Nelson L. Chang

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We describe a novel and robust feature descriptor called ordinal spatial intensity distribution (OSID) which is invariant to any monotonically increasing brightness changes. Many traditional features are invariant to intensity shift or affine brightness changes but cannot handle more complex nonlinear brightness changes, which often occur due to the(More)
This paper focuses on the representation and view generation of three-dimensional (3-D) scenes. In contrast to existing methods that construct a full 3-D model or those that exploit geometric invariants, our representation consists of dense depth maps at several preselected viewpoints from an image sequence. Furthermore, instead of using multiple calibrated(More)
A fundamental problem in computer vision and graphics is that of arbitrary view synthesis for static 3-D scenes, whereby a user-speciied viewpoint of the given scene may be created directly from a representation. We propose a novel compact representation for this purpose called the multivalued representation (MVR). Starting with an image sequence captured(More)
We present a general framework for the modeling and optimization of scalable multi-projector displays. Based on this framework, we derive algorithms that can robustly optimize the visual quality of an arbitrary combination of projectors without manual adjustment. When the projectors are tiled, we show that our framework automatically produces blending maps(More)
We consider the problem of rendering high-resolution images on a display composed of multiple superimposed lower-resolution projectors. A theoretical analysis of this problem in the literature previously concluded that the multi-projector superimposition of low resolution projectors cannot produce high resolution images. In our recent work, we showed to the(More)
Multi-projector super-resolution is the dual of multi-camera super-resolution. The goal of projector super-resolution is to produce a high resolution frame via superimposition of multiple low resolution subframes. Prior work claims that it is impossible to improve resolution via superimposed projection except in specialized circumstances. Rigorous analysis(More)
Image recognition is one of the fundamental problems in mul-timedia analysis. Typically in the training database, there will be more than one image for each object, however most existing bag-of-features based approaches treat them independently and completely ignore the feature correspondence relationship among them. As a result, features corresponding to(More)