• 전기전자학회논문지
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• 제26권2호
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• pp.226-231
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• 2022

정보 엔트로피의 개념은 다양한 분야에서 폭넓게 응용되고 있다. 최근 영상처리 분야에서도 정보 엔트로피 개념을 응용한 기술들이 많이 개발되고 있다. 현대 산업에서 컴퓨터 비전 기술들의 중요성과 수요가 증가함에 따라, 영상처리 기술들이 현대 산업에 효율적으로 적용되기 위해서는 실시간 처리가 가능해야 한다. 영상의 엔트로피 값을 추출하는 것은 소프트웨어로는 계산량이 복잡해 실시간 처리가 어려우며 실시간 처리가 가능한 영상 엔트로피 필터의 하드웨어 구조는 제안된 적이 없다. 본 논문에서는 barrel shifter를 사용하여 실시간 처리가 가능한 히스토그램 기반 엔트로피 필터의 하드웨어 구조를 제안한다. 제안한 하드웨어는 Verilog HDL을 이용하여 설계하였고, Xilinx사의 xczu7ev-2ffvc1156을 Target device로 설정하여 FPGA 구현하였다. Xilinx Vivado 프로그램을 이용한 논리합성 결과 4K UHD의 고해상도 환경에서 최대 동작 주파수 750.751MHz를 가지며, 1초에 30장 이상의 영상을 처리하며 실시간 처리 기준을 만족함을 보인다.

• 융합신호처리학회논문지
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• 제9권2호
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• pp.121-128
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• 2008

정확한 음성 구간 검출은 음성 인식 및 음성 코딩 그리고 음성 통신 시스템 등과 같은 음성 어플리케이션의 성능에 큰 영향을 미친다. 본 논문에서는 실제 운전하고 있는 상태에서 다양한 차량 노이즈 환경의 음성 구간 검출 방법을 제안한다. 기존의 음성 구간 검출은 시간 에너지, 주파수 에너지, 영 교차율, spectral entropy 등 다양한 방법을 사용하였으며 잡음 환경에서 급격하게 성능이 저하되는 단점이 있었다. 본 논문에서는 기존의 spectral entropy를 기반으로 하여 MFB(Mel-frequency Filter Banks) spectral entropy, 기울기 FFT(Fast Fourier Transform) spectral entropy, 기울기 MFB spectral entropy를 이용한 음성 구간 검출 방법을 제안한다. MFB는 멜 스케일과 FFT를 곱한 것으로 멜 스케일은 인간이 소리를 인지할 때 주파수에 대해 비선형적인 스케일이며 음성의 특징을 잘 반영한다. 제안한 MFB spectral entropy 방법은 다양한 차량 잡음 환경에서 음성 및 비음성 분별 능력을 향상시킬 수 있으며 실험 결과 93.21%의 음성 구간 검출율을 나타내었다. 이는 기존의 spectral entropy 방법과 비교할 때 MFB를 이용한 음성 구간 검출 방법이 3.2%의 검출율이 향상되었다.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1008-1014
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• 2020

Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

• ETRI Journal
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• 제37권5호
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

• Journal of Advanced Marine Engineering and Technology
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• 제32권5호
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• pp.768-774
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• 2008

In many areas of speech signal processing such as automatic speech recognition and packet based voice communication technique, VAD (voice activity detection) plays an important role in the performance of the overall system. In this paper, we present a new feature parameter for VAD which is the product of energy of the signal and the difference of two types of entropies. For this end, we first define a Mel filter-bank based entropy and calculate its difference from the conventional entropy in frequency domain. The difference is then multiplied by the spectral energy of the signal to yield the final feature parameter which we call PEED (product of energy and entropy difference). Through experiments. we could verify that the proposed VAD parameter is more efficient than the conventional spectral entropy based parameter in various SNRs and noisy environments.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1998년도 하계 학술대회 논문집
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• pp.270-275
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• 1998

It was assumed that the voice of cattle is generated with the virtual white noise through the digital filter called the linear prediction filter, and filter parameters (prediction coefficients) were estimated by the maximum entropy method (MEM) , using the sound signal of the animal . The feature planes were defined by the pairs of two parameters selected appropriately from these parameters. The cattle voices were divided into three levels, that is the high, medium and low levels according to their total power equivalent to the variances of the sound signal . It was found that the straight lines could be used for recognizing tow cow and one calf for high level voices. For high and medium level voices, however, it was difficult or impossible to recognize individual cattle on the parameters planes.

