A novel approach for detection and delineation of cell nuclei using feature similarity index measure

2016 ◽  
Vol 36 (1) ◽  
pp. 76-88 ◽  
Author(s):  
Jisha John ◽  
Madhu S. Nair ◽  
P.R. Anil Kumar ◽  
M. Wilscy
Keyword(s):  
Similarity Index ◽  
Cell Nuclei ◽  
Novel Approach ◽  
Feature Similarity ◽  
Feature Similarity Index ◽  
Index Measure

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

IMAGE SEQUENCE PREDICTION USING ANN AND RBFNN

2013 ◽  
Vol 13 (02) ◽  
pp. 1340006 ◽  
Author(s):  
NISHCHAL K. VERMA ◽  
SHIKHA SINGH
Keyword(s):  
Neural Network ◽  
Similarity Index ◽  
Image Sequence ◽  
Image Frame ◽  
Novel Approach ◽  
Canny Edge ◽  
Mean Structure ◽  
Structure Similarity Index ◽  
Artificial Neural Network Ann ◽  
Index Measure

A novel approach to predict future image frame of an image sequence is being presented. First, a method to predict the future position of a moving object in an image sequence is discussed using artificial neural network (ANN). Second, optical flow concept is used for generating complete image frame by calculating velocity of each pixel on both axes. A separate ANN (both sigmoidal and radial basis function neural network) is modeled for each pixel's velocity and predicted velocity of each pixel is then mapped to its future values and image frames are generated. The quality evaluations of predicted images are measured by Canny edge detection-based image comparison metric (CIM) and mean structure similarity index measure (MSSIM). These proposed approaches are found to generate future images up to 10 images successfully.

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).

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

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