Boundary & Geometric Region Features Image Segmentation for Quadtree Partitioning Scheme

Zainab Al-Rubaye

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Publication Date

Tue Jan 01 2013

Journal Name

International Journal Of Advanced Research In Computer Science And Software Engineering

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Boundary & Geometric Region Features Image Segmentation for Quadtree Partitioning Scheme

Image segmentation

boundary & region segmentation

quadtree partitioning scheme

Zainab Al-Rubaye

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In this paper, an efficient image segmentation scheme is proposed of boundary based & geometric region features as an alternative way of utilizing statistical base only. The test results vary according to partitioning control parameters values and image details or characteristics, with preserving the segmented image edges.

Publication Date

Thu Feb 01 2018

Journal Name

Journal Of Economics And Administrative Sciences

Estimation of parameters of two-dimensional sinusoidal signal model by employing Deferential Evaluation algorithm and the use of Sequential approach in estimation

Two dimension sinusoidal signal model

sequential Deferential Evaluation estimators method

Symmetric gray scale texture image.

ظافر حسين

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Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling the Symmetric gray scale texture image and estimating by using

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Publication Date

Mon Oct 13 2025

Journal Name

Mesopotamian Journal Of Cybersecurity

Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network

Face Recognition Systems (FRS)

Siamese Neural Network (SNN)

Faster Region-based Convolutional Neural Networks (R-CNN)

Local Binary Pattern-Convolutional Neural Network (LBP-CNN)

Deep Learning

Morphing Attacks

Sura

Thair

Nada

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Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

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