Periodicity: Bi Annual.
Impact Factor:
SJIF:4.78 & GIF:0.428
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJWT provides online manuscript tracking system.

Every issue of Journal of IJWT is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJWT special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJWT website. For complete procedure, contact us at

Paper Template
Copyright Form
Subscription Form
web counter
web counter
Published in:   Vol. 5 Issue 1 Date of Publication:   June 2016

Change Detection using Spatio-Temporal Features and Feedback Loops

K.Poornima,V. Mohan

Page(s):   65-68 ISSN:   2278-2397
DOI:   10.20894/IJWT. Publisher:   Integrated Intelligent Research (IIR)

Change detection is one of the most commonly used tasks in video processing. Background subtraction based change detection is the first step in many video applications to detect the foreground objects. Most of the background subtraction methods such as Local Binary patterns (LBP) and Local Ternary Patterns (LTP) are implemented using pixel by pixel representation. These can be viewed as improvements in many cases but they suffer in complexity, processing speed and illumination variations. In this paper, Local Binary Similarity Patterns (LBSP) makes pixel level decisions with automatic adjustment of tuning parameters for locally adapting to the changing input. It assigns the binary codes based on the similarity. For dynamic texture analysis, each pixel is modelled as a group of Spatiotemporal LBP (STLBP) histograms which combine spatial texture and temporal motion information together. The feedback loops are used to adjust the internal parameters and these adjustments are based on the continuous monitoring of model fidelity and segmentation noise which is observed under the form of blinking pixels. Adaptive background subtraction methods address all types of real time challenges including sudden illumination variations, background movements, shadows and ghost artifacts (falsely classified background regions).