The measurement accuracy of a phase-shifting measurement system is adversely affected by phase errors. This paper presents a theoretical analysis of phase errors caused by nonuniform surface reflectivity, such as varying reflectivity and a sharp change in reflectivity. Based on the analysis, a method to adaptively adjust the maximum input gray level of each pixel in projected fringe patterns to the local reflectivity was proposed to reduce phase errors. Experimental results for a planar checkerboard show that the measurement error can be reduced by 56.6% by using the proposed method.
In this paper, a new method is presented to match a pair of visible and infrared images of same scene based on hybrid visual features including line segments and interest points. First, improved Harris corner extraction method and line segment detector method is used to extract feature points and segments. Then, a novel descriptor integrating the information of line segments and interest points is proposed. Finally, the nearest neighbor algorithm is utilized to match the descriptors, and the RANSAC(Random Sample Consensus) algorithm is employed to rule out the wrong match pairs. The performances are evaluated by extensive experiments on real images. The results show that the proposed algorithm can take advantage of similar structures between the multimodal images to realize automatic matching efficiently.
This paper presents a new feature matching algorithm for nonrigid multimodal image registration. The proposed
algorithm first constructs phase congruency representations (PCR) of images to be registered. Then scale invariant
feature transform (SIFT) method is applied to capture significant feature points from PCR. Subsequently, the putative
matching is obtained by the nearest neighbour matching in the SIFT descriptor space. The SIFT descriptor is then
integrated into Coherent Point Drift (CPD) method so that the appropriate matching of two point sets is solved by
combining appearance with distance properties between putative match candidates. Finally, the transformation estimated
by matching the point sets is applied to registration of original images. The results show that the proposed algorithm
increases the correct rate of matching and is well suited for multi-modal image registration.
This paper presents a simple and computationally efficient saliency extraction method for detecting dim small target
from single frame in heterogeneous background. The proposed method is based on background subtraction (BS), which
identifies targets from the portion of a image that differs significantly from a background model. A set of horizon-directional
filters (HDF) with multi-scales are first implemented to effectively recover the background maps from the
input image. As a result, the foreground maps are extracted by computing the absolute difference between the input
image and the estimated background maps. Then the foreground maps are fused into the total saliency map using a
simple scheme. Finally, the experimental results of various cluttered background images show that the proposed method
is efficient and has an outstanding performance in dim small target detection just by thresholding the saliency map.
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