EDBT 2026 Demo / reviewers in the wild / expert
Tianhorng Chang
dblp:78/3903
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 1993
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 first-authorArtificial intelligence and machine learning · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing › multiscale analysis
multiresolution analysis |
0.0 | 1 | 1993 | Texture analysis and classification with tree-structured wavelet transform · IEEE Trans. Image Process. 1993 |
Image and video processing
texture analysis |
0.0 | 1 | 1993 | Texture analysis and classification with tree-structured wavelet transform · IEEE Trans. Image Process. 1993 |
Image and video processing › texture analysis
texture classification |
0.0 | 1 | 1993 | Texture analysis and classification with tree-structured wavelet transform · IEEE Trans. Image Process. 1993 |
Image and video processing › wavelet transform
wavelet packet decomposition |
0.0 | 1 | 1993 | Texture analysis and classification with tree-structured wavelet transform · IEEE Trans. Image Process. 1993 |
Methods — techniques the papers use, named apart from their topics
tree-structured wavelet transform · 0.0progressive classification · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1993 | Texture analysis and classification with tree-structured wavelet transformabstractA multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets is proposed. The development of this transform is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, it is possible to zoom into any desired frequency channels for further decomposition. In contrast, the conventional pyramid-structured wavelet transform performs further decomposition in low-frequency channels. A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed. The performance of the present method is compared with that of several other methods. Tianhorng Chang, C.-C. Jay Kuo |
IEEE Trans. Image Process. | 1 |
| 1992 | A wavelet transform approach to texture analysisabstractTraditional texture analysis algorithms focus too much on the local coupling between image pixels. Time/frequency analytical tools such as the Gabor and wavelet transforms can efficiently characterize the coupling of different scales in textures, and help to overcome this difficulty. However, the conventional wavelet transform, which has a finer resolution in the lower frequency channels, does not work properly for textured images, since textures are quasi-periodic signals whose dominant frequencies are located in the middle frequency regions. In the present research, a tree-structured wavelet transform is proposed. An adaptive procedure is developed to zoom into any frequency channels with significant information so that the decomposition can be further performed. The application of the new transform to texture classification is demonstrated.> Tianhorng Chang, C.-C. Jay Kuo |
ICASSP | 1 |
| 1992 | Texture classification with tree-structured wavelet transformabstractProposes a multiresolution approach based on a tree-structured wavelet transform for texture classification. The development of tree-structured wavelet transform is motivated by the observation that textures are quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, one is able to zoom into desired frequency channels and performs further decomposition. In contrast, the conventional wavelet transform only decomposes subsignals in low frequency channels. A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed.> Tianhorng Chang, C.-C. Jay Kuo |
ICPR (2) | 1 |