This paper proposed an unsupervised change detection method for water body extraction and change detection with
multi-temporal SAR images. Firstly, two optimal thresholds are estimated according to the strategy of maximum mutual
information, in which computation efficiency is largely improved based on integral image. Secondly, water body
extraction is done simultaneously in both input images by optimal thresholds. Finally, by fusing of two segmented
results, change detection can be achieved. Experimental results demonstrate the effectiveness of the proposed approach.
In this paper, we investigate object tracking in video sequences and propose a particle filter based tracking algorithm
with color and texture information fusion, in which the target model is jointly represented by spatial-weighted color
histogram and LBP (Local Binary Patterns) texture histogram. The property of local grayscale or color invariance for
LBP operator makes it more reliable to measure the spatial structure of local image texture. The system is less sensitive
to illumination changes and partial occlusions, and can be able to track objects in diverse conditions. Experimental
results demonstrate that the performance of the proposed method is more robust and accurate than the original color
based method, especially when tracking objects with similar color appearance to the background and partial occlusions.