• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.1-8
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• 2021

The performance of hidden camera cameras is improving day by day due to miniaturization and advanced technology integration according to the speed of technological development of smartphones. As this external networking computing environment is advanced and diversified, exposure to hidden cameras in addition to general safety cameras is also increasing. On the other hand, the technology for detecting and preventing hidden cameras is not keeping up with the development and speed of these hidden cameras. Therefore, in this study, the heat of the hidden camera was detected using infrared thermal detection technology based on general image and thermal image synthesis technology, and the reflectance of each wavelength according to the difference in ambient temperature was analyzed to reduce the false positive rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.135-140
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• 2012

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.168-173
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• 2011

In this paper, an optical image hiding technique using real-valued decoding key is proposed. In the embedding process, a each zero-padded original image placed in a quadrants on an input plane is multiplied by a statistically independent random phase pattern and is Fourier transformed. An encoded image is obtained by taking the real-valued data from the Fourier transformed image. And then a phase-encoded pattern, used as a hidden image and a decoding key, is generated by the use of multiple phase wrapping from the encoded images. A transmitted image is made from the linear superposition of the weighted hidden images and a cover image. In reconstruction process, the mirror reconstructed images can be obtained at two quadrants by the inverse-Fourier transform of the product of the transmitted image and the decoding key. Computer simulation and optical experiment are demonstrated in order to confirm the proposed technique.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.17-24
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• 2024

In this paper, we propose a highly secure technique to hide confidential data in image pixels using a quadruple encryption techniques. In the proposed technique, the boundary surface where the image outline exists and the flat surface with little change in pixel values are investigated. At the boundary of the image, in order to preserve the characteristics of the boundary, one bit of confidential data that has been multiply encrypted is spatially encrypted again in the LSB of the pixel located at the boundary to hide the confidential data. At the boundary of an image, in order to preserve the characteristics of the boundary, one bit of confidential data that is multiplely encrypted is hidden in the LSB of the pixel located at the boundary by spatially encrypting it. In pixels that are not on the border of the image but on a flat surface with little change in pixel value, 2-bit confidential data that is multiply encrypted is hidden in the lower 2 bits of the pixel using location-based encryption and spatial encryption techniques. When applying the proposed technique to hide confidential data, the image quality of the stego-image is up to 49.64dB, and the amount of confidential data hidden increases by up to 92.2% compared to the existing LSB method. Without an encryption key, the encrypted confidential data hidden in the stego-image cannot be extracted, and even if extracted, it cannot be decrypted, so the security of the confidential data hidden in the stego-image is maintained very strongly. The proposed technique can be effectively used to hide copyright information in general commercial images such as webtoons that do not require the use of reversible data hiding techniques.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.571-582
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• 2018

Recently, development of deep learning has shown better image classification result than human. According to recent research, a hidden layer of deep learning is deeper, and a preservation of extracted features shows good results. However, in the case of general images, the extracted features are clear and easy to sort. This study aims to classify plant leaf images. This plant leaf image has high similarity in each image. Since plant leaf images have high similarity not only between images of different species but also within the same species, classification accuracy is not increased by simply extending the hidden layer or connecting the layers. Therefore, in this paper, we tried to improve the hidden layer of the algorithm called DenseNet which shows the recent excellent classification results, and compare the results of several different modified layers. The proposed method makes it possible to classify plant leaf images collected in a natural environment more easily and accurately than conventional methods. This results in good classification of plant leaf image data including unnecessary noise obtained in a natural environment.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2264-2275
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• 1994

In this paper, image compression based on neural networks is presented with block classification and coding. Multilayer neural networks with error back-propagation learning algorithm are used to transform the normalized image date into the compressed hidden values by reducing spatial redundancies. Image compression can basically be achieved with smaller number of hidden neurons than the numbers of input and output neurons. Additionally, the image blocks can be grouped for adaptive compression rates depending on the characteristics of the complexity of the blocks in accordance with the sensitivity of the human visual system(HVS). The quantized output of the hidden neuron can also be entropy coded for an efficient transmission. In computer simulation, this approach lie in the good performances even with images outside the training set and about 25:1 compression rate was achieved using the entropy coding without much degradation of the reconstructed images.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.145-151
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• 2014

This paper proposes an image haze removal algorithm for a single image. The conventional Dark Channel Prior(DCP) algorithm estimates a transmission map using the dark information in an image, and the haze regions are then detected using a matting algorithm. However, since the DCP algorithm uses block-based processing, block artifacts are invariably formed in the transmission map. To solve this problem, the proposed algorithm generates a modified transmission map using a Hidden Markov Random Field(HMRF) and Expectation-Maximization(EM) algorithm. Experimental results confirm that the proposed algorithm is superior to conventional algorithms in image haze removal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.24-31
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• 2003

In this paper, a new opto-digital multiple information hiding and real-time extracting system is implemented. That is, multiple information is hidden in a cover image by using the stego keys which are generated by combined use of random sequence(RS) and Hadamard matrix(HM) and these hidden information is extracted in real-time by using a new optical correlator-based extraction system. In the experiment, 3 kinds of information, English alphabet of "N", "R", "L" having 512$\times$512 pixels, are formulated 8$\times$8 blocks and each of these information is multiplied with the corresponding stego keys having 64$\times$64 pixels one by one. And then, by adding these modulated data to a cover image of "Lena"having 512$\times$512 pixels, a stego image is finally generated. In this paper, as an extraction system, a new optical nonlinear joint transform correlator(NJTC) is introduced to extract the hidden data from a stego image in real-time, in which optical correlation between the stego image and each of the stego keys is performed and from these correlation outputs the hidden data can be asily exacted in real-time. Especially, it is found that the SNRs of the correlation outputs in the proposed optical NJTC-based extraction system has been improved to 7㏈ on average by comparison with those of the conventional JTC system under the condition of having a nonlinear parameter less than k=0.4. This good experimental results might suggest a possibility of implementation of an opto-digital multiple information hiding and real-time extracting system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.266-270
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• 2006

In this paper, we propose the human identification system based on Hidden Markov model using gait. Since each gait cycle consists of a set of continuous motion states and transition across states has probabilistic dependences, individual gait can be modeled using Hidden Markov model. We assume that individual gait consists of N discrete transitions and we propose gait feature representation, Modified Motion Silhouette Image (MMSI) to represent and recognize individual gait. MMSI is defined as a gray-level image and it provides not only spatial information but also temporal information. The experimental results show gait recognition performance of proposed system.