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Complex Networks, Simple Vision

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Luciano da Fontoura Costa

Statistical Mechanics · Submitted 14 Mar 2004

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In addition to investigating the therefore obtained weight and adjacency matrices in terms of node degree densities, it is shown that the combination of the concepts of network hub and \emph{2-}expansion of the adjacency matrix provides an effective means to separate the image elements, a challenging task in computer vision known as segmentation.

how it is stated · asserted 1.00 read the record →

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arXiv IDcond-mat/0403346 (version 1)
Pith IDU7CC4WDI, Pith's permanent record of this paper and its review
CitationLuciano da Fontoura Costa. "Complex Networks, Simple Vision." arXiv:cond-mat/0403346 (2004). https://pith.science/paper/U7CC4WDI
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,023 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.05 · human participants 0.03 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.06 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper proposes and illustrates a general framework to integrate the areas of vision research and complex networks. Each image pixel is associated to a network node and the Euclidean distance between the visual properties (e.g. gray-level intensity, color or texture) at each possible pair of pixels is assigned as the respective edge weight. In addition to investigating the therefore obtained weight and adjacency matrices in terms of node degree densities, it is shown that the combination of the concepts of network hub and \emph{2-}expansion of the adjacency matrix provides an effective means to separate the image elements, a challenging task in computer vision known as segmentation.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper proposes and illustrates a general framework to integrate the areas of vision research and complex networks. contribution 1.00 0.77 0.11 asserted 0.97
s1 Each image pixel is associated to a network node and the Euclidean distance between the visual properties (e.g. gray-level intensity, color or texture) at each possible pair of pixels is assigned as the respective edge weight. method 1.00 0.65 0.04 asserted 0.99
s2 In addition to investigating the therefore obtained weight and adjacency matrices in terms of node degree densities, it is shown that the combination of the concepts of network hub and \emph{2-}expansion of the adjacency matrix provides an effective means to separate the image elements, a challenging task in computer vision known as segmentation. result 0.80 0.92 0.65 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.58.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper proposes and illustrates a general framework to integrate the areas of vision research and complex networks. Each image pixel is associated to a network node and the Euclidean distance between the visual properties (e.g. gray-level intensity, color or texture) at each possible pair of pixels is assigned as the respective edge weight. In addition to investigating the therefore obtained weight and adjacency matrices in terms of node degree densities, it is shown that the combination of the concepts of network hub and \emph{2-}expansion of the adjacency matrix provides an effective means to separate the image elements, a challenging task in computer vision known as segmentation.

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