Hierarchical projective invariant contexts for shape recognition

Jia, Qi; Fan, Xin; Liu, Yu; Li, Haojie; Luo, Zhongxuan; Guo, He

doi:10.1016/j.patcog.2015.11.003

Cited by 33 publications

(12 citation statements)

References 47 publications

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“…The projective invariance of CN is introduced in [24] acting as geometric constraints for fiducial point localization under face pose changes. Later, Jia et al employ this invariant property to construct a shape descriptor robust to perspective deformations [25]. For the first time, we explicitly take the advantage of the CN property giving the characterization of the intrinsic geometry of an underlying planar curve of points.…”

Section: Introductionmentioning

confidence: 99%

A Fast Ellipse Detector Using Projective Invariant Pruning

Jia

Fan

Luo

et al. 2017

IEEE Trans. on Image Process.

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Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3×3 matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.

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Section: Introductionmentioning

confidence: 99%

A Fast Ellipse Detector Using Projective Invariant Pruning

Jia

Fan

Luo

et al. 2017

IEEE Trans. on Image Process.

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“…Shape context [1] and its variants [26,34] are among the most popular shape descriptors. Researchers also develop descriptors to accommodate a wide range of geometric transformations [15]. Motivated by the middle level "bag-of-features", Wang et al develop a bag of contour fragments (BCF) approach that achieves the state-of-the-art for simple shape contour classification [37].…”

Section: Related Workmentioning

confidence: 99%

Coupling Deep Textural and Shape Features for Sketch Recognition

Jia

Fan

Yu³

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Recognizing freehand sketches with high arbitrariness is such a great challenge that the automatic recognition rate has reached a ceiling in recent years. In this paper, we explicitly explore the shape properties of sketches, which has almost been neglected before in the context of deep learning, and propose a sequential dual learning strategy that combines both shape and texture features. We devise a two-stage recurrent neural network to balance these two types of features. Our architecture also considers stroke orders of sketches to reduce the intra-class variations of input features. Extensive experiments on the TU-Berlin benchmark set show that our method achieves over 90% recognition rate for the first time on this task, outperforming both humans and state-of-the-art algorithms by over 19 and 7.5 percentage points, respectively. Especially, our approach can distinguish the sketches with similar textures but different shapes more effectively than recent deep networks. Based on the proposed method, we develop an on-line sketch retrieval and imitation application to teach children or adults to draw. The application is available as Sketch.Draw. CCS CONCEPTS • Computing methodologies → Object recognition.

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“…This method has a clear physical interpretation and works very fast for estimating the affine transform. Jia et al [14] developed a new projective invariant, the characteristic number (CN) whose values is calculated on a series of five sample points along the shape contour. With the sample intervals varying, a coarse to fine strategy is developed for capturing both the global geometry described by projective invariants and the local contextual information.…”

Section: Related Workmentioning

confidence: 99%

“…3. This dataset is publicly available and has been used as test case in many works [7][10] [14]. To make a fair comparison, we choose those approaches which are particularly designed for affine shape recognition.…”

Section: Multiview Curve Database (Mcd)mentioning

confidence: 99%

A Novel Line Integral Transform for 2D Affine-Invariant Shape Retrieval

Wang

Gao

2020

Lecture Notes in Computer Science

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Radon transform is a popular mathematical tool for shape analysis. However, it cannot handle affine deformation. Although its extended version, trace transform, allow us to construct affine invariants, they are less informative and computational expensive due to the loss of spatial relationship between trace lines and the extensive repeated calculation of transform. To address this issue, a novel line integral transform is proposed. We first use binding line pairs that have the desirable property of affine preserving as a reference frame to rewrite the diametrical dimension parameters of the lines in a relative manner which make them independent on affine transform. Along polar angle dimension of the line parameters, a moment-based normalization is then conducted to degrade the affine transform to similarity transform which can be easily normalized by Fourier transform. The proposed transform is not only invariant to affine transform, but also preserves the spatial relationship between line integrals which make it very informative. Another advantage of the proposed transform is that it is more efficient than the trace transform. Conducting it one time can allow us to achieve a 2D matrix of affine invariants. While conducting the trace transform once only generates a single feature and multiple trace transforms of different functionals are needed to derive more to make the descriptors informative. The effectiveness of the proposed transform has been validated on two types of standard shape test cases, affinely distorted contour shape dataset and region shape dataset, respectively.

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Hierarchical projective invariant contexts for shape recognition

Cited by 33 publications

References 47 publications

A Fast Ellipse Detector Using Projective Invariant Pruning

A Fast Ellipse Detector Using Projective Invariant Pruning

Coupling Deep Textural and Shape Features for Sketch Recognition

A Novel Line Integral Transform for 2D Affine-Invariant Shape Retrieval

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