The Method of Shape Recognition Based on V-System

Song, Ruixia; Zhao, Zhaoxia; Li, Yanan; Zhang, Qiaoxia; Chen, Xi

doi:10.1109/fcst.2010.44

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Based on U-system, a novel orthogonal function system named the V-system [29]- [32]. This novel and special orthogonal system is applied to many fields, e.g., triangulated domains [31], shape recognition [33], 3D model retrieval [34]. The V-system can be considered as the generalization of the Haar wavelet system because the Haar wavelet system is a special case of the V-system of degree 0.…”

Section: Introductionmentioning

confidence: 99%

A Novel Shape Representation Method for Complex Trademark Image

Cai

Lan

et al. 2019

IEEE Access

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Trademark orthogonal representation is an important research field in artificial intelligence technology and has extensive applications in trademark retrieval and recognition. However, the Gibbs phenomenon arises when complex trademark shapes are represented by using traditional methods, such as continuous orthogonal function and wavelet. In order to represent the complex trademark without Gibbs phenomenon, a novel method named hierarchical V-system (HV system) is proposed in this study, which is generated by V-system of multi-degree k (k = 0, 1, 2, 3). The hierarchical structure brings more detailed shape representation information. We show that the proposed method in this paper can represent the trademark shapes with fewer finite terms and obtain accurate representation result. This study also proposes the normalized descriptors of hierarchical V-system (HV descriptors). Furthermore, we demonstrate that the HV descriptors satisfy the invariance in rotation, translation, and scale transform. The experimental results show that the hierarchical V-system method can give reasonable descriptors for representing complex trademark without Gibbs phenomenon.

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Section: Introductionmentioning

confidence: 99%

A Novel Shape Representation Method for Complex Trademark Image

Cai

Lan

et al. 2019

IEEE Access

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“…Development and Prediction of the shape by efficient, accurate, computationally fast and invariant algorithm for connected or overlapping objects in an image is the main consideration so that execution time and elapsed time is decreased. [6] F. Basic Steps 2. Over segmentation: Next step is to do over-segmentation and convert the image to binary image.…”

Section: Operational Stagesmentioning

confidence: 99%

Shape Prediction Linear Algorithm Using Fuzzy

Kaur¹,

Kundra²,

Kundra³

2012

IJACSA

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Abstract-The goal of the proposed method is to develop shape prediction algorithm using fuzzy that is computationally fast and invariant. To predict the overlapping and joined shapes accurately, a method of shape prediction based on erosion and over segmentation is used to estimate values for dependent variables from previously unseen predictor values based on the variation in an underlying learning data set.

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