The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a random perturbation in the observation system, the QIKF for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. Thus, the estimate error covariance matrix of QIKF can be more exactly analyzed. The boundedness of the estimate error covariance matrix of QIKF is obtained under some weak conditions. The design of the number of quantized levels is discussed to guarantee the stability of QIKF. To overcome the instability and divergence of QIKF when the number of quantization levels is small, we propose a Kalman filter using scaling quantized innovations. Numerical simulations show the validity of the theorems and algorithms.
Harris corner detector is a classic tool to extract feature.It is stable to illumination change and rotation but unstable to more complicated transform.In order to register images with different viewpoints,we extend Harris corner detector to scale-space to gain invariance to scale change,then we apply affine shape adaptation to the scale invariant point until convergence is reached,giving it invariance to affine transform.With these local features,we use general feature descriptor and matching algorithm to generate matches and then use the matches to calculate the geometric transform matrix,which enables the final registration.Result shows that our algorithm can get more accurate matches than scale invariant feature transform SIFT,and less difference exists between registered images.
