Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yuzo Taniguchi
dblp:53/842
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1988
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware accelerators and domain-specific architectures · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing › pattern detection › anomaly detection
defect detection |
0.0 | 1 | 1988 | An Automatic Wafer Inspection System Using Pipelined Image Processing Techniques · IEEE Trans. Pattern Anal. Mach. Intell. 1988 |
Image and video processing
industrial inspection |
0.0 | 1 | 1988 | An Automatic Wafer Inspection System Using Pipelined Image Processing Techniques · IEEE Trans. Pattern Anal. Mach. Intell. 1988 |
Hardware accelerators and domain-specific architectures › image processing accelerator
pipelined image processor |
0.0 | 1 | 1988 | An Automatic Wafer Inspection System Using Pipelined Image Processing Techniques · IEEE Trans. Pattern Anal. Mach. Intell. 1988 |
Methods — techniques the papers use, named apart from their topics
pipelined image processing · 0.0design pattern data comparison · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1988 | An Automatic Wafer Inspection System Using Pipelined Image Processing TechniquesabstractAn automatic wafer pattern inspection system has been developed that can detect defective patterns 6 mu m or larger in multilayered wafer patterns at a speed 30 times faster than that of a human inspector. The false-alarm rate is less than 0.5 occurrences/chip. This performance is achieved mainly by the use of a special comparison method between two adjacent patterns obtained through a single optical setup, and also by the use of digital design pattern data (CAD data). The main functions of the design pattern data are to specify the inspection area, to designate optimum parameters for inspection, and to separate defective portions into different layers, thereby facilitating the classification of the defects. All image processing is performed in one pass by a high-speed pipeline-structured image processor that can analyze an input image signal at a 7 MHz video rate.> Haruo Yoda, Yozo Ohuchi, Yuzo Taniguchi, Masakazu Ejiri |
IEEE Trans. Pattern Anal. Mach. Intell. | 3 |