Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU

Arisetty Sree Ashish; B. Ashwath Rao

doi:10.1109/ICKECS56523.2022.10060766

Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU

Arisetty Sree Ashish, B. Ashwath Rao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

One of the various techniques employed for image feature extraction is the Local Neighborhood Difference Pattern, also called as LNDP. LNDP considers the relationship between neighbors of a central pixel with its adjacent pixels and transforms this mutual relationship of all the neighboring pixels into a binary pattern. It has proven to be a powerful and effective descriptor for texture analysis. A parallel implementation of LNDP using Compute Unified Device Architecture (CUDA) has been proposed in this paper. A speedup of about 1000 times has been achieved through a shared memory parallel implementation for large images. Thus, an efficacious and efficient implementation has resulted in an increased execution speed and reduced execution time.

Original language	English
Title of host publication	IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665456371
DOIs	https://doi.org/10.1109/ICKECS56523.2022.10060766
Publication status	Published - 2022
Event	2022 IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022 - Chickballapur, India Duration: 28-12-2022 → 29-12-2022

Publication series

Name	IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022

Conference

Conference	2022 IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022
Country/Territory	India
City	Chickballapur
Period	28-12-22 → 29-12-22

All Science Journal Classification (ASJC) codes

Computer Science Applications
Control and Optimization
Modelling and Simulation
Education
Artificial Intelligence
Computer Networks and Communications
Information Systems and Management

Access to Document

10.1109/ICKECS56523.2022.10060766

Cite this

Sree Ashish, A., & Ashwath Rao, B. (2022). Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU. In IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022 (IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICKECS56523.2022.10060766

Sree Ashish, Arisetty ; Ashwath Rao, B. / Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU. IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022).

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title = "Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU",

abstract = "One of the various techniques employed for image feature extraction is the Local Neighborhood Difference Pattern, also called as LNDP. LNDP considers the relationship between neighbors of a central pixel with its adjacent pixels and transforms this mutual relationship of all the neighboring pixels into a binary pattern. It has proven to be a powerful and effective descriptor for texture analysis. A parallel implementation of LNDP using Compute Unified Device Architecture (CUDA) has been proposed in this paper. A speedup of about 1000 times has been achieved through a shared memory parallel implementation for large images. Thus, an efficacious and efficient implementation has resulted in an increased execution speed and reduced execution time.",

author = "{Sree Ashish}, Arisetty and {Ashwath Rao}, B.",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022 ; Conference date: 28-12-2022 Through 29-12-2022",

year = "2022",

doi = "10.1109/ICKECS56523.2022.10060766",

language = "English",

series = "IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Sree Ashish, A & Ashwath Rao, B 2022, Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU. in IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022. IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022, Chickballapur, India, 28-12-22. https://doi.org/10.1109/ICKECS56523.2022.10060766

Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU. / Sree Ashish, Arisetty; Ashwath Rao, B.
IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU

AU - Sree Ashish, Arisetty

AU - Ashwath Rao, B.

PY - 2022

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N2 - One of the various techniques employed for image feature extraction is the Local Neighborhood Difference Pattern, also called as LNDP. LNDP considers the relationship between neighbors of a central pixel with its adjacent pixels and transforms this mutual relationship of all the neighboring pixels into a binary pattern. It has proven to be a powerful and effective descriptor for texture analysis. A parallel implementation of LNDP using Compute Unified Device Architecture (CUDA) has been proposed in this paper. A speedup of about 1000 times has been achieved through a shared memory parallel implementation for large images. Thus, an efficacious and efficient implementation has resulted in an increased execution speed and reduced execution time.

AB - One of the various techniques employed for image feature extraction is the Local Neighborhood Difference Pattern, also called as LNDP. LNDP considers the relationship between neighbors of a central pixel with its adjacent pixels and transforms this mutual relationship of all the neighboring pixels into a binary pattern. It has proven to be a powerful and effective descriptor for texture analysis. A parallel implementation of LNDP using Compute Unified Device Architecture (CUDA) has been proposed in this paper. A speedup of about 1000 times has been achieved through a shared memory parallel implementation for large images. Thus, an efficacious and efficient implementation has resulted in an increased execution speed and reduced execution time.

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M3 - Conference contribution

AN - SCOPUS:85150683417

T3 - IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022

BT - IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022

PB - Institute of Electrical and Electronics Engineers Inc.

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Sree Ashish A, Ashwath Rao B. Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU. In IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022). doi: 10.1109/ICKECS56523.2022.10060766

Parallelization of Local Neighborhood Difference Pattern Feature Extraction using GPU

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