The DOI matches its Crossref record.
Recognition by Parts
Abstract
We argue that most natural objects have a part structure that we can recover from image data and thus use as the basis for "general-purpose" recognition. We describe a "parts" representation that is fairly general purpose, despite having only a small number of parameters. Having this expressive power captured by a small number of parameters allows us to approach the problem of recovering an object's part structure by use of minimal length encoding. We present several examples of recovering part structure using various types of range imagery to show that the recovery procedure is robust.
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
Cited by
Feature extraction from faces using deformable templates
Yuille, A. L., Cohen, D. S., & Hallinan, P. W. (2003) · Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1989.37836 · Cited by 708
Closed-form solutions for physically based shape modeling and recognition
Pentland, A., & Sclaroff, S. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.85660 · Cited by 466
Self-citationAutonomous exploration: driven by uncertainty
Whaite, P., & Ferrie, F. P. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.584097 · Cited by 240
Metadata
- Authors
- Pentland, A. P.
- Publisher
- Defense Technical Information Center
- Published
- 1987-08-25
- OpenAlex
- W1481387016
- Cited by
- 242
- References
- 0
- Type
- report
- Language
- en
- Open access
- closed
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: Recognition by Parts. https://app.cochat.ai/papers/10.21236/ada461783
- The paper
- Pentland, A. P. (1987). Recognition by Parts. https://doi.org/10.21236/ada461783
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.
