Ju Yong Chang

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In this paper, we propose a new method to integrate multiview normal fields using level sets. In contrast with conventional normal integration algorithms used in shape from shading and photometric stereo that reconstruct a 2.5D surface using a single-view normal field, our algorithm can combine multiview normal fields simultaneously and recover the full 3D(More)
In this paper, we propose a novel framework for modeling image-dependent contextual relationships using graph-based context model. This approach enables us to selectively utilize the contextual relationships suitable for an input query image. We introduce a context link view of contextual knowledge, where the relationship between a pair of annotated regions(More)
We present a system for fast model-based segmentation and 3D pose estimation of specular objects using appearance based specular features. We use observed (a) specular reflection and (b) specular flow as cues, which are matched against similar cues generated from a CAD model of the object in various poses. We avoid estimating 3D geometry or depths, which is(More)
In this paper, we present a new surfel (surface element) based multi-view stereo algorithm that runs entirely on GPU. We utilize the flexibility of surfel-based 3D shape representation and global optimization by graph cuts in the same framework. Unlike previous works, the algorithm is optimized to massive parallel processing on GPU. First, we construct(More)
In this paper, we propose a new stereo matching algorithm using an iterated graph cuts and mean shift filtering technique. Our algorithm consists of following two steps. In the first step, given an estimated sparse RDM (Reliable Disparity Map), we obtain an updated dense disparity map through a new constrained energy minimization framework that can cope(More)
This paper describes a new semi-global method for SFS (Shape-from-Shading) using graph cuts. The new algorithm combines the local method proposed by Lee and Rosenfeld [I] and the global inethod using energy minimization technique. By employing a new global energy minimization formulatioK the convedconcave ambiguity problcm of the Lee and Rosenfeld method(More)
We present a new gesture recognition method using multimodal data. Our approach solves a labeling problem, which means that gesture categories and their temporal ranges are determined at the same time. For that purpose, a generative probabilistic model is formalized and it is constructed by nonparametrically estimating multi-modal densities from a training(More)
  • Ju Yong Chang
  • IEEE Transactions on Pattern Analysis and Machine…
  • 2016
We present a new gesture recognition method that is based on the conditional random field (CRF) model using multiple feature matching. Our approach solves the labeling problem, determining gesture categories and their temporal ranges at the same time. A generative probabilistic model is formalized and probability densities are nonparametrically estimated by(More)
In this paper, we propose a new stereo matching algorithm using an iterated graph cuts and mean shift filtering technique. Our algorithm estimates the disparity map progressively through the following two steps. In the first step, with a previously estimated RDM (reliable disparity map) that consists of sparse ground control points, an updated dense(More)
In the present paper, a novel image classification method that uses the hierarchical structure of categories to produce more semantic prediction is presented. This implies that our algorithm may not yield a correct prediction, but the result is likely to be semantically close to the right category. Therefore, the proposed method is able to provide a more(More)