Image Tracking and Analysis Algorithm by Independent Component Analysis

2010 ◽  
Vol 44-47 ◽  
pp. 1622-1627
Author(s):  
Cheng Yi Yu ◽  
Yi Ying Chang ◽  
Yen Chieh Ouyang ◽  
Shen Chuan Tai ◽  
Tzu Wei Yu
Keyword(s):  
Independent Component Analysis ◽  
Natural Environment ◽  
Color Space ◽  
Component Analysis ◽  
Brightness Distribution ◽  
Target Object ◽  
Independent Component ◽  
Image Capture ◽  
Main Component Analysis ◽  
Main Component

Along with digitizing and multimedia era, the image has not changed from the original entity into any changes can be dealt with digital preservation methods. Although the digital image capture technology means more and more developed, but there are still many variables affect the quality of an image. An image quality usually depends on the user's usage or changes in the natural environment. Due to the natural environment of the most common factors that influence is light, so an image of the brightness distribution over the target object caused by extreme hardly recognizable condition common. Therefore, we will use the independent component analysis of an input color images Red, Green, and Blue three Color Space to the main component analysis, in order to achieve the target tracking and analysis.

Classification of Remotely Sensed Images Using Independent Component Analysis and Spatial Consistency

2004 ◽  
Vol 8 (2) ◽  
pp. 216-222 ◽  
Author(s):  
Xiang-Yan Zeng ◽  
◽  
Yen-Wei Chen ◽  
Zensho Nakao ◽  
Keyword(s):  
Independent Component Analysis ◽  
Color Space ◽  
Component Analysis ◽  
Remotely Sensed ◽  
Classification Performance ◽  
Independent Component ◽  
Color Representation ◽  
Spatial Consistency ◽  
Remotely Sensed Images

We apply independent component analysis (ICA) to learn efficient color representation of remotely sensed images. Among the three basis functions obtained from RGB color space, two are in an opposing-color model by which the responses of R, G and B cones are combined in opposing fashions. This is coincident with the idea of contrasting reflected in many color systems. The interesting point is that there is no summation component that corresponds to illumination in other transforms. Spectral independent components are then used to cluster pixels. After pixel-based classification, we segment an image on the basis of regions by spatial consistency. Experimental results show that this method considerably improves the classification performance of multispectral remotely sensed images.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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