Anti-aliasing is generally an expensive process because either super-sampling or sophisticated rendering is required. This paper presents a new type of antialiasing filter, pixel tracing filler, for animation sequence...
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Anti-aliasing is generally an expensive process because either super-sampling or sophisticated rendering is required. This paper presents a new type of antialiasing filter, pixel tracing filler, for animation sequences, which does not require an additional sample or additional calculation in the rendering phase. The filter calculates the correlation among the images using animation information, and sub-pixel information is extracted from the sequence based on the correlation. Theoretical studies prove that the filter becomes an ideal anti-aliasing filter in the limit that the filter size is infinite. The algorithm is simple image processing implemented as post-filtering. The computational cost is independent of the complexity of the scene. Experiments demonstrate the efficiency of the scene. Experiments demonstrate the efficiency of the filter. Almost complete anti-aliasing was achieved at the rate of approximately 30 s per frame for very complex scenes at the resolution of 256 x 256 pixels. The pixel-tracing filter provides effective anti-aliasing for animation sequences at a very modest computational cost.
A hierarchical matching is the most efficient way to recognize a 3-D object. In most conventional methods of 3-D object recognition, a hierarchical structure is formed after the detailed 3D structure of an object has ...
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A hierarchical matching is the most efficient way to recognize a 3-D object. In most conventional methods of 3-D object recognition, a hierarchical structure is formed after the detailed 3D structure of an object has been extracted by dividing it coarsely. This approach is very inefficient, since it is difficult to obtain a detailed 3D structure initially in addition to a large amount of computation time. This paper proposes a method which extracts a multiscale 3D structure directly from coarse to fine. In this method, a space containing an object is divided into coarse voxels, and a ''3D voting'' is applied. This voting adds a certain value to a voxel through which a back-projected line connects the center of the camera lens and a feature point on an image. A voxel having a high value is regarded as a region containing a 3D feature point, and this is extracted. Extraction of a hierarchical structure is carried out by repeating the dividing process from coarse voxels to fine voxels so that the operation becomes efficient. This paper also analyzes the effects of the deviation of a feature point in an image and the deviation of the camera center on the 3D voting. The paper shows also that an application of a del(2)G filter to the voxel space (after 3D voting) is effective in suppressing the errors due to the deviations. The method has been confirmed successfully by processing six complex test images.
Describes a method for extracting nonrigid moving objects. Every moving object may be approximated by a set of iso-intensity patches. The method finds the motions of these patches by integrating the locations and the ...
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Describes a method for extracting nonrigid moving objects. Every moving object may be approximated by a set of iso-intensity patches. The method finds the motions of these patches by integrating the locations and the intensities of temporal edges. Then, it finds the objects by finding clusters among the motions of the patches. The proposed method can extract many nonrigid objects on a complicated background. The performance of the proposed method was tested successfully on extracting pedestrians from a cinematic sequence viewing a street crossing.< >
Stochastic approaches are very effective for modelling natural phenomena. This paper presents a motion model based on a stochastic process as well as physics, and proposes motion synthesis techniques for stochastic mo...
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A two-step procedure that finds natural clusters in geometric point data is described. The first step computes a hierarchical cluster tree minimizing an entropy objective function. The second step recursively explores...
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A two-step procedure that finds natural clusters in geometric point data is described. The first step computes a hierarchical cluster tree minimizing an entropy objective function. The second step recursively explores the tree for a level clustering having minimum description length. Together, these two steps find natural clusters without requiring a user to specify threshold parameters or so-called magic numbers. In particular, the method automatically determines the number of clusters in the input data. The first step exploits a new hierarchical clustering procedure called numerical iterative hierarchical clustering (NIHC). The output of NIHC is a cluster tree. The second step in the procedure searches the tree for a minimum-description-length (MDL) level clustering. The MDL formulation, equivalent to maximizing the posterior probability, is suited to the clustering problem because it defines a natural prior distribution.< >
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