Differential Invariants under Gamma Correction
Andreas Siebert
Abstract
This paper presents invariants under gamma correction and similarity transformations. The invariants are local features based on differentials which are implemented using derivatives of the Gaussian. The use of the proposed invariant representation is shown to yield improved correlation results in a template matching scenario.
Create a lesson
Related papers
Towards Physically Grounded Articulated 3D Assets: A Multi-Agent Pipeline and Benchmark
Xudong Cai, Ruijie Zhu, Haoliang Shang et al.
InstantFusion: A Shared Latent Interface for Heterogeneous Image Generators
Xuanlang Dai, Jiazi Bu, Yujie Zhou et al.
Images Talk Back: Guiding MMDiTs by Suppressing Image-to-Text Feedback
Yucheng Yang, Xiaoxiao Sun, Shusheng Yang et al.
Hide and Seek: HIDE Concepts to SEEK explanations for black box models
Bhanu Tokas, Rahul Nair, Rockwell Jackson et al.
Can machines reason non-verbally?
Shaheer U. Saeed, Zion Tse, Gregory Slabaugh et al.
Where to Look Matters: Rethinking Sub-Volume Sampling in 3D Medical Self-supervised Learning
Junkai Liu, Le Zhang