Histogram matching attack on selective perceptual video encryptions in H.264/AVC

2012 ◽  
Author(s):  
Jay M. Joshi ◽  
Upena D. Dalal
Keyword(s):  
Histogram Matching

Retinal vessel segmentation using histogram matching

2008 ◽  
Author(s):  
Giri Babu Kande ◽  
T.Satya Savithri ◽  
P.V. Subbaiah
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Histogram Matching ◽  
Retinal Vessel Segmentation

scholarly journals A New Modified Histogram Matching Normalization for Time Series Microarray Analysis

Microarrays ◽  
2014 ◽  
Vol 3 (3) ◽  
pp. 203-211
Author(s):  
Laura Astola ◽  
Jaap Molenaar
Keyword(s):  
Time Series ◽  
Microarray Analysis ◽  
Histogram Matching ◽  
Time Series Microarray

Down-scaling SRTM slope based on histogram matching and slope distribution model

2016 ◽  
Author(s):  
Chunmei Wang ◽  
Qinke Yang ◽  
Jiaxin Wen ◽  
David L.B. Jupp
Keyword(s):  
Distribution Model ◽  
Histogram Matching

scholarly journals An Efficient PET-MRI Medical Image Fusion based on IHS-NSCT-PCA Integrated Method

2019 ◽  
Vol 9 (2) ◽  
pp. 2073-2079
Keyword(s):  
Structural Data ◽  
Fusion Rule ◽  
Integrated Approach ◽  
Disease Diagnosis ◽  
Weighted Averaging ◽  
Fusion Algorithm ◽  
Integrated Method ◽  
Histogram Matching ◽  
Multiple Imaging ◽  
Fused Image

Merging of multiple imaging modalities leads to a single image that acquire high information content. These find useful applications in disease diagnosis and treatment planning. IHS-PCA method is a spatial domain approach for fusion that offersfinestvisibility but demands vast memory and it lacks steering information. We propose an integrated approach that incorporates NSCT combined with PCA utilizing IHS space and histogram matching. The fusion algorithm is applied on MRI with PET image and improved functional property was obtained. The IHS transform is a sharpening technique that converts multispectral image from RGB channels to Intensity Hue and Saturation independent values. Histogram matching is performed with intensity values of the two input images. Pathological details in images can be emphasized in multi-scale and multi-directions by using PCA withNSCT. Fusion rule applied is weighted averaging andprincipal components are used for dimensionality reduction. Inverse NSCT and Inverse IHS are performed so as to obtain the fused image in new RGB space. Visual and subjective investigation is compared with existing methods which demonstrate that our proposed technique gives high structural data content with high spatial and spectral resolution compared withearlier methods.

scholarly journals Survei Penelitian Pengenalan Pola dalam Identifikasi Biometrik

2017 ◽  
Vol 7 (1) ◽  
pp. 19
Author(s):  
Regina Lionnie ◽  
Mudrik Alaydrus
Keyword(s):  
Local Binary Pattern ◽  
Image Alignment ◽  
Chi Square ◽  
Histogram Matching ◽  
Gabor Features ◽  
Averaging Filter

Pengenalan pola memainkan peranan yang penting dalam identifikasibiometrik. Hal ini dikarenakan pengenalan pola dalam identifikasibiometrik membantu pihak berwenang dalam mengungkap identitasseorang kriminal. Pengenalan pola identifikasi biometrik dalam imageprocessing mencakup pengenalan pola wajah, geometri dari sebuahtangan, iris dan retina dari organ mata, sklera mata, pembuluh darah,tanda kulit dan rambut tubuh. Pengenalan pola identifikasi biometrikmembutuhkan metode pengenalan pola yang akurat, pemilihan tahap praproses dan metode klasifikasi yang sesuai. Pada survei paper ini dibahasmengenai beberapa metode tahap pra proses seperti Averaging Filter,Histogram, Desaturation, Binerisation dan Image Alignment. Metodepengenalan pola yang dibahas pada paper ini adalah Gabor Features,Local Binary Pattern, Local Gabor Binary Pattern dan Haar WaveletTransform. Sedangkan metode klasifikasi yang dibahas adalah Euclideandistance, Chi-square distance dan Histogram Matching. Agar dapatmemberikan hasil terbaik, setiap sistem pengenalan pola tidak dapatmenggunakan metode yang sama untuk mengenali pola identifikasibiometrik yang berbeda. Dibutuhkan penelitian dalam penggunaanmetode pra proses, ekstraksi fitur dan klasifikasi untuk setiap identifikasibiometrik yang ingin dikenali polanya.

Adaptive image enhancement based on NSCT coefficient histogram matching

2014 ◽  
Vol 22 (8) ◽  
pp. 2214-2222 ◽  
Author(s):  
周妍 ZHOU Yan ◽  
李庆武 LI Qing-wu ◽  
霍冠英 HUO Guan-ying
Keyword(s):  
Image Enhancement ◽  
Histogram Matching
Sign in / Sign up

Export Citation Format

Share Document