scholarly journals Automatic Personal Identification Using Feature Similarity Index Matching

2012 ◽  
Vol 9 (5) ◽  
pp. 678-685
Author(s):  
Alaa
Keyword(s):  
Similarity Index ◽  
Personal Identification ◽  
Index Matching ◽  
Feature Similarity ◽  
Feature Similarity Index

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

2018 ◽  
Vol 5 (2) ◽  
pp. 69-94
Author(s):  
K R Chetan ◽  
S Nirmala
Keyword(s):  
Digital Images ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Input Image ◽  
Tamper Detection ◽  
Fragile Watermarking ◽  
Feature Similarity ◽  
Feature Similarity Index ◽  
Index Value ◽  
Watermarking Scheme

A novel adaptive semi-fragile watermarking scheme for tamper detection and recovery of digital images is proposed in this paper. This scheme involves embedding of content and chroma watermarks generated from the first level Discrete Curvelet Transform (DCLT) coarse coefficients. Embedding is performed by quantizing the first level coarse DCLT coefficients of the input image and amount of quantization is intelligently decided based on the energy contribution of the coefficients. During watermark extraction, a tampered matrix is generated by comparing the feature similarity index value between each block of extracted and generated watermarks. The tampered objects are subsequently identified and an intelligent report is formed based on their severity classes. The recovery of the tampered objects is performed using the generated DCLT coefficients from luminance and chrominance components of the watermarked image. Results reveal that the proposed method outperforms existing method in terms of tamper detection and recovery of digital images.

scholarly journals FSITM: A Feature Similarity Index For Tone-Mapped Images

2015 ◽  
Vol 22 (8) ◽  
pp. 1026-1029 ◽  
Author(s):  
Hossein Ziaei Nafchi ◽  
Atena Shahkolaei ◽  
Reza Farrahi Moghaddam ◽  
Mohamed Cheriet
Keyword(s):  
Similarity Index ◽  
Feature Similarity ◽  
Feature Similarity Index

scholarly journals Review on Image Recoloring Methods for Efficient Naturalness by Coloring Data Modeling Methods for Low Visual Deficiency

2021 ◽  
Vol 3 (3) ◽  
pp. 169-183
Author(s):  
A. Pasumpon Pandian
Keyword(s):  
Performance Measures ◽  
Research Study ◽  
Similarity Index ◽  
Learning Method ◽  
Modeling Methods ◽  
Learning Techniques ◽  
Feature Similarity ◽  
Colour Distribution ◽  
Feature Similarity Index ◽  
New Applications

Recent research has discovered new applications for object tracking and identification by simulating the colour distribution of a homogeneous region. The colour distribution of an object is resilient when it is subjected to partial occlusion, scaling, and distortion. When rotated in depth, it may remain relatively stable in other applications. The challenging task in image recoloring is the identification of the dichromatic color appearance, which is remaining as a significant requirement in many recoloring imaging sectors. This research study provides three different vision descriptions for image recoloring methods, each with its own unique twist. The descriptions of protanopia, deuteranopia, and tritanopia may be incorporated and evaluated using parametric, machine learning, and reinforcement learning techniques, among others. Through the use of different image recoloring techniques, it has been shown that the supervised learning method outperforms other conventional methods based on performance measures such as naturalness index and feature similarity index (FSIM).

Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

2020 ◽  
Vol 11 (2) ◽  
pp. 31-61
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Symbiotic Relationship ◽  
Symbiotic Organisms Search ◽  
Image Thresholding ◽  
Prime Concern ◽  
Feature Similarity Index ◽  
Symbiotic Organisms

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.

Elephant Herding Optimization for Multi-Level Image Thresholding

2020 ◽  
Vol 11 (4) ◽  
pp. 64-90
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Cuckoo Search ◽  
Threshold Value ◽  
Objective Functions ◽  
Image Thresholding ◽  
Bee Colony ◽  
Kapur’S Entropy ◽  
Feature Similarity Index

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

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