mlsanityData to 7 Oct 2026

Second-order Gaussian directional derivative representations for image high-resolution corner detection

arXiv 2601.08182 ↗First posted 13 Jan 2026cs.CVcited by 3

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Abstract (arXiv)

Corner detection is widely used in various computer vision tasks, such as image matching and 3D reconstruction. Our research indicates that there are theoretical flaws in Zhang et al.'s use of a simple corner model to obtain a series of corner characteristics, as the grayscale information of two adjacent corners can affect each other. In order to address the above issues, a second-order Gaussian directional derivative (SOGDD) filter is used in this work to smooth two typical high-resolution angle models (i.e. END-type and L-type models). Then, the SOGDD representations of these two corner models were derived separately, and many characteristics of high-resolution corners were discovered, which enabled us to demonstrate how to select Gaussian filtering scales to obtain intensity variation information from images, accurately depicting adjacent corners. In addition, a new high-resolution corner detection method for images has been proposed for the first time, which can accurately detect adjacent corner points. The experimental results have verified that the proposed method outperforms state-of-the-art methods in terms of localization error, robustness to image blur transformation, image matching, and 3D reconstruction.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 83 references that are in the map. First posted on arXiv 13 Jan 2026.

Builds on

21 of 83 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020End-to-End Object Detection with Transformerscited by 7,384
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  4. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
  5. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  6. 2019D2-Net: A Trainable CNN for Joint Detection and Description of Local Featurescited by 324
  7. 2016LIFT: Learned Invariant Feature Transformcited by 305
  8. 2017HPatches: A benchmark and evaluation of handcrafted and learned local descriptorscited by 261
  9. 2018LF-Net: Learning Local Features from Imagescited by 151
  10. 2020ASLFeat: Learning Local Features of Accurate Shape and Localizationcited by 129
  11. 2024XFeat: Accelerated Features for Lightweight Image Matchingcited by 113
  12. 2021ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extractioncited by 77
9 more
  1. 2023DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matchingcited by 62
  2. 2020D2D: Keypoint Extraction with Describe to Detect Approachcited by 34
  3. 2022BALF: Simple and Efficient Blur Aware Local Feature Detectorcited by 34
  4. 2022Self-Supervised Equivariant Learning for Oriented Keypoint Detectioncited by 26
  5. 2021NDPNet: A novel non-linear data projection network for few-shot fine-grained image classificationcited by 13
  6. 2025Dynamic Accumulated Attention Map for Interpreting Evolution of Decision-Making in Vision Transformercited by 12
  7. 2022ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretizationcited by 10
  8. 2023Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detectioncited by 6
  9. 2021HarrisZ^+: Harris Corner Selection for Next-Gen Image Matching Pipelinescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Deep learning-based neurodevelopmental assessment in preterm infantscited by 3
  2. 2026GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury predictioncited by 1
  3. 2026Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classificationcited by 0