Feature description and matching are at the base of many computer vision applications. However, traditional local descriptors cannot fully describe all information of features, and there are so many feature points and so long local descriptors that the matching steps are time-consuming. In order to solve these problems. This paper proposed a new efficient method for description and matching, called TSMwGLD (the two-step matching with global and local Descriptors). In TSMwGLD, first, it designed a simple global descriptor and then found N best-matching points by using global descriptors, and at the same time it could eliminate lots of points which didn’t match in global information. Next, the method continued the matching step to find the best-matching point by using the local descriptors of N candidate points. So the whole matching process could become faster because the distances between global descriptors with the size of 8 were computed more easily than local descriptors with the size of 64 in SURF. The experimental results show that TSMwGLD results in increased accuracy and faster matching than original method. Especially for blurred images with textures, the matching time is less than tenth of original and the whole description and matching process is about two times faster than SURF.
To detect IWST, foreign and domestic scholars put forward many meaningful detection methods. However, most of the
algorithms are too much complex in the calculation to meet real-time and reliability requirements in practical application.
A simple self-adaptive threshold algorithm to capture and track the IWST is presented in this paper. Testing results
showed that algorithm not only effectively extract IWST and track it through the clouds in the sky background, but also
has a strong robustness for the interference of background noises.
In order to save the computational time of and apply in engineering practice well, a lot of correlation matching
algorithms have been proposed at present. A practical effective method based on small diamond search template is
presented in this paper. By comparing with the classical algorithm based on the large diamond, it is very pleased with the
results. The algorithm presented not only reduced the computational complexity and improved the computational speed,
but also ensured the search precision of correlation matching. It is fully able to satisfy the requirement of real-time video
tracking, and be pleased greatly with the accuracy and reliability of the real-time video tracking.
Auto white balance plays a key role in a digital camera system, and determines image quality to a large extent. If the
white balance is not to be considered in the development of CCD camera, under the different color temperatures, this will
cause chromatic aberration. A new effective automatic white balance algorithm for digital camera is proposed in this
paper. With a new color temperature estimation method based on the extraction both skin and white regions, the
algorithm can find more proper pixels to calculate the averaged chromatic aberration to improve the precision of the
estimated color temperature. And to some extent, the algorithm solves the problem that the classical automatic white
balance algorithm fails in estimating color temperature in the past in the case of the images have not white regions.