• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.83-90
• /
• 2011

In cell biology area, microscopy enables detecting objects inside cells that are stained or fluorescently tagged. It is disadvantageous for observing these objects because of the noisy characteristics of their environmental surrounding. In this paper, a framework is proposed to increase the throughput and reliability for analysis of these images. First, we apply adaptive beamlet transform to extract edges meaningfully followed by orientation, location, and length in different scales. Then, a post-process is implemented to extend and map them onto original image. Our proposed scheme is compared with Canny edge detector and conventional beamlet transform from four evaluation aspects. It produces better results when experiments are conducted on real images. Much better results for observing internal parts make this framework competitive for analysis of cell images.

• Journal of Korea Multimedia Society
• /
• v.8 no.5
• /
• pp.633-640
• /
• 2005

In this paper, we propose an adaptive face region extraction algorithm based on skin color information. It consists oi the extraction of face candidate region and projection step. In the step of face candidate region extraction, we extract the pixels which are regarded as the candidate skin color pixels by using the given range. Then, the ratio between the total pixels and the extracted pixels is calculated. According to the ratio, we adaptively decide the range of the skin color and extract face candidate region. In the projection step, we project the extracted face candidate region into vertical direction to estimate the width of the face. Then the redundant parts are efficiently removed by using the estimated face width. And the extracted face width information is used at the horizontal projection step to extract the height of the face. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• Journal of Korea Society of Industrial Information Systems
• /
• v.8 no.2
• /
• pp.14-20
• /
• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.11
• /
• pp.4573-4584
• /
• 2015

The traditional feature extraction algorithms for recognition of communication signals can hardly realize the balance between computational complexity and signals' interclass gathered degrees. They can hardly achieve high recognition rate at low SNR conditions. To solve this problem, a novel feature extraction algorithm based on Holder coefficient was proposed, which has the advantages of low computational complexity and good interclass gathered degree even at low SNR conditions. In this research, the selection methods of parameters and distribution properties of the extracted features regarding Holder coefficient theory were firstly explored, and then interval gray relation algorithm with improved adaptive weight was adopted to verify the effectiveness of the extracted features. Compared with traditional algorithms, the proposed algorithm can more accurately recognize signals at low SNR conditions. Simulation results show that Holder coefficient based features are stable and have good interclass gathered degree, and interval gray relation classifier with adaptive weight can achieve the recognition rate up to 87% even at the SNR of -5dB.

• Journal of Biomedical Engineering Research
• /
• v.8 no.1
• /
• pp.1-6
• /
• 1987

In this study, we described the result of computer simulation which is real time extraction of FECG and FHR using adaptive digital signal processing. And we designed 2 channel cassette interface circuit to save FECG signal.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.2
• /
• pp.85-90
• /
• 2004

This paper proposes the wavelet image coding using the significant cluster extraction by morphology and the adaptive quantization. In the conventional MRWD method, the additional seed data takes large potion of the total data bits. The proposed method extracts the significant cluster using morphology to improve the coding efficiency. In addition, the adaptive quantization is proposed to reduce the number of redundant comparative operations which are indispensably occurred in the MRWD quantization. The experimental result shows that the proposed algorithm has the improved coding efficiency and computational cost while preserving superior PSNR

• Journal of Industrial Technology
• /
• v.17
• /
• pp.21-29
• /
• 1997

In this paper, an adaptive image binarization scheme is proposed for automated surface strain measurement. At first, we reviewed an image based 3D deformation factor measurement briefly. Then, a new adaptive thresholding method is proposed for the extraction of lattice pattern from a deformed plate image using its local mean and variance. Some experimental results are presented to verify the effectiveness of our approaches.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.9C
• /
• pp.546-554
• /
• 2011

Near targets in the infrared (IR) images have the steady feature for inner region and the transient feature for the boundary region. Based on these features, this paper proposes a new method to extract the fine target shape of near targets in the IR images. First, we detect the boundary region of the candidate targets using the local variance weighted information entropy (WIE) of the original images. And then, a coarse target region can be estimated based on the labeling of the boundary region. For the coarse target region, we use the opening filter with an adaptive structure element to extract the fine target shape. The decision of the adaptive structure element size is optimized for the width information of target boundary by calculating the average WIE in the enlarged windows. The experimental results show that a proposed method has better extraction performance than the previous threshold algorithms.

• Journal of Digital Contents Society
• /
• v.16 no.6
• /
• pp.951-959
• /
• 2015

Scene text extraction is important because it offers some important information on different image based applications pouring in current smart generation. Edge-Enhanced MSER(Maximally Stable Extremal Regions) which enhances the boundaries using the canny operator after extracting the basic MSER shows excellent performance in terms of text extraction. But according to setting the threshold of the canny operator, the result images using Edge-Enhanced MSER are different, so there needs a method figuring out the threshold. In this paper, we propose a AEMSER(Adaptive Edge-enhanced MSER) that applies the method extracting the boundary using the middle value of histogram to Edge-Enhanced MSER to get the canny operator's threshold. The proposed method can acquire better result images than the existing methods because it extracts the area only for the obvious boundaries.

• International Journal of Advanced Culture Technology
• /
• v.4 no.4
• /
• pp.75-80
• /
• 2016

The process of extracting external boundary of an object is a very important process for recognizing an object in the image. The proposed extraction method consists of two processes: External Boundary Extraction and Thinning. In the first step, external boundary extraction process separates the region representing the object in the input image. Then, only the pixels adjacent to the background are selected among the pixels constituting the object to construct an outline of the object. The second step, thinning process, simplifies the outline of an object by eliminating unnecessary pixels by examining positions and interconnection relations between the pixels constituting the outline of the object obtained in the previous extraction process. As a result, the simplified external boundary of object results in a higher recognition rate in the next step, the object recognition process.