• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제4권4호
• /
• pp.655-670
• /
• 2010

To be reversible as a data hiding method, the original content and hidden message should be completely recovered. One important objective of this approach is to achieve high embedding capacity and low distortion. Using predicted errors is very effective for increasing the embedding capacity. Sorting the predicted errors has a good influence on decreasing distortion. In this paper, we present an improved reversible data hiding scheme using upgraded histogram shifting based on sorting the predicted errors. This new scheme is characterized by the algorithm which is able to find the optimum threshold values and manage the location map effectively. Experimental results compared with other methods are presented to demonstrate the superiority of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
• /
• 한국정보처리학회 2017년도 춘계학술발표대회
• /
• pp.861-864
• /
• 2017

본 논문은 Exponential histogram을 사용하여 데이터스트림에서 개념 변화를 검출 하는 기법을 제안한다. 스트림 데이터와 같이 빠르게 증가하는 데이터에 대한 개념 변화를 찾는 것은 중요 문제이다. 기존에 사용하던 슬라이딩 윈도우 기반의 방법들은 과거의 데이터를 버렸지만, 제안하는 방법은 과거의 데이터를 효율적으로 저장하며, 윈도우의 크기를 변경 할 수 있는 방법을 제안한다. 실험을 통해 제안하는 방법에 대한 효율성과 정확성을 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 한국해양정보통신학회 2006년도 춘계종합학술대회
• /
• pp.197-200
• /
• 2006

A scene changing detection using histogram and header information of H.264 video stream is presented in this paper. The method using histogram is normal to be detect the changing scene. But this technique results in a lot of processing time because video data is compressed and decompressed to video stream and calculated the difference of histogram between scenes on the each frame. The method using H.264 header information is available to detect the scene change at real time without the process of calculation. Histogram and header information is more rapid for scene change detection with being the same performance in precision and recall.

• Journal of IKEEE
• /
• 제5권2호
• /
• pp.190-200
• /
• 2001

This paper is addressed to welding defect feature vector selection and implementation method of welding defect classifier using fuzzy techniques. We compare IAV, zero-crossing number as time domain analysis, power spectrum coefficient as frequency domain, histogram as both domain for welding defect feature selection. We choose histogram as feature vector by graph analysis and find out that maximum frequent occurrence number and section of corresponding signal scale in relative histogram show obvious difference between normal welding and voiding with penetration depth defect. We implement a fuzzy welding defect classifier using these feature vector, test it to verify its effectiveness for 695 welding data frame which consist of 4000 sampled data. As result of test, correct classification rate is 92.96%. Lab experimental results show a effectiveness of fuzzy welding defect classifier using relative histogram for practical Laser welding monitoring system in industry.

• Journal of KIISE:Databases
• /
• 제33권2호
• /
• pp.214-223
• /
• 2006

Recently there is a need to store and process enormous spatial data in spatio-temporal databases. For effective query processing in spatio-temporal databases, selectivity estimation in query optimization techniques, which approximate query results when the precise answer is not necessary or early feedback is helpful, has been studied. There have been selectivity estimation techniques such as sampling-based techniques, histogram-based techniques, and wavelet-based techniques. However, existing techniques in spatio-temporal databases focused on selectivity estimation for future extent of moving objects. In this paper, we construct a new histogram, named T-Minskew, for query optimization of past spatio-temporal data. We also propose an effective selectivity estimation method using T-Minskew histogram and effective histogram maintenance technique to prevent frequent histogram reconstruction using threshold.

• Proceedings of the IEEK Conference
• /
• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
• /
• pp.211-214
• /
• 2002

Automatic video partitioning is the first step for content-based indexing and retrieval of video data. In this paper, an efficient algorithm for scene change detection is proposed, where cumulative histogram and edge information are utilized. Experimental results have shown the effectiveness of the proposed algorithm.

• Proceedings of the Korea Multimedia Society Conference
• /
• 한국멀티미디어학회 2000년도 추계학술발표논문집
• /
• pp.159-162
• /
• 2000

현재 대부분의 영상 압축 기법들은 영상의 특징에 따라 최적화된 전용 압축기법을 사용한다. 본 논문에서는 입력영상에 대한 특징 정보를 사전에 가지고 있지 않더라도 입력되는 영상의 Histogram을 자동 인식하고, 추출된 Histogram 특성에 따라 각 영상의 특징에 맞는 적응적 압축기법을 적용할 수 있도록 히스토그램특성 분석기준을 제안하였으며, 이를 구현하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• 제43권4호
• /
• pp.27-36
• /
• 2006

Lossless data embedding is a method to insert information into a host image that guarantees complete restoration when the extraction has been done. In this paper, we propose a noble reversible data embedding algorithm for images in wavelet domain. The proposed embedding technique, which modifies histogram of wavelet coefficient, is composed of two inserting steps. Data is embedded to wavelet coefficient using modification of histogram in first embedding process. Second embedding step compensates the distortion caused by the first embedding process as well as hides more information. Hence we achieve higher inserting capacity. In view of the relationship between the embedding capacity and the PSNR value, our proposed method shows considerably higher performance than the current reversible data embedding methods.

• Journal of Intelligence and Information Systems
• /
• 제10권1호
• /
• pp.1-11
• /
• 2004

It is very important part of pre-processing for get better results by image processing that get emphasized image by processing of source image. Emphasized image is not only good looking image but clear and sharp image. Emphasized images are used very useful data at contour extraction and image recognition in image processing. It have different image recognition by how much represent a origin scene in row quality image. Present algorithms that get emphasized premier image do not get clear picture of degree that want in various kind of images and there is shortcoming that need much process times being proportional size of picture quality or accumulation degree of histogram. In this paper, we propose method to change distribution chart that pixels occupy in histogram as subsequentness in reflex of various kinds as well as that picture quality reflex method to emphasize so that is suitable in practical use purpose originally of premier. Proposed algorithm re-allot histogram distribution by reverse tracking histogram. Experimental images are same result and take less processing time than histogram equalization.

• Korean Journal of Remote Sensing
• /
• 제30권4호
• /
• pp.537-546
• /
• 2014

In this paper, we propose a method to detect interesting objects in inaccessible areas using high resolution satellite images. We define the interesting objects as a set of objects which have conceptually similar image patterns, not having exact sizes or shapes. In this paper, we developed a learning and classifier of Support Vector Machine (SVM) that extracts characteristic data for inputted images using Histogram of Gradients (HOG) feature and detects similar objects in other images using the characteristic data. As automatic search of interesting objects in our proposed method, we identify that our method provides reduced time and efforts for manual searching similar objects.