Considering the change of optical information on material surface, this paper proposes a useful notion called pixel-level
optical constants (POC). Through Fresnel equations, traditional optical constants (refractive index n and extinction
coefficient k ) can reflect photoelectric characteristics on material surface. Combining with Mueller calculus in
polarization optics, POC can describe the distrbution of photoelectric characteristics on material surface. POC is mainly
calculated by the decomposition of Mueller matrix which includes Fresnel amplitude, ratio of two orthogonal reflection
coefficient component P and variation of phase difference between incident light and reflected light Δ . With the
regularity of polarized light and the statistics of Mueller matrices, optical characteristics can be detailed to each pixel in
POC, which will independently show the distribution of polarization characteristics on material surface. And it can also be
approximately averaged to obtain traditional optical constants. So POC is significant to optical researches on material
We propose a three-view constraint for the motion object detection using moving camera. The proposed method classifies feature points in the video sequence into background or motion object by applying the epipolar constraint and a novel geometric constraint called the “Three-view Distance Constraint”. The three-view distance constraint, being the main contribution of this paper, is derived from the relative camera poses in three different views and implemented within the detection framework. Unlike the epipolar constraint, the three-view distance constraint modifies the surface degradation to the line degradation. The three-view distance constraint is capable of detecting moving objects followed by a moving camera in the same direction . We evaluate the proposed method with several video sequences to demonstrate the effectiveness and robustness of the three-view distance constraint.
In this paper, we propose to obtain the optical characteristics on material surface by Mueller calculus. In our research, a new metric for Mueller matrices, named R(M) , is defined to describe the polarization and depolarization characteristics on material surface by analyzing the constitute of Mueller matrices. The definition of R(M) is derived from the definition of the depolarization scalar metric for Mueller matrices named Q (M ) which can show the diattenuation and depolarization characteristics. With the advantage of Q (M ) , we assumed and proved the advantage of R(M) against the traditional metrics, the polarizance parameter P(M) and the depolarization index DI (M ) . This comparison can fully illustrate the value of R(M) . It is considered that P(M) and DI (M ) which cannot analyze the optical characteristics commonly to give a comprehensive evaluation. However, composed of P(M) and DI (M ) , R(M) can comprehensively reflect the optical signification which P(M) and DI (M ) represent. R(M) can be used to analyze different optical polarized characteristics on material surface with five bounds as totally depolarizing, partially depolarizing, totally polarizing, partially polarizing, nondepolarizing nonpolarizing. This means that R(M) can enable us to distinguish different materials by their different polarized characteristic on surface. With the definition of R(M) , it can be known that how the optical polarized characteristics work to change the polarized state of incident light on material surface.
The purpose of infrared polarization image is to highlight man-made target from a complex natural background. For
the infrared polarization images can significantly distinguish target from background with different features, this paper
presents a wavelet-based infrared polarization image fusion algorithm. The method is mainly for image processing of
high-frequency signal portion, as for the low frequency signal, the original weighted average method has been applied.
High-frequency part is processed as follows: first, the source image of the high frequency information has been extracted
by way of wavelet transform, then signal strength of 3*3 window area has been calculated, making the regional signal
intensity ration of source image as a matching measurement. Extraction method and decision mode of the details are
determined by the decision making module. Image fusion effect is closely related to the setting threshold of decision
making module. Compared to the commonly used experiment way, quadratic interpolation optimization algorithm is
proposed in this paper to obtain threshold. Set the endpoints and midpoint of the threshold searching interval as initial
interpolation nodes, and compute the minimum quadratic interpolation function. The best threshold can be obtained by
comparing the minimum quadratic interpolation function. A series of image quality evaluation results show this method
has got improvement in fusion effect; moreover, it is not only effective for some individual image, but also for a large
number of images.