Many existing bottom-up saliency detection methods measure the multi-scale local prominence by building the Gaussian scale space. As a kind of linear scale space, it is a natural representation of human perception. However the Gaussian filtering does not respect the boundaries of proto-objects and smooth both noises and details. In this paper, we compute the pixel level center-surround difference in a nonlinear scale space which makes blurring locally adaptive to the image regions. The nonlinear scale space is built by a efficient evolution techniques and extended to represent color images. In contrast to some widely used region-based measures, we represent feature statistics by multivariate normal distributions and compare them with the Wasserstein distance on <i>l</i>2 norm (<i>W</i><sub>2</sub> distance). From the perspective of visual organization in imaging, many priors are proved to be efficient in global consideration. In order to further precisely locate the proper salient object, we also use the background prior as a global cue to refine the obtained local saliency map. The experimental results show that our approach outperforms 5 recent state of the art saliency detection methods in terms of precision and recall on a newly published benchmark.
In planetary or lunar landing missions, hazard avoidance is critical for landing safety. Therefore, it is very important to correctly detect hazards and effectively find a safe landing area during the last stage of descent.
In this paper, we propose a passive sensing based HDA (hazard detection and avoidance) approach via descent images to lower the landing risk. In hazard detection stage, a statistical probability model on the basis of the hazard similarity is adopted to evaluate the image and detect hazardous areas, so that a binary hazard image can be generated. Afterwards, a safety coefficient, which jointly utilized the proportion of hazards in the local region and the inside hazard distribution, is proposed to find potential regions with less hazards in the binary hazard image. By using the safety coefficient in a coarse-to-fine procedure and combining it with the local ISD (intensity standard deviation) measure, the safe landing area is determined. The algorithm is evaluated and verified with many simulated descent downward looking images rendered from lunar orbital satellite images.
Frost is a kind of ground coagulation phenomena, and if the temperature of dew point is below 0C<sup>o</sup> , the water vapor
condenses as solid, which is called frost. The frost phenomena observing is an important step in daily ground observation
work, and the results is one of 36 critical data in meteorological observation field. This work is usually accomplished by
manual. In this paper, we propose an effective method for frost observation based on image processing. The changing of
frost formation process is well simulated by using the curve fitting of gray correlation coefficient between certain lengths
of frames, while the characteristic of frost surface texture is also well described by texture analysis based on texture
descriptor. The experiment results show that our method can get high detection accuracy in the different kinds of
continuous changing environment.