A superior Support Vector Machine digital watermarking for color image

2012 ◽  
Author(s):  
Saurabh Tiwari ◽  
Ashish Dongre
Keyword(s):  
Support Vector Machine ◽  
Digital Watermarking ◽  
Color Image ◽  
Support Vector

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

scholarly journals Identificação de embarcações em imagens aerotransportadas de radar de abertura sintética (R-99 SAR) na área marítima do Brasil

2011 ◽  
Vol 17 (3) ◽  
pp. 458-475 ◽  
Author(s):  
Sérgio Roberto Horst Gamba ◽  
Edson Eyji Sano ◽  
Marcelo Peres Rocha
Keyword(s):  
Support Vector Machine ◽  
Color Image ◽  
Support Vector

O objetivo deste trabalho foi identificar embarcações em imagens de radar obtidas pela aeronave R-99 da Força Aérea Brasileira. Dados de amplitude, obtidas na banda L e nas polarizações HH, HV, VH e VV da região de Porto de Tubarão, ES, foram processados por meio de diferentes tipos de realces, filtros, classificadores e transformadores espectrais. As imagens com maior potencial para identificar embarcações foram ainda analisadas para diferenciar embarcações militares de mercantes, considerando-se os cinco elementos de interpretação (forma, tamanho, sombra, tonalidade e fatores associados, isto é, o contexto em que as embarcações se encontram nas imagens) e as cinco fases de interpretação de imagens (detecção, reconhecimento, análise, dedução e classificação). A combinação de processamentos mais favoráveis foi o realce com contraste 50-200, seguido de filtro abertura ou erosão e classificador SVM (Support Vector Machine) ou transformação SCI (Synthetic Color Image). Foi possível discriminar embarcações nas fases de detecção e reconhecimento, enquanto a diferenciação entre embarcações mercantes e militares foi obtida nas fases de análise e dedução. No nível de classificação, não foi possível definir o tipo de embarcação militar (e.g., fragata ou contratorpedeiro) ou o tipo de embarcação mercante (e.g., petroleiro ou graneleiro).

New mean shift tracking for color image based on online support vector machine

2014 ◽  
Vol 29 (1) ◽  
pp. 120-128
Author(s):  
郭敬明 GUO Jing-ming ◽  
何昕 HE Xin ◽  
魏仲慧 WEI Zhong-hui
Keyword(s):  
Support Vector Machine ◽  
Color Image ◽  
Mean Shift ◽  
Support Vector ◽  
Online Support

scholarly journals Support Vector Machine (SVM) for Colorization the Grayscale Image

2020 ◽  
Vol 13 (3) ◽  
pp. 207-214
Author(s):  
Shymaa Akram Alrubaie ◽  
Israa Mohammed Hassoon
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Mean Squared Error ◽  
Color Image ◽  
Related Result ◽  
Support Vector ◽  
Source Image ◽  
Grayscale Image ◽  
Color System ◽  
Grayscale Images

Recently, there have been several automatic approaches to color grayscale images, which depend on the internal features of the grayscale images.  There are several scales which are considered as a prominent key to extract the corresponding chromatic value of the gray level. In this aspect, colorizing methods that rely on automatic algorithms are still under investigation, especially after the development of neural networks used to recognize the features of images. This paper develops a new model to obtain a color image from an original grayscale image through the use of the Support Vector Machine to recognize the features of grayscale images which are extracted from two stages: the first stage is   Haar Discrete Wavelets Transform used to configure the vector that combines with six of Statistical Measurements: (Mean, Variance, Skewness, Kurtosis, Energy and Standard Deviation) extracts from the grayscales image in the second stage. After the Support Vector Machine recognition has been done, the colorization process uses the result of Support Vector Machine to recover the color to greyscale images by using YCbCr color system then it converts the color to default color system (RGB) to be more clear. The proposed model will be able to move away from relying on the user to identify the source image which matches in color levels and it exceeds the network determinants of image types with similar color levels. In addition, Support Vector Machine is considered to be more reliable than neural networks in classification algorithms. The model performance is evaluated by using the Root Mean Squared Error (RMSE) in measuring the success of the assumed modal of matching the coloring (resulting) images and the original color images. So, a reality-related result has been obtained at a good rate for all the tested images. This model has proved to be successful in the process of recognizing the chromatic values of greyscale images then retrieving it. It takes less time complexity in trained data, and it isn’t complex in working.

Sign in / Sign up

Export Citation Format

Share Document