• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2075-2092
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• 2017

Person re-identification is to match pedestrians observed from non-overlapping camera views. It has important applications in video surveillance such as person retrieval, person tracking, and activity analysis. However, it is a very challenging problem due to illumination, pose and viewpoint variations between non-overlapping camera views. In this work, we propose a viewpoint invariant method for matching pedestrian images using orientation of pedestrian. First, the proposed method divides a pedestrian image into patches and assigns angle to a patch using the orientation of the pedestrian under the assumption that a person body has the cylindrical shape. The difference between angles are then used to compute the similarity between patches. We applied the proposed method to real-time global multi-object tracking across multiple disjoint cameras with non-overlapping field of views. Re-identification algorithm makes global trajectories by connecting local trajectories obtained by different local trackers. The effectiveness of the viewpoint invariant method for person re-identification was validated on the VIPeR dataset. In addition, we demonstrated the effectiveness of the proposed approach for the inter-camera multiple object tracking on the MCT dataset with ground truth data for local tracking.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2112-2122
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• 1997

This paper presents a new video codec which is based on 2-D triangular mesh-based motion compensation and two step grid point motion estimation. With this approach the efficiency of compression and the quality of reconstructed images are improved. The compensation of motion for each triangular patch is performed by image warping using motion vectors at the grid points. The prediction error coding and the rate control meet MPEG-4 VM 3.0 specification. The experimental results show that the codec system proposed is simple in complexity and moreover, the quality of decoded images is improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.283-288
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• 2011

This paper have proposed an object retrieval using the corners area variability based on correlogram. The proposed algorithm is processed as follows. First, the corner points of the object in an image are extracted and then the feature vectors are obtained. It are rearranged according to the number dimension and consist of sequence vectors. And the similarity based on the maximum of sequence vectors is measured. The proposed technique is invariant to the rotation or the transfer of the objects and more efficient in case that the objects present simple structure. In simulation that use Wang's database, the method presents that the recall property is improved by 0.03% and more than the standard corner patch histogram.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.35-39
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• 2006

With a growing concern about the state of infrastructure worldwide, the demand for the development of reliable nondestructive testing techniques (NDT) is ever increasing. Among possible NDT techniques. microwave method is proven to be effective in fast and non-contact inspection of concrete structures and inclusions inside concrete. It is also found that the microwave method has a potential in detecting the delamination between fiber reinforced polymer (FRP) plate and concrete. On the other hand, ultrasonic method can be another way to find the delamination. In this paper, the research work needed for the development of a reliable microwave method and ultrasonic method is studied in actual measurements of concrete specimens reinforced with FRP. Concrete specimens are made with FRP and artificial delamination inside. A microwave measurement system with horn antennas with high center frequency and broad frequency bandwidth are used to image inside concrete specimens for the detection of debonding. between concrete and FRP. Also, the equipment of ultrasonic method which is commercialized are used at the same condition. Both of the results are analyzed in comparison of each other. Microwave and ultrasonic methods have been used for the detection of debonding between concrete and fiber-reinforced plastic (FRP).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5485-5506
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• 2016

In this article, a video based fire detection framework for CCTV surveillancesystems is presented. Two novel features and a novel image type with their corresponding algorithmsareproposed for this purpose. One is for the slow-smoke detection and another one is for fast-smoke/flame detection. The basic idea is slow-smoke has a highly varying chrominance/luminance texture in long periods and fast-smoke/flame has a highly varying texture waiting at the same location for long consecutive periods. Experiments with a large number of smoke/flame and non-smoke/flame video sequences outputs promising results in terms of algorithmic accuracy and speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.53-61
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• 1998

In this paper, we propose an efficient multi-view images coding algorithm to find the optimal depth and texture from the set of multi-view images. The proposed algorithm consists of two consecutive steps, i) the depth estraction step, and ii) the texture extraction step, comparedwith the traditional algorithem which finds the depth and texture concurrently. The X-Y plane of the normalized object space is divided into traingular paatches and the Z value of the node is determined in the first step and then the texture of the each patch is extracted in the second step. In the depth extraction step, the depth of the node is determined by applying the block based disparity compensation method to the windowed area centered at the node. In the second step, the texture of the traingular patches is extracted from the multi-view images by applying the affine transformation based disparity compensation method to the traingular pateches with the depth extracted from the first step. Experimental results show that the SNR(Singnal-to- Noise Ratio) of images enconded by our algorithm is better than that of images encoded by the traditional algorithm by the amount about 4dB for for the test sets of multi-view images called dragon, kid, city and santa.

• Animal cells and systems
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• v.15 no.2
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• pp.95-106
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• 2011

The transient receptor potential vanilloid 4 (TRPV4) cation channel, a member of the TRP vanilloid subfamily, is expressed in a broad range of tissues. Nitric oxide (NO) as a gaseous signal mediator shows a variety of important biological effects. In many instances, NO has been shown to exhibit its activities via a protein S-nitrosylation mechanism in order to regulate its protein functions. With functional assays via site-directed mutagenesis, we demonstrate herein that NO induces the S-nitrosylation of TRPV4 $Ca^{2+}$ channel on the $Cys^{853}$ residue, and the S-nitrosylation of $Cys^{853}$ reduced its channel sensitivity to 4-${\alpha}$ phorbol 12,13-didecanoate and the interaction between TRPV4 and calmodulin. A patch clamp experiment and $Ca^{2+}$ image analysis show that the S-nitrosylation of $Cys^{853}$ modulates the TRPV4 channel as an inhibitor. Thus, our data suggest a novel regulatory mechanism of TRPV4 via NO-mediated S-nitrosylation on its $Cys^{853}$ residue.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.