• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1121-1141
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• 2020

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.945-958
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• 2017

This paper proposes a novel method of reversible data hiding in JPEG images. After analyzing the JPEG features, we provide a new algorithm of selecting appropriate blocks and coefficients to carry secret messages. Instead of embedding data into the histogram of all coefficients, we propose a strategy of ordered embedding to hide data by histogram shifts in several rounds. On the recipient end, secret messages can be exactly extracted, and the original JPEG image can be losslessly recovered. Experimental results show that high embedding rate can be achieved with limited distortions. Most importantly, the marked JPEG file generated by the proposed method requires less storage size than state-of-the-art works.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.207-210
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• 2009

Edge detection is an important process in low level image processing. But many proposed methods for edge detection are not very robust to the image noise and are not flexible for different images. To solve the both problems, an algorithm is proposed which eliminate the noise by mean shift algorithm in advance, and then adaptively determine the double thresholds based on gradient histogram and minimum interclass variance, With this algorithm, it can fade out almost all the sensitive noise and calculate the both thresholds for different images without necessity to setup any parameter artificially, and choose edge pixels by fuzzy algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2733-2737
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• 2014

Motion detection according to the movement and the change area detection method according to the background difference and the motion history image for use in a motion estimation technique using a real-time image, the motion detection method according to the optical flow, the back-projection of the histogram of the object to track for motion tracking At the heart of MeanShift center point of the object and the object to track, while used, the size, and the like due to the motion tracking algorithm CamShift, Kalman filter to track with direction. In this paper, we implemented the motion detection algorithm based on color and shape information of the object and verify.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.967-970
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• 2014

An efficient peak detection algorithm in magnitude spectrum is proposed to distinguish the M-frequency shift keying(FSK) signals from other digitally modulated signal. In addition, recognition of the modulation order estimation of FSK signals is also studied based on the fact that the magnitude spectrum of FSK signals reveals the number of peaks equal to the modulation order. When no a priori information about the signals, we utilize the histogram of the magnitude spectrum to determine the threshold which is important factor in peak detection algorithm. The simulation results show high probability of classification under 500 symbols and signal-to-noise ratio(SNR) higher than 4dB.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.617-625
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• 2017

In this paper, an effective reversible data embedding algorithm was proposed to embed secrete data into image. In the proposed algorithm, prediction image is generated by accurately predicting pixel values using local similarity existing in image, difference sequence is generated using the generated prediction image and original cover image, and then histogram shift technique is applied to create a stego-image with secrete data hidden. Applying the proposed algorithm, secrete data can be extracted from the stego-image and the original cover image can be restored without loss. Experimental results show that it is possible to embed more secrete data into cover image than APD algorithm by applying the proposed algorithm.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1357-1367
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• 2013

Recently, as one of images based methods in facial expression recognition, the research which used ULBP block histogram feature and SVM classifier was performed. Due to the properties of LBP introduced by Ojala, such as highly distinction capability, durability to the illumination changes and simple operation, LBP is widely used in the field of image recognition. In this paper, we combined $LBP_{8,2}$ and $LBP_{8,1}$ to describe micro features in addition to shift, size change in calculating ULBP block histogram. From sub-windows of 660 of $LBP_{8,1}$ and 550 of $LBP_{8,2}$, ULBP histogram feature of 1210 were extracted and weak classifiers of 50 were generated using AdaBoost. By using the combined $LBP_{8,1}$ and $LBP_{8,2}$ hybrid type of ULBP histogram feature and SVM classifier, facial expression recognition rate could be improved and it was confirmed through various experiments. Facial expression recognition rate of 96.3% by hybrid boosted ULBP block histogram showed the superiority of the proposed method.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.371-376
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• 2018

In the system security technology, the information hiding field is developed as technologies for embedding information, which are generally used as contents media. The proposed technique is a technical steganography technique which uses a technique of concealing certain information through physical / statistical change of signal values of contents. Recently, there have been various studies based on histogram shifting in reversible data concealment. In multi - peak histogram shifting, the capacity of data concealment gradually increased by applying multiple peak histogram method. In this paper, we analyze the effect of concealment in terms of adopting the histogram shift method including skipping. In addition, we propose multi - branch data concealment as a general method to improve concealment capacity. The above proposal has proved to be an example using mathematical expressions, and further improvement measures could be derived.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.23-28
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• 2009

Recently, the research which concerns the object class recognition has been done. Although an object tracking based on most of histograms employs a colored model to improve robustness, the system is not reliable enough yet. In this paper, we presents a method to express and track an object by using the histograms which are composed with visual features through succesive frames. The experimental results shows that this method is reliable to track a car within 80m distance from camera.

• The Journal of the Korea Contents Association
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• v.14 no.8
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• pp.30-39
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• 2014

The proposed watermark embedding algorithm uses histogram of difference image between a modified original image and predicted image. To increase the prediction performance of the predicted image, the reference pixels for prediction are adaptively selected and the other pixels are directionally interpolated with the reference pixels. The simulation result shows that the proposed algorithm gives good performances in the embedding capacity and the PSNR values.