmented region using the corresponding PSF.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.2 s.308
• /
• pp.30-39
• /
• 2006

Iris recognition is that identifies a user based on the unique iris texture patterns which has the functionalities of dilating or contracting pupil region. Iris recognition systems extract the iris pattern in iris image captured by iris recognition camera. Therefore performance of iris recognition is affected by the quality of iris image which includes iris pattern. If iris image is blurred, iris pattern is transformed. It causes FRR(False Rejection Error) to be increased. Optical defocusing is the main factor to make blurred iris images. In conventional iris recognition camera, they use two kinds of focusing methods such as lilted and auto-focusing method. In case of fixed focusing method, the users should repeatedly align their eyes in DOF(Depth of Field), while the iris recognition system acquires good focused is image. Therefore it can give much inconvenience to the users. In case of auto-focusing method, the iris recognition camera moves focus lens with auto-focusing algorithm for capturing the best focused image. However, that needs additional H/W equipment such as distance measuring sensor between users and camera lens, and motor to move focus lens. Therefore the size and cost of iris recognition camera are increased and this kind of camera cannot be used for small sized mobile device. To overcome those problems, we propose method to increase DOF by iris image restoration algorithm based on focus value of iris image. When we tested our proposed algorithm with BM-ET100 made by Panasonic, we could increase operation range from 48-53cm to 46-56cm.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.4
• /
• pp.159-165
• /
• 2012

The accurate focusing position should be determined for accurate measurements In VMS. Camera lens focusing is an important problem in computer vision and video measuring systems (VMS) that use CCD cameras and high precision XYZ stages. Camera focusing is a very important step in high precision measurement systems that use computer vision technique. The auto focusing process consists of two steps, the focus value measurement step and the exact focusing position determination step. It is suitable for eliminating high frequency noises with lower processing time and without blurring. An automatic focusing technique is applied to measure a crater with a one-dimensional search algorithm for finding the best focus. Throughout this paper, the suggested algorithm for the Auto focusing was combined with the learning. As a result, it is expected that such a combination would be expanded into the system of recognizing voices in a noisy environment.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.9
• /
• pp.447-453
• /
• 2001

In this paper, a new auto-focusing algorithm for digital cameras is proposed. One of the primary concerns of digital image processing is to increase image quality, and the most important factor for degrading the images is the blurring effect due to inexact focusing. The blurring effect occurs when the focusing lens is located on an unsuitable position. Therefore, focusing on an object should be proceeded before acquiring images. The proposed auto-focusing algorithm is MMDT(min-max difference threshold), and the performance of the proposed algorithm is evaluated by the use of the focus curve. It is shown that the proposed algorithm is superior to other previous auto-focusing algorithms in both the focus shape and computation time aspects. Especially, the improvement of the focus curve shape in both monotonousness and slope indicates that focusing can be done rapidly in comparison with other previous proposed algorithms.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.30 no.6
• /
• pp.569-577
• /
• 2010

Nonplanar surface geometries of components are frequently encountered in real ultrasonic inspection situations. Use of rigid array transducers can lead to beam defocusing and reduction of defect image quality due to the mismatch between the planar array and the changing surface. When a flexible array is used to fit the complex surface profile, the locations of array elements should be known to compute the delay time necessary for adaptive heam focusing. An alternative method is to employ the time reversal focusing technique that does not require a prior knowledge about the properties and structures of the specimen and the transducer. In this paper, a time reversal method is applied to simulate beam focusing of flexible arrays and imaging of point-like defects contained in specimens with nonplanar surface geometry. Quantitative comparisons are made for the performance of a number of array techniques in terms of the ability to focus and image three point-like reflectors positioned at regular intervals. The sinusoidal profile array studied here exhibits almost the same image quality as the flat, reference case.