• 정보처리학회논문지B
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• 제15B권1호
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• pp.61-68
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• 2008

본 논문에서는 스팸메일에 나타나는 스팸성 자질과 URL 자질의 공동 학습을 이용한 최대엔트로피모델 기반 스팸 필터 시스템을 제안한다. 스팸성 자질은 스패머들이 스팸메일에 인위적으로 넣는 강조 패턴이나 필터 시스템을 통과하기 위해 비정상적으로 변형시킨 단어들을 말한다. 스팸성 자질 외에 반복적으로 나타나는 URL과 비정상적인 URL도 자질로 사용하였다. 메일에 나타난 정상적인 URL과 필터 시스템을 피하기 위해 변형된 비정상적인 URL들이 스팸 메일을 걸러내는데 도움을 줄 수 있기 때문이다. 또한 스팸성 자질과 URL자질을 이용한 공동 학습을 하였다. 공동 학습은 학습 과정에서 두 자질을 독립적으로 이용한 비지도 학습 방법으로 정답을 모르는 문서를 이용할 수 있다는 장점을 갖는다. 실험을 통해 스팸성 자질과 URL을 이용함으로써 스팸 필터 시스템의 성능을 향상시킬 수 있음을 확인하였으며 두 자질 집합을 이용한 공동 학습이 필요한 학습 문서의 수를 감소시키면서, 정확도는 일괄 학습 정확도에 근접한다는 것을 확인하였다.

• Journal of Information Processing Systems
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• 제12권4호
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• pp.631-643
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• 2016

Due to the block-based discrete cosine transform (BDCT), JPEG compressed images usually exhibit blocking artifacts. When the bit rates are very low, blocking artifacts will seriously affect the image's visual quality. A bilateral filter has the features for edge-preserving when it smooths images, so we propose an adaptive-weighted bilateral filter based on the features. In this paper, an image-deblocking scheme using this kind of adaptive-weighted bilateral filter is proposed to remove and reduce blocking artifacts. Two parameters of the proposed adaptive-weighted bilateral filter are adaptive-weighted so that it can avoid over-blurring unsmooth regions while eliminating blocking artifacts in smooth regions. This is achieved in two aspects: by using local entropy to control the level of filtering of each single pixel point within the image, and by using an improved blind image quality assessment (BIQA) to control the strength of filtering different images whose blocking artifacts are different. It is proved by our experimental results that our proposed image-deblocking scheme provides good performance on eliminating blocking artifacts and can avoid the over-blurring of unsmooth regions.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1998년도 Proceedings of International Workshop on Advanced Image Technology
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• pp.189-194
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• 1998

We introduce a new zerotree scheme that effectively exploits the inter-scale self-similarities found in the octave decomposition by a wavelet transform. A zerotree is useful to efficiently code wavelet coefficients and its efficiency was proved by Shapiro's EZW. In the coding scheme, wavelet coefficients are symbolized and entropy-coded for more compression. The entropy per symbol is determined from the produced symbols and the final coded size is calculated by multiplying the entropy and the total number of symbols. In this paper, were analyze produced symbols from the EZW and discuss the entropy per symbol. Since the entropy depends on the produced symbols, we modify the procedure of symbolic streaming out for the purpose. First, we extend the relation between a parent and children used in the EZW to raise a probability that a significant parent has significant children. The proposed relation is flexibly extended according to the fact that a significant coefficient is highly addressed to have significant coefficients in its neighborhood. The extension way is reasonable because an image is decomposed by convolutions with a wavelet filter and thus neighboring coefficients are not independent with each other.