Region of interest (ROI) coding is important in applications where certain parts of an image are of a
higher importance than the rest of the image. Human vision system actively seeks interesting regions in images to reduce
the search export in tasks, such as object detection and recognition. Similarly, prominent actions in video sequences are
more likely to attract human's first sight than their surrounding neighbors. Based on the mechanism of HVS, this paper
proposes a model of the focus of attention for detecting the attended regions in video sequences. It uses the similarity
between the adjacent frames, establishes the gray histogram, selects the maximum similarity as predicable model, and gets
position of the focus of attention in the next fame. And on the application of an algorithm for visual attention the paper
shows the region of interest (ROI) coding in JPEG 2000. JPEG 2000 ROI coding is used in combination with an
algorithm for VA to provide a progressive bit-stream where the regions highlighted by the VA algorithm are coded as an
ROI and presented first in the bit-stream. It can be seen that there is an improvement in image quality centered on the
ROI although this is achieved at the expense of reduced quality in the background of the image.
ROI(Region of Interest) extraction plays an important role in the field of infrared image signal processing. How to make
the compressed image preserve certain auto target recognition performance is an important problem. In order to reduce
storage space and lower transmission time, this paper proposed a new algorithm to extract ROI of sequence image.
Different from traditional region of interest extraction algorithms, the algorithm extracts region of interest based on a
dynamic background modeling approach. Aiming at the slow learning rate of traditional mixture Gaussian model(GMM)
this paper proposed a moving object detection algorithm . First, the mixed Gaussian model was constructed , and a new
way was adopted to update background which utilized different equations at different phases. With the development of
adaptive background update and the adaptive learning rate which always has been improved on the application matures.
The validity of the proposed approach is demonstrated on the infrared image ROI extraction. Experimental results show
that the approach is efficient both in computational cost and segmentation quality. On the other hand the improved
algorithm solves the hole and hollow when the moving object is short of sufficient surface texture by symmetrical