blur invariant feature descriptor using multidirectional integral projection

Lee, Man Hee; Park, In Kyu

doi:10.4218/etrij.16.0115.0631

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…For instance, Tombari et al [46] developed more robust descriptors for identifying textureless image pairs, while Lee et al [47] proposed a descriptor with blur invariance by combining integral projections in four directions. However, these targeted descriptors only focus on specific challenging conditions and require extensive experimentation to enhance their matching robustness against various challenging factors.…”

Section: Feature Descriptionmentioning

confidence: 99%

A survey of feature matching methods

Huang,

Guo,

Wang

et al. 2024

IET Image Processing

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Feature matching plays a crucial role in computer vision, with applications in visual localization, simultaneous localization and mapping (SLAM), image stitching, and more. It establishes correspondences between sets of feature points from multiple images, enabling various tasks. Over the years, feature matching has witnessed significant development, with an increasing number of methods being applied. However, different methods exhibit different degrees of applicability in different scenarios and requirements due to their different rationales. To cope with these issues, a comprehensive analysis and comparison of matching methods are essential. Existing reviews often lack coverage of deep learning models and focus more on feature detection and description, neglecting the matching process. This survey investigates feature detection, description, and matching techniques within the feature‐based image‐matching pipeline. Representative methods, their mechanisms, and application scenarios are also briefly introduced. In addition, comprehensive evaluations of classical and state‐of‐the‐art methods are conducted through extensive experiments on representative datasets. Particularly, matching‐based applications are compared to fully demonstrate the advantages of the methods. Lastly, this survey highlights current problems and development directions in matching methods, serving as a reference for researchers in the field.

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Section: Feature Descriptionmentioning

confidence: 99%

A survey of feature matching methods

Huang,

Guo,

Wang

et al. 2024

IET Image Processing

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“…This is because EAS is more likely to detect keypoints in flat regions while the current descriptors rely largely on the neighboring gradients of keypoints. To alleviate this problem, a possible solution is to combine EAS with blur-robust feature descriptors (e.g., [50]), which will be investigated in the future.…”

Section: Application To Feature Matching With Real Datamentioning

confidence: 99%

Blur-Countering Keypoint Detection via Eigenvalue Asymmetry

Zhang

Akashi

2020

IEEE Access

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Well-known corner or local extrema feature based detectors such as FAST and DoG have achieved noticeable successes. However, detecting keypoints in the presence of blur has remained to be an unresolved issue. As a matter of fact, various kinds of blur (e.g., motion blur, out-of-focus and space-variant) remarkably increase challenges for keypoint detection. As a result, those methods have limited performance. To settle this issue, we propose a blur-countering method for detecting valid keypoints for various types and degrees of blurred images. Specifically, we first present a distance metric for derivative distributions, which preserves the distinctiveness of patch pairs well under blur. We then model the asymmetry by utilizing the difference of squared eigenvalues based on the distance metric. To make it scale-robust, we also extend it to scale space. The proposed detector is efficient as the main computational cost is the square of derivatives at each pixel. Extensive visual and quantitative results show that our method outperforms current approaches under different types and degrees of blur. Without any parallelization, our implementation achieves real-time performance for low-resolution images (e.g., 320 × 240 pixel).

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“…Moreover, the special filtering operation improves the localization accuracy and distinctiveness in-case of blur images. The work in [38] has used L0 gradient minimization [39] to make the descriptor blur invariant. The L0 gradient minimization based filtering was applied, but better results were obtained by using guided image filtering [40] in the proposed scheme.…”

Section: A Texture Smoothingmentioning

confidence: 99%

Image Local Features Description Through Polynomial Approximation

et al. 2019

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This work introduces a novel local patch descriptor that remains invariant under varying conditions of orientation, viewpoint, scale, and illumination. The proposed descriptor incorporate polynomials of various degrees to approximate the local patch within the image. Before feature detection and approximation, the image micro-texture is eliminated through a guided image filter with the potential to preserve the edges of the objects. The rotation invariance is achieved by aligning the local patch around the Harris corner through the dominant orientation shift algorithm. Weighted threshold histogram equalization (WTHE) is employed to make the descriptor insensitive to illumination changes. The correlation coefficient is used instead of Euclidean distance to improve the matching accuracy. The proposed descriptor has been extensively evaluated on the Oxford's affine covariant regions dataset, and absolute and transition tilt dataset. The experimental results show that our proposed descriptor can categorize the feature with more distinctiveness in comparison to state-of-the-art descriptors. INDEX TERMS Covariant, descriptor, handcrafted feature, patch, textures.

show abstract

blur invariant feature descriptor using multidirectional integral projection

Cited by 6 publications

References 24 publications

A survey of feature matching methods

A survey of feature matching methods

Blur-Countering Keypoint Detection via Eigenvalue Asymmetry

Image Local Features Description Through Polynomial Approximation

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