Linear Bezier Curve Geometrical Feature Descriptor for Image Recognition

Pande, Sandeep Dwarkanath; Manna, Siladittya

doi:10.2174/2213275912666190617155154

Cited by 10 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the perspective of expressing and describing the target, any attribute of the target, whether it is special or not, belongs to the category of target characteristics, is an objective part of target characteristics, is the reason why the target is different from other targets, and is also the content of target description and expression [19]. Static properties, such as shape, colour, and hierarchy, are separated from dynamic attributes, such as motion speed, motion direction, and motion mode.…”

Section: Realization Of Gait Feature Recognitionmentioning

confidence: 99%

Athlete Gait Feature Recognition Method Based on Multisource Sensing Information

Xue

2022

Security and Communication Networks

View full text Add to dashboard Cite

Aiming at the problem of low accuracy of two-dimensional gait recognition at present, a gait feature recognition method based on multisource sensing information is proposed. The multisource sensing information is combined to collect the athlete’s gait characteristics, collect the single frame gait image sequence of the human lower limbs during the movement, and extract the human body’s three-dimensional feature data during human walking by using the body structure and multisource sensing information, so as to realize the separation of the athlete’s gait image background. Finally, it is confirmed by experiments that the recognition rate of athlete gait feature recognition method based on multisource sensing information is significantly improved.

show abstract

Section: Realization Of Gait Feature Recognitionmentioning

confidence: 99%

Athlete Gait Feature Recognition Method Based on Multisource Sensing Information

Xue

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The authors in [17] addressed some issues for designing THFB and proposed a novel category of THFB wavelets which are depending on half band polynomial. The authors of [18][19][20] have used Fourier transform based descriptors as representative features in image processing.…”

Section: IImentioning

confidence: 99%

Reconstruction of an Image Based on 13/19 Triplet Half-Band Wavelet Filter Bank and Orthogonal Matching Pursuit

Kanjalkar

Rao

2022

IJRITCC

View full text Add to dashboard Cite

Compressive Sensing Scheme for image reconstruction presented in this paper is depending on a combination of Orthogonal Matching Search and a 13/19 triplet half band filter bank (THFB) which is resulting from 1/2-band polynomial. Here, the consideration is made for 13/19 triplet half band wavelet filter sets. The half-band polynomial is applied which is generalized and used to receive the required frequency response. The image reconstruction is done later based on this. The designed triplet wavelet filters give a sparse image which is used for the input image. Gaussian probability density function and the Orthogonal Matching Pursuit (OMP) are presented for reconstructing the image. The results and observations demonstrate that the compressive sensing by using OMP and designed wavelet filters offers good result for performance as compared to the existing wavelet filters.

show abstract