3D object recognition using scale-invariant features

Lim, Jeonghun; Lee, Kunwoo

doi:10.1007/s00371-017-1453-y

Cited by 11 publications

(2 citation statements)

References 31 publications

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“…However, the two main differences between these descriptors are, (i) RoPS requires mesh information for the LRF estimation, while MVD can be directly applied on the point cloud and (ii), during the feature calculation process RoPS creates a quantitative distribution matrix for each projection of V , while MVD creates a local depth distribution matrix. Lim and Lee [49] extend the 2D SIFT descriptor to be applicable on a 3D mesh and exploit the gradients of the scalar functions defined on V by convolving the point cloud with Gaussian kernels. Then adjacent Gaussian functions are subtracted to produce the DoG functions and this procedure repeats with down‐sampled Gaussian functions in the next octave.…”

Section: 2d/3d Keypoint Detection and Feature Description Methodsmentioning

confidence: 99%

Performance evaluation of single and cross‐dimensional feature detection and description

2020

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Three-dimensional local feature detection and description techniques are widely used for object registration and recognition applications. Although several evaluations of 3D local feature detection and description methods have already been published, these are constrained in a single dimensional scheme, i.e. either 3D or 2D methods that are applied onto multiple projections of the 3D data. However, cross-dimensional (mixed 2D and 3D) feature detection and description has yet to be investigated. Here, we evaluated the performance of both single and cross-dimensional feature detection and description methods on several 3D datasets and demonstrated the superiority of cross-dimensional over single-dimensional schemes. 2D / 3D Keypoint Detection and Feature Description Methods Keypoint DetectorsKeypoint detectors analyse the structure around a vertex or a pixel depending on the data domain (3D or 2D,

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Section: 2d/3d Keypoint Detection and Feature Description Methodsmentioning

confidence: 99%

Performance evaluation of single and cross‐dimensional feature detection and description

2020

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“…Here These strategic places will offer suitable hiding spots for the hidden message. In order to identify, describe, and match local characteristics in pictures, David Lowe created the SIFT algorithm in 1999 [5]. SIFT identifies and characterizes local features in images.…”

Section: B Detection Of Key Pointsmentioning

confidence: 99%

Quantum Steganography: Hiding Secret Messages in Images using Quantum Circuits and SIFT

Azooz,

Salah,

Kherallah

et al. 2023

IJACSA

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In today's era of escalating digital threats and the growing need for safeguarding sensitive information, this research strives to advance the field of information concealment by introducing a pioneering steganography methodology. Our approach goes beyond the conventional boundaries of image security by seamlessly integrating classical image processing techniques with the cutting-edge realm of quantum encoding. The foundation of our technique lies in the meticulous identification of distinctive features within the cover image, a crucial step achieved through the utilization of SIFT (Scale-Invariant Feature Transform). These identified key points are further organized into coherent clusters employing the K-means clustering algorithm, forming a structured basis for our covert communication process. The core innovation of this research resides in the transformation of the concealed message into a NEQR (Novel Enhanced Quantum Representation) code, a quantum encoding framework that leverages the power of quantum circuits. This transformative step ensures not only the secrecy but also the integrity of the hidden information, making it highly resistant to even the most sophisticated decryption attempts. The strategic placement of the quantum circuit representing the concealed message at the centroids of the clusters generated by the Kmeans algorithm conceals it within the cover image seamlessly. This fusion of classical image processing and quantum encoding results in an unprecedented level of security for the embedded information, rendering it virtually impervious to unauthorized access. Empirical findings from extensive experimentation affirm the robustness and efficacy of our proposed strategy.

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Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes

Zhou

Tong

et al. 2018

Vis Comput

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3D feature descriptors provide information between corresponding models and scenes. 3D objection recognition in cluttered scenes, however, remains a largely unsolved problem. Practical applications impose several challenges which are not fully addressed by existing methods. Especially in cluttered scenes there are many feature mismatches between scenes and models.We therefore propose Histograms of Gaussian Normal Distribution (HGND) for extracting salient features on a local reference frame (LRF) that enables us to solve this problem. We propose a LRF on each local surface patches using the scatter matrix's eigenvectors. Then the HGND information of each salient point is calculated on the LRF, for which we use both the mesh and point data of the depth image. Experiments on 45 cluttered scenes of the Bologna Dataset and 50 cluttered scenes of the UWA Dataset are made to evaluate the robustness and descriptiveness of our HGND. Experiments carried out by us demonstrate that HGND obtains a more reliable matching rate than stateof-the-art approaches in cluttered situations.

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3D object recognition using scale-invariant features

Cited by 11 publications

References 31 publications

Performance evaluation of single and cross‐dimensional feature detection and description

Performance evaluation of single and cross‐dimensional feature detection and description

Quantum Steganography: Hiding Secret Messages in Images using Quantum Circuits and SIFT

Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes

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