• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.677-680
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• 2016

In computer vision and image processing, feature detection and description are essential parts of many applications which require a representation for objects of interest. Applications like object recognition or motion tracking will not produce high accuracy results without good features. Due to its importance, research on image feature has attracted a significant attention and several techniques have been introduced. This paper provides a review on well-known image feature detection and description techniques. Moreover, two experiments are conducted for the purpose of evaluating the performance of mentioned techniques.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.149-154
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• 2023

This paper presents a novel approach to multi-focus image fusion using light field cameras. The proposed neural network, LFFCNN (Light Field Focus Convolutional Neural Network), is composed of three main modules: feature extraction, feature fusion, and feature reconstruction. Specifically, the feature extraction module incorporates SPP (Spatial Pyramid Pooling) to effectively handle images of various scales. Experimental results demonstrate that the proposed model not only effectively fuses a single All-in-Focus image from images with multi focus images but also offers more efficient and robust focus fusion compared to existing methods.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1420-1423
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• 2002

The classification and recognition of two-dimensional trademark patterns independently of their position, orientation, size and scale by proposing two feature vectors has been discussed. The paper presents experimentation on two feature vectors showing size- invariance and scale-invariance respectively. Both feature vectors are equally invariant to rotation as well. The feature extraction is based on local as well as global statistics of the image. These feature vectors have appealing mathematical simplicity and are versatile. The results so far have shown the best performance of the developed system based on these unique sets of feature. The goal has been achieved by segmenting the image using connected-component (nearest neighbours) algorithm. Second part of this work considers the possibility of using back propagation neural networks (BPN) for the learning and matching tasks, by simply feeding the feature vectosr. The effectiveness of the proposed feature vectors is tested with various trademarks, not used in learning phase.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.157-166
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• 2001

In this paper we propose a feature vector extraction technique using regression of shape features for the content-based image retrieval system. The proposed technique can reduce the number of dimensions of a feature vector by converting the extracted high-dimensional feature vector into a specific n-dimensional feature vector. This paper shows how to resolve the 'dimensionality curse' problem by reducing the number of dimensions of a feature vector, and shows that the technique is more efficient than the conventional techniques for the practical image retrievals.

• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.545-552
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• 1996

Researches on chromosome are very significant in cytogenetics since a gene of the chromosome controls revelation of the inheritance plasma The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, we propose an algorithm for reconstruction of the chromosDme image to improve the chromosome classification accuracy. Morphological feature parameters are extracted from the reconstructed chromosome images. The reconstruction method from chromosome image is the 32 direction line algorithm. We extract three morphological feature parameters, centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), by preprocessing ten human chromosDme images. The experimental results show that proposed algorithm is better than that of other researchers'comparing by feature parameter errors.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.367-370
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• 2005

This paper proposes a novel method for content-based watermarking based on feature points of an image. At each feature point, watermark is embedded after affine normalization according to the local characteristic scale and orientation. The characteristic scale is the scale at which the normalized scale-space representation of an image attains a maximum value, and the characteristic orientation is the angle of the principal axis of an image. By binding watermarking with the local characteristics of an image, resilience against affine transformations can be obtained. Experimental results show that the proposed method is robust against various image processing steps including affine transformations, cropping, filtering, and JPEG compression.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.99-102
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• 2009

In this paper a hierarchical stereo matching algorithm based on feature extraction is proposed. The boundary (edge) as feature point in an image is first obtained by segmenting an image into red, green, blue and white regions. With the obtained boundary information, disparities are extracted by matching window on the image boundary, and the initial disparity map is generated when assigned the same disparity to neighbor pixels. The final disparity map is created with the initial disparity. The regions with the same initial disparity are classified into the regions with the same color and we search the disparity again in each region with the same color by changing block size and search range. The experiment results are evaluated on the Middlebury data set and it show that the proposed algorithm performed better than a phase based algorithm in the sense that only about 14% of the disparities for the entire image are inaccurate in the final disparity map. Furthermore, it was verified that the boundary of each region with the same disparity was clearly distinguished.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.193-195
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• 2006

As multimedia information increases sharply, In image retrieval field the method that can analyze image data quickly and exactly is required. In the case of image data, because each data includes a lot of informations, between accuracy and speed of retrieval become trade-off. To solve these problem, feature vector extracting process that use Genetic Algorithm for implementing prompt and correct image clustering system in case of retrieval of mass image data is proposed. After extracting color and texture features, the representative feature vector among these features is extracted by using Genetic Algorithm.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1281-1287
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• 2016

This paper presents a method of single image dehazing and feature matching for aerial remote sensing images. In the case of a aerial image, transferring the information of the original image is difficult as the contrast leans by the haze. This also causes that the image contrast decreases. Therefore, a refined transmission map based on a hidden Markov random field. Moreover, the proposed algorithm enhances the accuracy of image matching surface-based features in an aerial remote sensing image. The performance of the proposed algorithm is confirmed using a variety of aerial images captured by a Worldview-2 satellite.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.1-14
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• 2011

While the circulation of images become active, various requirements to manage increasing database are raised. The content-based technology is one of methods to satisfy these requirements. The image is represented by feature vectors extracted by various methods in the content-based technology. The global feature method insures fast matching speed because the feature vector extracted by the global feature method is formed into a standard shape. The global feature extraction methods are classified into two categories, the spatial feature extraction and statistical feature extraction. And each group is divided by what kind of information is used, color feature or gray scale feature. In this paper, we introduce various global feature extraction technologies and compare their performance by accuracy, recall-precision graph, ANMRR, feature vector size and matching time. According to the experiments, the spatial features show good performance in non-geometrical modifications, and the extraction technologies that use color and histogram feature show the best performance.