• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.286-295
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• 2000

We present a novel statistically adaptive method using the Minimum Description Length(MDL) principle for unsupervised segmentation of magnetic resonance(MR) images. In the method, Markov random filed(MRF) modeling of tissue region accounts for random noise. Intensity measurements on the local region defined by a window are modeled by a finite Gaussian mixture, which accounts for image inhomogeneities. The segmentation algorithm is based on an iterative conditional modes(ICM) algorithm, approximately finds maximum ${\alpha}$ posteriori(MAP) estimation, and estimates model parameters on the local region. The size of the window for parameter estimation and segmentation is estimated from the image using the MDL principle. In the experiments, the technique well reflected image characteristic of the local region and showed better results than conventional methods in segmentation of MR images with inhomogeneities, especially.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.867-873
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• 2018

Recently, interests in the field of image processing have increased with the increased demand for digital imaging devices. However, digital images are damaged by the noise generated by various causes during image processing. Generally in digital imaging devices, noise such as AWGN, etc. is generated and performance and reliability are deteriorated, and various researches are in progress to remove it. Accordingly in the present paper, an algorithm to protect the high frequency components of the images and to efficiently remove AWGN is proposed. The proposed algorithm calculates the final output by adjusting estimate values applied with Gaussian mask and weight values applied according to the local mask distribution. And for performance evaluation of the proposed algorithm, simulations were performed with the existing methods, and the characteristics were compared through PSNR and the processed, enlarged images.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.92-103
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• 1997

This paper proposes a new gray-level image segmentation method using GA(genetic algorithm) and an ACA(adaptive clustering algorithm). The solution in the general GA can be moving because of stochastic reinsertion, and suffer from the premature convergence problem owing to deficiency of individuals before finding the optimal solution. To cope with these problems and to reduce processing time, we propose the new GBR algorithm and the technique that resolves the premature convergence problem. GBR selects the individual in the child pool that has the fitness value superior to that of the individual in the parents pool. We resolvethe premature convergence problem with producing the mutation in the parents population, and propose the new method that removes the small regions in the segmented results. The experimental results show that the proposed segmentation algorithm gives better perfodrmance than the ACA ones in Gaussian noise environments.

• ETRI Journal
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• v.38 no.3
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• pp.560-567
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• 2016

To solve the problem of parameter optimization in image sensor-based visible light positioning systems, theoretical limits for both the location and the azimuth angle of the image sensor receiver (ISR) are calculated. In the case of a typical indoor scenario, maximum likelihood estimations for both the location and the azimuth angle of the ISR are first deduced. The Cramer-Rao Lower Bound (CRLB) is then derived, under the condition that the observation values of the image points are affected by white Gaussian noise. For typical parameters of LEDs and image sensors, simulation results show that accurate estimates for both the location and azimuth angle can be achieved, with positioning errors usually on the order of centimeters and azimuth angle errors being less than $1^{\circ}$. The estimation accuracy depends on the focal length of the lens and on the pixel size and frame rate of the ISR, as well as on the number of transmitters used.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.655-658
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• 2003

멀티미디어 기술과 인터넷의 급속한 발전으로 인해 영상 데이터의 복사가 더욱 쉬워지고 있다. 이러한 작품들을 보호하기 위해 데이터 안에 저작권을 표시할 수 있는 기술들이 필요해지고 지난 몇 년간 데이터에 다른 정보를 삽입할 수 있는 많은 기법들이 제안되어 왔다. 본 논문에서는 RGB 칼라 영상을 인간 시각 특성을 나타내는 I.UV 좌표계로 변화하여 인간 시각에 둔감한 U영역에 키를 이용하여 랜덤하게 워터마크를 삽입하고 추출하는 워터마킹 기법을 제안한다. 또한 웨이브릿(Wavelet) 변환을 사용하였으며 비교적 공격에 강한 고주파수 영역에 삽입하였고 추출시는 삽입한 키 값을 이용하여 워터마크를 추출한다. 제안한 워터마킹 기법은 시각적으로 보이지 않고, 가우시안 노이즈(Gaussian Noise) 및 필터링(filtering)에도 견고함을 보인다.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.399-409
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• 2019

Three-dimensional (3D) human pose reconstruction from single-view image is a difficult and challenging topic. Existing approaches mostly process frame-by-frame independently while inter-frames are highly correlated in a sequence. In contrast, we introduce a novel spatial-temporal 3D human pose reconstruction framework that leverages both intra and inter-frame relationships in consecutive 2D pose sequences. Orthogonal matching pursuit (OMP) algorithm, pre-trained pose-angle limits and temporal models have been implemented. Several quantitative comparisons between our proposed framework and recent works have been studied on CMU motion capture dataset and Vietnamese traditional dance sequences. Our framework outperforms others by 10% lower of Euclidean reconstruction error and more robust against Gaussian noise. Additionally, it is also important to mention that our reconstructed 3D pose sequences are more natural and smoother than others.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.25-33
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• 2023

Noise generated during the acquisition and transmission of CT images acts as a factor that degrades image quality. Therefore, noise removal to solve this problem is an important preprocessing process in image processing. In this paper, we remove noise by using a deformable convolutional autoencoder (DeCAE) model in which deformable convolution operation is applied instead of the existing convolution operation in the convolutional autoencoder (CAE) model of deep learning. Here, the deformable convolution operation can extract features of an image in a more flexible area than the conventional convolution operation. The proposed DeCAE model has the same encoder-decoder structure as the existing CAE model, but the encoder is composed of deformable convolutional layers and the decoder is composed of conventional convolutional layers for efficient noise removal. To evaluate the performance of the DeCAE model proposed in this paper, experiments were conducted on CT images corrupted by various noises, that is, Gaussian noise, impulse noise, and Poisson noise. As a result of the performance experiment, the DeCAE model has more qualitative and quantitative measures than the traditional filters, that is, the Mean filter, Median filter, Bilateral filter and NL-means method, as well as the existing CAE models, that is, MAE (Mean Absolute Error), PSNR (Peak Signal-to-Noise Ratio) and SSIM. (Structural Similarity Index Measure) showed excellent results.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.141-147
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• 2014

The wavelet shrinkage is a technique that reduces the wavelet coefficients to minimize the MSE(Mean Square Error) between the signal and the noisy signal by making use of the threshold determined by the variance of the wavelet coefficients. In this paper, by using the monotonic transform and the power of wavelet subbands, new thresholds applicable to the high and the low frequency wavelet bands are proposed, and the thresholds are applied to the ST(soft-threshold) technique to denoise on image signals with additive Gaussian noise. And the results of PSNRs are compared with the results obtained by the VisuShrink technique and those of [15]. The results shows the validity of this technique.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.97-103
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• 2010

In this paper, we propose a new digital watermarking technique. The main idea of the proposed algorithm relies on the assumption that any real number can be expressed as a summation of the exponential form with base of 2 and if only consider the first few summations some numbers can be expressed in the same form. Therefore, we can be sure that some amount of changes does not affect the first few summations. The algorithm decomposes a host image in wavelet domain and intensity of the significant wavelet coefficient is expressed in exponential form with base of 2. Multiple barcode watermarks are then embedded by modifying the parity of the exponent. The proposed scheme is semi-blind and also offers either objective or subjective deteew su as well. From extracted watermarks, more accurate watermark is obtained by merging technique as a final watermark. As a simulation result, the proposed algorithm could resist most cases of salt and pepper noise, Gaussian noise and JPEG compression.