2D object recognition based on curvature functions obtained from local histograms of the contour chain code

Bandera, Antonio; Urdiales, Cristina; Arrebola, Fabián; Sandoval, F.

doi:10.1016/s0167-8655(98)00123-8

Cited by 35 publications

(17 citation statements)

References 11 publications

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“…Bandera, et.al [2] proposed a real time 2D object recognition algorithm. Contours are represented by their curvature functions, decomposed in the Fourier domain as linear combination of a set of representative objects.…”

Section: Literature Surveymentioning

confidence: 99%

Image Retrieval using Chain code and Autoregression

Shweta¹,

Sachin²

2017

IJCA

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Over the years passed has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Similarly, digital image retrieval has expanded in many directions that are resulting into explosion in the volume of image data required to be organized. This paper presents a framework for image retrieval based on chain code and auto regression that helps to achieve higher retrieval efficiency. In this paper, we discuss about the key contributions of the methodology that is followed while performing experiment for image retrieval based on chain code and auto regression. Here comparative study of results and also efficiency of both these image retrieval techniques are discussed which are obtained while experimentation.

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Section: Literature Surveymentioning

confidence: 99%

Image Retrieval using Chain code and Autoregression

Shweta¹,

Sachin²

2017

IJCA

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“…In the proposed method, the leader algorithm is extended to generate a hierarchical structure with clusters (leaders as representatives) in the first level and subclusters (subleaders as representatives) in the second level. Bandera et al (1999) have used a two level classification stage using mode analysis/ centroid cluster analysis. They consider some prototypes from the base clusters initially and form type 1 and type 2 clusters based on the feature vector and curvature function respectively.…”

Section: Incremental Clusteringmentioning

confidence: 99%

Leaders–Subleaders: An efficient hierarchical clustering algorithm for large data sets

Vijaya

Murty

Subramanian

2004

Pattern Recognition Letters

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“…Fourier descriptors have been successfully applied to contour enhancement and object inspection, providing position, orientation and scale invariant properties by normalization. (Bandera, Urdiales, Arrebola, & Sandoval, 1999;Sánchez-Marín, 2000). However, performing forward and backward transformations requires heavy computation when calculating the complex equations.…”

Section: Introductionmentioning

confidence: 99%