SAR image segmentation based on Artificial Bee Colony algorithm

Ma, Miao; Liang, Jingqiu; Guo, Min; Fan, Yi; Yin, Yilong

doi:10.1016/j.asoc.2011.05.039

Cited by 164 publications

(67 citation statements)

References 15 publications

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“…The ABC has also been effectively applied to address challenging optimization problems [21]. However, this approach has a setback in terms of their neighborhood search, where it randomly generates new food sources.…”

Section: From the Point Of Image Processing Viewmentioning

confidence: 99%

2.5 D Facial Analysis via Bio-Inspired Active Appearance Model and Support Vector Machine for Forensic Application

Abdullah¹,

Abdulameer²,

Zamani³

et al. 2017

ijacsa

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Abstract-In this paper, a fully automatic 2.5D facial technique for forensic applications is presented. Feature extraction and classification are fundamental processes in any face identification technique. Two methods for feature extraction and classification are proposed in this paper subsequently. Active Appearance Model (AAM) is one of the familiar feature extraction methods but it has weaknesses in its fitting process. Artificial bee colony (ABC) is a fitting solution due to its fast search ability. However, it has drawback in its neighborhood search. On the other hand, PSO-SVM is one of the most recent classification approaches. However, its performance is weakened by the usage of random values for calculating velocity. To solve the problems, this research is conducted in three phases as follows: the first phase is to propose Maximum Resource Neighborhood Search (MRNS) which is an enhanced ABC algorithm to improve the fitting process in current AAM. Then, Adaptively Accelerated PSO-SVM (AAPSO-SVM) classification technique is proposed, by which the selection of the acceleration coefficient values is done using particle fitness values in finding the optimal parameters of SVM. The proposed methods AAM-MRNS, AAPSO-SVM and the whole 2.5D facial technique are evaluated by comparing them with the other methods using new 2.5D face image data set. Further, a sample of Malaysian criminal real case of CCTV facial investigation suspect has been tested in the proposed technique. Results from the experiment shows that the proposed techniques outperformed the conventional techniques. Furthermore, the 2.5D facial technique is able to recognize a sample of Malaysian criminal case called "Tepuk Bahu" using CCTV facial investigation.

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Section: From the Point Of Image Processing Viewmentioning

confidence: 99%

2.5 D Facial Analysis via Bio-Inspired Active Appearance Model and Support Vector Machine for Forensic Application

Abdullah¹,

Abdulameer²,

Zamani³

et al. 2017

ijacsa

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“…Miao Ma et al [14] proposed a rapid SAR segmentation method for images that relied on ABC algorithm. The image was firstly segmented using DWT.…”

Section: Fig4: Steganalysismentioning

confidence: 99%

A Review on Image Steganography Techniques

Kaur¹,

Kaur²,

Kumar³

2015

IJCA

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The emerging Internet Technology has led to the need of high level of security of data during its transmission. For this purpose steganography plays a major role in society. Steganography is basically the art of secretly hiding data or message in any cover medium such as an image, audio or video. Hence it allows secret communication to take place without the knowledge of any unintended user. This article gives a brief overview of image steganography and the techniques used for hiding data in the cover image to obtain stego image. The evaluation of performance is based on the value of PSNR.

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“…Segmentation is considered as very essential role in analysis many applications such as Synthetic Aperture Radar (SAR) image. Commonly, segmentation of SAR images classify into two classes; one based on gray levels and the other based on texture [2,3]. The main obstacle that faced the segmentation of (SAR) images is the speckle noise [2,3].…”

Section: Introductionmentioning

confidence: 99%