Jae Kyun Ahn

Efficient Fine-Granular Scalable Coding of 3D Mesh Sequences

An efficient fine-granular scalable coding algorithm of 3-D mesh sequences for low-latency streaming applications is proposed in this work. First, we decompose a mesh sequence into spatial and temporal layers to support scalable decoding. To support the finest-granular spatial scalability, we decimate only a single vertex at each layer to obtain the next layer. Then, we predict the coordinates of decimated vertices spatially and temporally based on a hierarchical prediction structure. Last, we quantize and transmit the spatio-temporal prediction residuals using an arithmetic coder. We propose an efficient context model for the arithmetic coding. Experiment results show that the proposed algorithm provides significantly better compression performance than the conventional algorithms, while supporting finer-granular spatial scalability.

Progressive compression of 3D triangular meshes using topology-based Karhunen-Loève transform

In this work, we propose a progressive compression algorithm using topology-based Karhunen-Loeve transform(KLT). First, we simplify an input mesh to represents an original mesh in several level of details. Then, coordinates of decimated vertices at each level are predicted from the coarser level mesh, and the prediction residuals are transmitted to a decoder. To provide high coding efficiency, we apply the topology-based KLT, which compacts the energy into a few coefficients, to the prediction residuals. Moreover, we develop a bit plane coder, which uses a context-adaptive arithmetic coder, for the entropy coding. Experiments on various 3D meshes show that the proposed algorithm provides enhanced compression performance.

Real-time segmentation of objects from video sequences with non-stationary backgrounds using spatio-temporal coherence

A real-time video segmentation algorithm, which can extract objects from video sequences even with non-stationary backgrounds, is proposed in this work. First, we segment the first frame into an object and a background interactively to build the probability density functions of colors in the object and the background. Then, for each subsequent frame, we construct a coherence strip, which is likely to contain the object contour, by exploiting spatio-temporal correlations. Finally, we perform the segmentation by minimizing an energy function composed of color, coherence, and smoothness terms. Experimental results on various test sequences show that the proposed algorithm provides accurate segmentation results in real-time, even though video sequences contain unstable camera motions.

R-D optimized progressive compression of 3D meshes using prioritized gate selection and curvature prediction

A rate-distortion (R-D) optimized progressive coding algorithm for three-dimensional (3D) meshes is proposed in this work. We propose the prioritized gate selection and the curvature prediction to improve the connectivity and geometry compression performance, respectively. Furthermore, based on the bit plane coding, we develop a progressive transmission method, which improves the qualities of intermediate meshes as well as that of the fully reconstructed mesh, and extend it to the view-dependent transmission method. Experiments on various 3D mesh models show that the proposed algorithm provides significantly better compression performance than the conventional algorithms, while supporting progressive reconstruction efficiently.

Fast background subtraction algorithm using two-level sampling and silhouette detection

An efficient background subtraction algorithm using two-level sampling and silhouette detection is proposed in this work. In the two-level sampling, we identify moving objects at the block level and then at the pixel level. Then, in the silhouette detection, around each sampled foreground pixel, we refine the shapes of foreground objects. We also develop two fast modes for the silhouette detection, which utilizes the spatio-temporal coherence of moving foreground objects. Simulation results demonstrate that the proposed algorithm provides accurate segmentation results without flickering artifacts, while requiring a low computational load.

Large-Scale 3D Point Cloud Compression Using Adaptive Radial Distance Prediction in Hybrid Coordinate Domains

An adaptive range image coding algorithm for the geometry compression of large-scale 3D point clouds (LS3DPCs) is proposed in this work. A terrestrial laser scanner generates an LS3DPC by measuring the radial distances of objects in a real world scene, which can be mapped into a range image. In general, the range image exhibits different characteristics from an ordinary luminance or color image, and thus the conventional image coding techniques are not suitable for the range image coding. We propose a hybrid range image coding algorithm, which predicts the radial distance of each pixel using previously encoded neighbors adaptively in one of three coordinate domains: range image domain, height image domain, and 3D domain. We first partition an input range image into blocks of various sizes. For each block, we apply multiple prediction modes in the three domains and compute their rate-distortion costs. Then, we perform the prediction of all pixels using the optimal mode and encode the resulting prediction residuals. Experimental results show that the proposed algorithm provides significantly better compression performance on various range images than the conventional image or video coding techniques.

3D mesh compression based on dual-ring prediction and MMSE prediction

A three-dimensional (3D) mesh compression algorithm based on novel prediction methods and a mode decision scheme is proposed in this work. After decomposing an input mesh into base and refinement layers, we segment the geometry data of each layer into clusters. To encode vertex positions efficiently, we propose two prediction methods: the dual ring prediction and the minimum mean square error (MMSE) prediction. Also, we develop a mode decision scheme that selects the best prediction mode for each cluster. Simulation results demonstrate that the proposed algorithm provides significantly better compression performance than conventional techniques.

Stitching of heterogeneous images using depth information

We propose a novel heterogeneous image stitching algorithm, which employs disparity information as well as color information. It is challenging to stitch heterogeneous images that have different background colors and diverse foreground objects. To overcome this difficulty, we set the criterion that objects should preserve their shapes in the stitched image. To satisfy this criterion, we derive an energy function using color and disparity gradients. As the gradients are highly correlated with object boundaries, we can find the optimal seam from the energy function, along which two images are pasted. Moreover, we develop a retargeting scheme to reduce the size of the stitched image further. Experimental results demonstrate that the proposed algorithm is a promising tool for stitching heterogeneous images.

High quality video acquisition and segmentation using alternate flashing system

A high quality video acquisition algorithm is proposed in this work.We construct a flashing system to capture lit and unlit frames alternately. We develop a reliable motion estimation scheme, which matches correspondences between an unlit frame and a lit frame. Then, we construct a high quality frame, which combines natural scene mood in the unlit frame and textural details in the lit frame. Furthermore, we propose an object segmentation algorithm based on the observation that foreground objects are more sensitive to flash lights than backgrounds. Simulation results demonstrate that the proposed algorithm can acquire high quality video sequences and segment foreground objects from the sequences efficiently.