• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1016-1022
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• 2014

Kinect is a motion sensing camera released by Microsoft in November 2010 for the Xbox360 that is used to produce depth and color images. Because Kinect uses an infrared pattern, it generates holes and noises around an object's boundaries in the obtained images. The flickering phenomenon and unmatched edges also occur. In this paper, we propose a real time virtual-view video synthesis algorithm which results in a high quality virtual view by solving these problems stated above. The experimental results show that the proposed algorithm performs much better than the conventional algorithms.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.773-775
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• 2003

From the view point of data fusion, we integrate LIDAR data and digital aerial images to perform 3D building modeling in this study. The proposed scheme comprises two major parts: (1) building block extraction and (2) building model reconstruction. In the first step, height differences are analyzed to detect the above ground areas. Color analysis is then performed for the exclusion of tree areas. Potential building blocks are selected first followed by the refinement of building areas. In the second step, through edge detection and extracting the height information from LIDAR data, accurate 3D edges in object space is calculated. The accurate 3D edges are combined with the already developed SMS method for building modeling. LIDAR data acquired by Leica ALS 40 in Hsin-Chu Science-based Industrial Park of north Taiwan will be used in the test.

• Journal of Animal Environmental Science
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• v.5 no.3
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• pp.139-148
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• 1999

This study was conducted to develop an image processing system which can classify the pig's thermoregulatory behavior under the different environmental conditions. The 4 pigs of 25kg were housed in the environmentally controlled chamber(1.4m$\times$2.2m floor space). Postural behavior of the pigs was captured with an CCD color camera. The raw behavioral images were processed by thresholoding, reduction, separation of slightly contacted pigs, labeling, noise removal, computation of number of labels, and classification of the pig's behavior. The correct classification rate of the image processing system was 97.8%(88 out of 90 testing images). The results of this study showed that the image processing system could be used for a behavior-based automatic environmental controller.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1386-1394
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• 2016

Pyramid fusion usually adjusts the Laplacian weights of pixels of the input images by evaluating predefined criteria. This has advantages that it can selectively express intense color and enhance the contrast when applied to HDR exposure fusion. But it may cause noise because the weights are determined by pixel importance without considering the interdependent pixel relationship that constitutes a scene. This paper proposes a fusion method using simple weight criteria generated from the gray pixel values, which is expected to preserve the interdependent relationship and improve execution speed. In order to evaluate the performance of the proposed method we examine a homogeneity measure, H and compare the execution time for both methods. The proposed method is found to be more advantageous with respect to homogeneity and execution speed.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.2-55
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region efface candidate. The feature information in the region of face candidate is used to detect a face region. In the recognition step, as a tested, the 360 images of 30 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression, Input variables of the neural networks are the feature information that comes from the eigenface spaces. The simulation results of 30 persons show that the proposed method yields high recognition rates.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.648-656
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• 2021

Color digital images are used in many multimedia applications and in many vital applications. Some of these applications require excellent protection for these images because they are confidential or may contain confidential data. In this paper, a new method of data cryptography is introduced, tested, and implemented. It will be shown how this method will increase the security level and the throughput of the data cryptography process. The proposed method will use a secret image_key to generate necessary private keys for each byte of the data block. The proposed method will be compared with other standard methods of data cryptography to show how it will meet the requirements of excellent cryptography, by achieving the objectives: Confidentiality, Integrity, Non-repudiation, and Authentication.

• Journal of IKEEE
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• v.22 no.4
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• pp.1188-1194
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• 2018

This paper proposes an image enhancement-based license plate detection algorithm to improve the overall performance of system. Non-uniform illumination conditions have huge impact on overall plate detection system accuracy. In this paper, we propose an algorithm for color image enhancement-based license plate detection for improving accuracy of images degraded by excessively strong and low sunlight. Firstly, the image is enhanced by Multi-Scale Retinex algorithm. Secondly, a plate detection method is employed to take advantage of geometric properties of connected components, which can significantly reduce the undesired plate regions. Finally, intersection over union method is applied for detecting the accurate location of number plate. Experimental results show that the proposed method significantly improves the accuracy of plate detection system.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1138-1156
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• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.156-165
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• 2021

In this paper, we propose an histogram weighted centroid based Siamese region proposal network for object tracking. The original Siamese region proposal network uses two identical artificial neural networks which take two different images as the inputs and decide whether the same object exist in both input images based on a similarity measure. However, as the Siamese network is pre-trained offline, it experiences many difficulties in the adaptation to various online environments. Therefore, in this paper we propose to incorporate the histogram weighted centroid feature into the Siamese network method to enhance the accuracy of the object tracking. The proposed method uses both the histogram information and the weighted centroid location of the top 10 color regions to decide which of the proposed region should become the next predicted object region.