• Journal of Digital Convergence
• /
• v.10 no.6
• /
• pp.297-302
• /
• 2012

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• The KIPS Transactions:PartB
• /
• v.11B no.2
• /
• pp.177-186
• /
• 2004

It is a weak point of the motion estimation technique for video compression that the predicted video encoding algorithm requires higher-order computational complexity. To reduce the computational complexity of encoding algorithms, researchers introduced techniques such as 3D-WT that don't require motion prediction. One of the weakest points of previous 3D-WT studies is that they require too much memory for encoding and too long delay for decoding. In this paper, we propose a technique called `FS (Fast playable and Scalable) 3D-WT' This technique uses a modified Haar wavelet transform algorithm and employs improved encoding algorithm for lower memory and shorter delay requirement. We have executed some tests to compare performance of FS 3D-WT and 3D-V. FS 3D-WT has exhibited the same high compression rate and the same short processing delay as 3D-V has.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.3
• /
• pp.30-39
• /
• 2012

In this paper, a safe driving assistance system is proposed by detecting the status of driver's doze based on face and eye detection. By the level of the fatigue, safe driving system alarms or set the seatbelt on vibration. To reduce the effect of backward light and too strong solar light which cause a decrease of face and eye detection rate and false fatigue detection, post processing techniques like image equalization are used. Haar transform and PCA are used for face detection. By using the statistic of the face and eye structural ratio of normal Koreans, we can reduce the eye candidate area in the face, which results in reduction of the computational load. We also propose a new eye status detection algorithm based on Hough transform and eye width-height ratio, which are used to detect eye's blinking status which decides doze level by measuring the blinking period. The system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. In this paper, four algorithms are implemented and proposed algorithm is made based on the probability model and we achieves 84.88% of correct detection rate through indoor and in-car environment experiments. And also we achieves 69.81% of detection rate which is better result than that of other algorithms using IR camera.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.2
• /
• pp.94-101
• /
• 2012

This paper proposes an improved image steganalysis scheme to raise the detection rate of stego images out of cover images. To improve the detection rate of stego image in the steganalysis, tiny variation caused by data hiding should be amplified. For this, we extract feature vectors of cover image and stego image by two steps. First, we separate image into upper 4 bit subimage and lower 4 bit subimage. As a result, stego noise is expanded more than two times. We decompose separated subimages into twelve subbands by applying 3-level Haar wavelet transform and calculate co-occurrence probabilities of two different subbands in the same scale. Since co-occurrence probability of the two wavelet subbands is affected by data hiding, it can be used as a feature to differentiate cover images and stego images. The extracted feature vectors are used as the input to the multilayer perceptron(MLP) classifier to distinguish between cover and stego images. We test the performance of the proposed scheme over various embedding rates by the LSB, S-tool, COX's SS, and F5 embedding method. The proposed scheme outperforms the previous schemes in detection rate to existence of hidden message as well as exactness of discrimination.

• The KIPS Transactions:PartA
• /
• v.13A no.7 s.104
• /
• pp.639-646
• /
• 2006

Data compression technologies enable us to store and transfer large amount of data efficiently, and become more and more important due to increasing data size and the network traffic. Moreover, as a result of the increase of computing power, volumetric data produced from various applied science and engineering fields has been getting much larger. In this Paper, we present a volume compression scheme which exploits Daubeches wavelet transform. The proposed scheme basically supports lossy compression for 3D volume data, and provides unit-wise random accessibility. Since our scheme shows far lower error rates than the previous compression methods based on Haar filter, it could be used well for interactive visualization applications as well as large volume data compression requiring image fidelity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.705-708
• /
• 2018

Among biometrics commercialized for security, iris recognition technology has the most excellent security for the probability of the match between individuals is the lowest. Current commercialized iris recognition technology has excellent recognition ability, but this technology has a fatal drawback. Without the user's active cooperation, it cannot recognize the iris correctly. To make up for this weakness, recent trend of iris recognition development mounts a non-volunteering, unconstrained method. According to this information, the objective of this research is developing a module that can identify people iris from a video acquired by high performance infrared camera in a range of 3m and in a involuntary way. For this, we import images from the video and find people's face and eye positions from the images using Haar classifier trained through Cascade training method. finally, we crop the iris by Hough circle transform and compare it with data from the database to identify people.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.6
• /
• pp.491-496
• /
• 2015

Cloud computing has emerged with promise to decrease the cost of server additional cost and expanding the data storage and ease for computer resource sharing and apply the new technologies. However, Cloud computing also raises many new security concerns due to the new structure of the cloud service models. Therefore, the secure user authentication is required when the user is using cloud computing. This paper, we propose the enhanced AdaBoost algorithm for access cloud security zone. The AdaBoost algorithm despite the disadvantage of not detect a face inclined at least 20, is widely used because of speed and responsibility. In the experimental results confirm that a face inclined at least 20 degrees tilted face was recognized. Using the FEI Face Database that can be used in research to obtain a result of 98% success rate of the algorithm perform. The 2% failed rate is due to eye detection error which is the people wearing glasses in the picture.

• Korean Journal of Soil Science and Fertilizer
• /
• v.43 no.6
• /
• pp.968-974
• /
• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.