Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 230

The DOI matches its Crossref record.

Stochastic models for closed boundary analysis: Representation and reconstruction

Kashyap, R., Chellappa, R.·1981·IEEE Transactions on Information Theory

Abstract

The analysis of closed boundaries of arbitrary shapes on a plane is discussed. Specifically, the problems of representation and reconstruction are considered. A one-to-one correspondence between the given closed boundary and a univariate or multivariate sequence of real numbers is set up. Univariate or multivariate circular autoregressive models are suggested for the representation of the sequence of numbers derived from the closed boundary. The stochastic model representing the closed boundary is invariant to transformations like sealing, translation, choice of starting point, and rotation over angles that are multiples of2\pi/N, whereNis the number of observations. Methods for estimating the unknown parameters of the model are given and a decision rule for choosing the appropriate number of coefficients is included. Constraints on the estimates are derived so that the estimates are invariant to the transformations of the boundaries. The stochastic model enables the reconstruction of a dosed boundary using FFT algorithms. Results of simulations are included and the application to contour coding is discussed.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tit.1981.1056390

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.

A few free checks a day · papers already verified open instantly · results are public

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

See all 230

Metadata

Authors
Kashyap, R., & Chellappa, R.
Venue
IEEE Transactions on Information Theory
Published
1981-09
Volume
27
Issue
5
Pages
627-637
ISSN
  • 0018-9448 (ISSN-L)
  • 1557-9654
OpenAlex
W2069527924
Cited by
230
References
0
FWCI
2.54
Type
journal article
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: Stochastic models for closed boundary analysis: Representation and reconstruction. https://app.cochat.ai/papers/10.1109/tit.1981.1056390
The paper
Kashyap, R., & Chellappa, R. (1981). Stochastic models for closed boundary analysis: Representation and reconstruction. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.1981.1056390

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.