C
Charles R. Dyer
Researcher at University of Wisconsin-Madison
Publications - 141
Citations - 10220
Charles R. Dyer is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Motion estimation & Motion field. The author has an hindex of 43, co-authored 141 publications receiving 9919 citations. Previous affiliations of Charles R. Dyer include University of Wisconsin System & University of Maryland, College Park.
Papers
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Journal ArticleDOI
A Comparative Study of Texture Measures for Terrain Classification
TL;DR: In this paper, three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively.
Journal ArticleDOI
Photorealistic Scene Reconstruction by Voxel Coloring
Steven M. Seitz,Charles R. Dyer +1 more
TL;DR: A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information.
Proceedings ArticleDOI
View morphing
Steven M. Seitz,Charles R. Dyer +1 more
TL;DR: This paper introduces a simple extension to image morphing that correctly handles 3D projective camera and scene transformations and works by prewarping two images prior to computing a morph and then postwarped the interpolated images.
Journal ArticleDOI
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
TL;DR: The age manifold learning scheme for extracting face aging features is introduced and a locally adjusted robust regressor for learning and prediction of human ages is designed, which improves the age estimation accuracy significantly over all previous methods.
Journal ArticleDOI
Model-based recognition in robot vision
Roland T. Chin,Charles R. Dyer +1 more
TL;DR: This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision, and an evaluation and comparison of existing industrial part- recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.