Image velocimetry for clouds with relaxation labeling based on deformation consistency

Correlation-based cloud tracking has been extensively used to measure atmospheric winds, but still difficulty remains. In this study, aiming at developing a cloud tracking system for Akatsuki, an artificial satellite orbiting Venus, a formulation is developed for improving the relaxation labeling technique to select appropriate peaks of cross-correlation surfaces which tend to have multiple peaks. The formulation makes an explicit use of consistency inherent in the type of cross-correlation method where template sub-images are slid without deformation; if the resultant motion vectors indicate a too-large deformation, it is contradictory to the assumption of the method. The deformation consistency is exploited further to develop two post processes; one clusters the motion vectors into groups within each of which the consistency is perfect, and the other extends the groups using the original candidate lists. These processes are useful to eliminate erroneous vectors, distinguish motion vectors at different altitudes, and detect phase velocities of waves in fluids such as atmospheric gravity waves. As a basis of the relaxation labeling and the post processes as well as uncertainty estimation, the necessity to find isolated (well-separated) peaks of cross-correlation surfaces is argued, and an algorithm to realize it is presented. All the methods are implemented, and their effectiveness is demonstrated with initial images obtained by the ultraviolet imager onboard Akatsuki. Since the deformation consistency regards the logical consistency inherent in template matching methods, it should have broad application beyond cloud tracking

Cloud trains associated with Martian Mountain Lee Waves on the eastern side of the Phlegra Montes

Abstract Mountain lee waves have often been observed on the eastern side of the Phlegra Montes as wave trains visualized by water ice clouds in the Martian atmosphere. The seasonality and formation condition of these lee waves and associated cloud trains have not yet been investigated, whereas those of Martian dust storms have been studied observationally and numerically. We extract the cloud trains in this region from images observed by the Mars Orbiter Camera onboard the Mars Global Surveyor and measure the wavelengths of the lee waves. It is revealed that, on the eastern side of the Phlegra Montes, cloud trains tend to form in the northern winter season, except during the 2001 global dust storm. The results suggest that stationary mountain waves are excited in the thermally stable atmosphere, but are trapped below an altitude of approximately 10 km due to the zonal wind structure that increases rapidly with altitude. This is consistent with the previous studies on gravity waves in the Martian atmosphere and is the first study to constrain the typical altitude of the cloud trains from imager observations. Graphical Abstrac

Automated blood vessel extraction using local features on retinal images

ABSTRACT An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak to turned image, thus a method was improved by the addition of HLAC features to a polar transformed image. The blood vessels were classified using an artificial neural network (ANN) with HLAC features using 105 mask patterns as input. To improve performance, the second ANN (ANN2) was constructed by using the green component of the color retinal image and the four output values of ANN, Gabor filter, double-ring filter and black-top-hat transformation. The retinal images used in this study were obtained from the "Digital Retinal Images for Vessel Extraction" (DRIVE) database. The ANN using HLAC output apparent white values in the blood vessel regions and could also extract blood vessels with low contrast. The outputs were evaluated using the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis. The AUC of ANN2 was 0.960 as a result of our study. The result can be used for the quantitative analysis of the blood vessels