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<a class="site-title" href="index.htm">Hyojin Kim</a>
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<h4>Image Segmentation</h4>
<span style="color:gray">Lawrence Livermore National Laboratory (2014 - 2015)</span>
<P>Unsupervised, automatic image segmentation without contextual knowledge, or user intervention is a challenging problem. The key to robust segmentation is an appropriate selection of local features and metrics. However, a single aggregation of the local features using a greedy merging order often results in incorrect segmentation. We developed an unsupervised approach, which uses the consensus inferred from hierarchical segmentation ensembles, for partitioning images into foreground and background regions. By exploring an expanded set of possible aggregations of the local features, the proposed method generates meaningful segmentations that are not often revealed when only the optimal hierarchy is considered. A graph cuts-based approach is employed to combine the consensus along with a foreground-background model estimate, obtained using the ensemble, for effective segmentation. Experiments with a standard dataset show promising results when compared to several existing methods including the state-of-the-art weak supervised techniques that use co-segmentation.</P>
<P>H. Kim, J. J. Thiagarajan and P-T. Bremer, "Image Segmentation using Consensus from Hierarchical Segmentation Ensembles", IEEE International Conference on Image Processing, 2014" <a href="paper/kim_icip2014.pdf">PDF</a></P>
<br>
<img src="img/imgseg1.jpg" width="320">
<br><br><br>
<h4>LiDAR-Optical Imagery Registration</h4>
<span style="color:gray">Lawrence Livermore National Laboratory (2012 - 2013)</span>
<P>Automatic fusion of aerial optical imagery and untextured LiDAR data is of significant interest for photo-realistic 3D modeling in recent years. We developed an unsupervised, robust registration method, based on feature correspondence between a LiDAR depth map and a stereo depth map. Each stereo depth map is generated from edge-preserving dense correspondence between the image and another optical image, followed by ground plane estimation and alignment for depth consistency. Our two-pass RANSAC with Maximum Likelihood estimation incorporates 2D-2D and 2D-3D correspondences to yield robust camera pose estimation. Experiments with <a href="http://dirsapps.cis.rit.edu/3d-rochester/?q=3d-rochester/">Rochester dataset</a> showed promising results, without using inital camera pose from GPS/INS.</P>
<P> H. Kim, C. Correa and N. Max, "Automatic Registration of LiDAR and Optical Imagery using Depth Map Stereo", IEEE International Conference on Computational Photography, pp. 205-212, 2014. <a href="paper/kim_iccp2014.pdf">PDF</a></P>
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<img src="img/lidar03.png" width="320">
<br><br><br>
<h4>Multi-View Stereo for Planar Scene Reconstruction</h4>
<span style="color:gray">UC Davis and Lawrence Livermore National Laboratory (2011 - 2012)</span>
<P>We developed a multi-view stereo algorithm for piecewise planar scene reconstruction and optimization. Our segmentation-based reconstruction algorithm is iterative to minimize our defined energy function, consisting of reconstruction, refinement and optimization steps. The first step is a plane initialization to allow each segment to have a set of initial plane candidates. Then a plane refinement based on LBFGS non-linear optimization improves the accuracy of the segment planes. Finally a plane optimization with a segment-adjacency graph leads to optimal segment planes, each of which is chosen among possible plane candidates by evaluating its relationship with adjacent planes in 3D. This algorithm yields better accuracy and performance, compared to the existing approaches described in this paper. The results show our method is suitable for outdoor or aerial urban scene reconstruction, especially in wide baselines and images with textureless regions.</P>
<P>H. Kim, H. Xiao and N. Max, "Piecewise Planar Scene Reconstruction and Optimization for Multi-View Stereo", Lecture Notes in Computer Science (ACCV 2012), Springer, Vol. 7727, pp 191-204, 2012. <a href="./kim_accv2012.pdf">PDF</a></P>
<P>H. Kim, Q. Hunter, M. Duchaineau, K. Joy and N. Max, "GPU-Friendly Multi-View Stereo for Outdoor Planar Scene Reconstruction", International Conference on Computer Vision Theory and Application, Vol. 2, pp 255-264, 2012.</P>
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<img src="img/mvs01.png" width="320">
<br><br><br>
<h4>Multi-View Integration</h4>
<span style="color:gray">UC Davis and Lawrence Livermore National Laboratory (2009-2010)</span>
<P>We developed GPU-based scalable volumetric integeration for multi-view stereo using GPU computing where massive GPU threads perform voxel carving in parallel to integerate multiple depth maps. The output is a photo-realistic 3D scene model in a volumetric or a point-based representation.</P>
<P>H. Kim, M. Duchaineau, N. Max, "GPU-based Scalable Volumetric Reconstruction for Multi-View Stereo",
International Conference on Image and Vision Computing, New Zealand, pp 477-482, 2011. <a href="./kim_mvi2011.pdf">PDF</a></P>
<br>
<img src="img/mvi01.png" width="320">
<br><br><br>
<h4>Casual Sterescopic Video</h4>
<span style="color:gray">Adobe Research (2010)</span>
<P>Casual Stereoscopic 3D (S3D) video compression and reconstruction techniques in Adobe Flash system for multiple webcam videos, making use of motion compensation (inter-frame) and stereo correlation (stereo-frame)</P>
<br>
<img src="img/svideo02.png" width="320">
<br><br><br>
<h4>Satellite-based Auroral Tomography</h4>
<span style="color:gray">Space Science Center, University of New Hampshire (2007-2008)</span>
<P>Satellite-based tomography and 3D auroral volume reconstruction algorithms for sequential
auroral images measured from satellite instruments or ground-based observatories</P>
<br><br><br>
<h4>Geomagnetic Database and Monitoring System</h4>
<span style="color:gray">Space Science Center, University of New Hampshire (2006-2007)</span>
<P>Monitoring software for the ground-based ULF data acquisition system</P>
<br><br><br>
<h4>Lossless Data Compression for Auroral Imagers</h4>
<span style="color:gray">Space Science Center, University of New Hampshire (2005-2006)</span>
<P>An adaptive lossless data compression based on pixel-delta encoding together with Rice compression, for rocket-borne auroral images as part of ROPA mission at Poker Flat Research Range in Alaska</P>
<br><br><br>
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