The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Model-based recognition in robot vision
Open accessAbstract
This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the "bin-picking" problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2½-D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three central issues common to each category, namely, feature extraction, modeling, and matching, are examined in detail. An evaluation and comparison of existing industrial part-recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Representations for Rigid Solids: Theory, Methods, and Systems
Requicha, A. G. (1980) · ACM Computing Surveys · doi:10.1145/356827.356833 · Cited by 1,436
Automated Visual Inspection: A Survey
Chin, R. T., & Harlow, C. A. (1982) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1982.4767309 · Cited by 400
Self-citation
Cited by
Comparing images using the Hausdorff distance
Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.232073 · Cited by 4,545
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Metadata
- Authors
- Chin, R. T., & Dyer, C. R.
- Venue
- ACM Computing Surveys
- Published
- 1986-03
- Volume
- 18
- Issue
- 1
- Pages
- 67-108
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2026311529
- Cited by
- 567
- References
- 2
- FWCI
- 31.87
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Model-based recognition in robot vision. https://app.cochat.ai/papers/10.1145/6462.6464
- The paper
- Chin, R. T., & Dyer, C. R. (1986). Model-based recognition in robot vision. ACM Computing Surveys. https://doi.org/10.1145/6462.6464
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
