Vehicle-logo Recognition Method Based on Tchebichef Moment Invariants and SVM

Dai, Shijie; Huang, He; Gao, Zhangying; Li, Kai; Shu-mei, Xiao

doi:10.1109/wcse.2009.263

Cited by 18 publications

(5 citation statements)

References 4 publications

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“…Additional approaches to quantifying shape features are described in Yang et al (2008). Based on the unique requirements for phytolith morphometrics, the most promising of these approaches include: Zernike moments (Athilakshm and Wahi 2014;Cao et al 2013;Garcia-Ordas et al 2014;Khotanzad and Hong 1990;Mahi and Kaouadji 2014;Tsolakidis et al 2014); Chebyshev moments (Dai et al 2009;Marcos and Cristobal. 2013; curvature scale-space (Kumar et al 2012); bounding-box splitting (Bauckhage and Tsotsos 2005); wavelet analysis (Essendelft 2013;Gui et al 2014); shock graphs (Macrini 2003;Sebastian et al 2004;Siddiqi et al 1999); and boundaryline analysis (Liang et al 2012).…”

Section: Emergent Shape Descriptorsmentioning

confidence: 99%

A conceptual framework for a computer-assisted, morphometric-based phytolith analysis and classification system

Evett

Cuthrell

2016

Journal of Archaeological Science

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Although automated approaches to shape analysis and object classification have been widely applied in the biological sciences, technical and time considerations have limited their use in phytolith research. As advanced microscopy systems become more affordable and accessible and digital imaging software provides a wider range of sophisticated analytical tools, there is increased potential for effective use of machinevision and automation in phytolith research. In this paper, we describe technical limitations of phytolith imaging and identify several techniques that might improve results. Drawing on examples of software developed for related disciplines, we then describe a conceptual framework for development and integration of automated phytolith analysis software for: separating phytoliths from non-phytolith material in digital images; segmentation of phytolith boundaries; quantitative phytolith feature extraction, including a discussion of potentially more powerful, non-traditional parameters of phytolith shape and texture; phytolith classification and identification; and phytolith database image retrieval. While recognizing the difficulty of implementing this framework and the need for extensive empirical testing of suggested approaches on phytoliths, we examine the possibility of aggregating quantitative phytolith data collected in studies worldwide to construct a cloud-based database of phytolith images with associated morphotype data.

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Section: Emergent Shape Descriptorsmentioning

confidence: 99%

A conceptual framework for a computer-assisted, morphometric-based phytolith analysis and classification system

Evett

Cuthrell

2016

Journal of Archaeological Science

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“…Logo recognition is reviewed from intelligent transportation, advertisement, and document retrieval. First, Dai et al [2] used the Tchebichef moment invariants and support vector machine to recognize the vehicle logos. However, the computation process has much computational load.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Moments-Based Logo Recognition Methods

Zhang

Wang

Anwar

et al. 2014

Abstract and Applied Analysis

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Logo recognition is an important issue in document image, advertisement, and intelligent transportation. Although there are many approaches to study logos in these fields, logo recognition is an essential subprocess. Among the methods of logo recognition, the descriptor is very vital. The results of moments as powerful descriptors were not discussed before in terms of logo recognition. So it is unclear which moments are more appropriate to recognize which kind of logos. In this paper we find out the relations between logos with different transforms and moments, which moments are fit for logos with different transforms. The open datasets are employed from the University of Maryland. The comparisons based on moments are carried out from the aspects of logos with noise, and rotation, scaling, rotation and scaling.

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“…In this respect we have few distinctive groups. The first group of methods belongs to the domain of document processing (Chen et al 2003), the second is logo and trademark recognition in outdoor conditions (Gori et al 2003), such as sport events (Ballan et al 2008), the next one concerns car logo detection and recognition (Dai et al 2009;Psyllos et al 2010;Mao et al 2013), just to name a few.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, the obtained results are sensitive to viewpoint change as well as plane rotation. In other work, Dai et al (2009) propose application of the Tchebichef moment invariants and SVM Dai et al (2009). However, these are also sensitive to noise and geometrical distortions.…”

Section: Related Workmentioning

confidence: 99%

Hybrid ensemble of classifiers for logo and trademark symbols recognition

Cyganek

2014

Soft Comput

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The paper presents a hybrid ensemble of diverse classifiers for logo and trademark symbols recognition. The proposed ensemble is composed of four types of different member classifiers. The first one compares color distribution of the logo patterns and is responsible for sifting out images of different color distribution. The second of the classifiers is based on the structural tensor recognition of local phase histograms. A proposed modification in this module consists of tensor computation in the space of the morphological scale-space. Thanks to this, more discriminative histograms describing global shapes are obtained. Next in the chain, is a novel member classifier that joins the Hausdorff distance with the correspondence measure of the log-polar patches computed around the corner points. This sparse classifier allows reliable comparison of even highly deformed patterns. The last member classifier relies on the statistical affine moment invariants which describe global shapes. However, a real advantage is obtained by joining the aforementioned base classifiers into a hybrid ensemble of classifiers, as proposed in this paper. Thanks to this a more accurate response and generalizing properties are obtained at reasonable computational requirements. Experimental results show good recognition accuracy even for the highly deformed logo patterns, as well as fair generalization properties which support human search and logo assessment tasks.

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Vehicle-logo Recognition Method Based on Tchebichef Moment Invariants and SVM

Cited by 18 publications

References 4 publications

A conceptual framework for a computer-assisted, morphometric-based phytolith analysis and classification system

A conceptual framework for a computer-assisted, morphometric-based phytolith analysis and classification system

A Comparison of Moments-Based Logo Recognition Methods

Hybrid ensemble of classifiers for logo and trademark symbols recognition

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