A novel approach for image feature description based on dual gradient orientation histogram

2017 ◽  
Author(s):  
Jia-qi Bao ◽  
Xing-peng Mao
Keyword(s):  
Image Feature ◽  
Feature Description ◽  
Novel Approach ◽  
Orientation Histogram ◽  
Gradient Orientation ◽  
Dual Gradient

scholarly journals Segmentation-based Euler number with multi-levels for image feature description

2017 ◽  
Vol 111 ◽  
pp. 245-251
Author(s):  
Qian Zhang ◽  
Lin Wang ◽  
Jiang-Hao Yu ◽  
Minggui Zhang
Keyword(s):  
Euler Number ◽  
Image Feature ◽  
Feature Description

Medical Image Mining Using Fuzzy Connectedness Image Segmentation

2016 ◽  
pp. 184-209
Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Image Feature ◽  
Image Mining ◽  
Imaging Data ◽  
Dicom Image ◽  
Fuzzy Connectedness ◽  
Geometric Moments ◽  
Novel Approach ◽  
Minimum Number

This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.

Medical Image Mining Using Fuzzy Connectedness Image Segmentation

Biometrics ◽  
2017 ◽  
pp. 233-258
Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Image Feature ◽  
Image Mining ◽  
Imaging Data ◽  
Dicom Image ◽  
Fuzzy Connectedness ◽  
Geometric Moments ◽  
Novel Approach ◽  
Minimum Number

This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.

scholarly journals Compact Local Directional Texture Pattern for Local Image Description

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hui Zeng ◽  
Rui Zhang ◽  
Mingming Huang ◽  
Xiuqing Wang
Keyword(s):  
Image Matching ◽  
Local Region ◽  
Image Feature ◽  
Image Description ◽  
Texture Pattern ◽  
Gradient Magnitude ◽  
Gradient Orientation ◽  
Contrast Information ◽  
Directional Texture ◽  
Local Image

This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments have validated the effectiveness of the designed CLDTP descriptor.

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