Rotation Invariant Indexing For Image Using Zernike Moments and R–Tree

Nugroho, Saptadi; Utomo, Darmawan

doi:10.12928/telkomnika.v9i2.705

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…Due to these properties of ZM, it outperforms than many other descriptors, such as Geometric, Legendre and Pseudo Zernike moments [7], [8]. Nugroho and Tian et.al [9], [10] proved that, the rotarional invariant property gives the dominant results compare to other shape descriptors.…”

Section: Wherementioning

confidence: 97%

Shape Based Image Retrieval using Lower Order Zernike Moments

Sucharitha

Senapati

2017

IJECE

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Shape is one of the significant features of Content Based Image Retrieval (CBIR). This paper proposes a strong and successful shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments. For shape classification Zernike moment (ZM) is the dominant solution. The radial polynomial of Zernike moment produces the number of concentric circles based on the order. As the order increases number of circles will increases, due to this the local information of an image will be ignored. In this paper, we introduced a novel method for radial polynomial where local information of an image given importance. We succeeded to extract the local features and shape features at very a low order of polynomial compared to the state of traditional ZM.The proposed method gives an advantage of a lower order, less complex, and lower dimension feature vector.For more similar images we find that simple Euclidian distance approximately zero. Proposed method tested on a MPEG-7 CE-1 shape database, Coil-100 databases. Experiments demonstrated that it is outperforming in identifying the shape of an object in the image and reduced the retrieving time and complexity of calculations.

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Section: Wherementioning

confidence: 97%

Shape Based Image Retrieval using Lower Order Zernike Moments

Sucharitha

Senapati

2017

IJECE

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“…The goalpost is chosen as a landmark reference because it can be seen by the robot from any position on the field. The goalpost detection is calculated using the Center of Gravity (COG) method [11][12] and Android OpenCV Library. The enemy team's goal area and its own team's goal area are determined using Android's compass.…”

Section: The Robot Location On the Field 231 Goal Detectionmentioning

confidence: 99%

Location and Position Determination Algorithm For Humanoid Soccer Robot

2016

Self Cite

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“…Feature detection is a process of detecting keypoints of an object and extracting them into descriptors so that they can be matched. This process can be solved using many algorithms, such as SURF [1], SIFT [2], or Zernike moments [3]. There are two phases of feature detection, training phase and testing phase [4].…”

Section: Introductionmentioning

confidence: 99%