Point-pattern matching method using SURF and Shape Context

Gui, Yewei; Su, Ang; Du, Jing

doi:10.1016/j.ijleo.2012.05.037

Cited by 23 publications

(8 citation statements)

References 9 publications

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“…In this section, we use synthetic datasets and real world datasets to assess the effectiveness of the proposed LDSC. We take three algorithms as baselines: PSC [15], ICP [9] and TPS-RPM [11]. We use Accuracy as the evaluation criterium and describe it in subsection 4.1.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…They used the spatial distribution of point sets as features of each point, but it is not good for dealing with large angle rotation between two point sets. However, this kind of algorithms is designed for two dimensional problem and cannot work well in three dimension situation [15,16]. Positive Bipartite Graph Shape Context (PSC) algorithm was proposed especially for three dimensional point pattern matching problem [17].…”

Section: Shape Context Based Algorithmsmentioning

confidence: 99%

“…The similarity of two points p i and q j is defined in Eq. (15), where K is the number of divided regions and 144 in this paper, O k (x) is the value of the k th region of point x. The smaller w ij means more similarity between p i and q j .…”

Section: Three Dimensional Space Divisionmentioning

confidence: 99%

See 2 more Smart Citations

A Local Density Shape Context Algorithm for Point Pattern Matching in Three Dimensional Space

et al. 2018

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Three dimensional space point pattern matching technology shows significant usage in many scientific fields. It is a great challenge to match pairwise with rigid transformation in three dimensional space. In this paper, we propose an effect of Local Density Shape Context algorithm (LDSC). In LDSC, the point local density is firstly used for cutting down the negative impacting on extracting the feature descriptor. And the optimization of pairwise matching is firstly used in LDSC for improving the effectiveness. To demonstrate the performance of LDSC, we conduct experiments on synthetic datasets and real world datasets. The experimental results indicate that LDSC outperforms the three compared classical methods in most cases. LDSC is robust to outliers and noise.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Shape Context Based Algorithmsmentioning

confidence: 99%

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A Local Density Shape Context Algorithm for Point Pattern Matching in Three Dimensional Space

et al. 2018

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“…Liu [19] discussed several types of camera geometry and error analyses of feature point matching. Gui [20] presented a novel point-pattern matching method based on speeded-up robust features and the shape context to increase matching accuracy. Tong [21] improved the scale-invariant feature transform (SIFT) algorithm, and removed feature points surrounding image boundaries to increase matching accuracy.…”

Section: Introductionmentioning

confidence: 99%

Quantification Method for the Uncertainty of Matching Point Distribution on 3D Reconstruction

Bian

Liu

Wang

et al. 2020

IJGI

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Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching points. This study mainly focuses on the uncertainty of matching point distribution. First, horizontal dilution of precision (HDOP) is used to quantify the feature point distribution in the overlapping region of multiple images. Then, the quantization method is constructed. H D O P ∗ ¯ , the average of 2 × arctan ( H D O P × n 5 − 1 ) / π on all images, is utilized to measure the uncertainty of matching point distribution on 3D reconstruction. Finally, simulated and real scene experiments were performed to describe and verify the rationality of the proposed method. We found that the relationship between H D O P ∗ ¯ and the matching point distribution in this study was consistent with that between matching point distribution and 3D reconstruction. Consequently, it may be a feasible method to predict the quality of 3D reconstruction by calculating the uncertainty of matching point distribution.

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“…The solutions to the above problems depend on a few technologies, such as machine vision technology, image processing technology, artificial intelligence technology, positioning technology, and so on. At present, the method to acquire landmarks mostly relies on visual system [3,4], and the machine has been able to represent and describe the landmarks by themselves, namely through feature points detected in image [5][6][7]. Usually, a lot of feature points will appear when the image is processed, and we hope to select those feature points that the identification and robustness are better, so they can provide a good positioning reference for vehicle.…”

Section: Introductionmentioning

confidence: 99%

A Computational Model for Landmarks Acquisition in Positioning

Zhou

et al. 2015

J Intell Robot Syst

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This paper presents a computational model for landmarks acquisition in vehicle's positioning. Considering the machine vision, visual attention mechanism of biology, feature-integration theory and the demand of navigation, the computational model is divided into two part: pre-attention state and attention state, and the detailed process includes extraction of feature points, generation of local saliency value, generation of integration saliency value and selection of attention points. At the end of the paper, the proposed computational model is realized through the simulation. What's more, the robustness of the attention points and the vehicle's positioning performance when the attention points are used as landmarks are analyzed. Simulation validates that the computational model is availability. Besides, in terms of the performance of the self-attribute and providing positioning reference, the attention points behave well.

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Point-pattern matching method using SURF and Shape Context

Cited by 23 publications

References 9 publications

A Local Density Shape Context Algorithm for Point Pattern Matching in Three Dimensional Space

A Local Density Shape Context Algorithm for Point Pattern Matching in Three Dimensional Space

Quantification Method for the Uncertainty of Matching Point Distribution on 3D Reconstruction

A Computational Model for Landmarks Acquisition in Positioning

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