Autonomous extraction of optimal flame fronts in OH planar laser-induced fluorescence images.

The location of a flame front is often taken as the point of maximum OH gradient. Planar laser-induced fluorescence of OH can be used to obtain the flame front by extracting the points of maximum gradient. This operation is typically performed using an edge detection algorithm. The choice of operating parameters a priori poses significant problems of robustness when handling images with a range of signal-to-noise ratios. A statistical method of parameter selection originating in the image processing literature is detailed, and its merit for this application is demonstrated. A reduced search space method is proposed to decrease computational cost and render the technique viable for large data sets. This gives nearly identical output to the full method. These methods demonstrate substantial decreases in data rejection compared to the use of a priori parameters. These methods are viable for any application where maximum gradient contours must be accurately extracted from images of species or temperature, even at very low signal-to-noise ratios.

Survey of plankton blooms and red tides and their interaction with environmental factors through monitoring satellite images

One of the most important marine ecological phenomena is red tide which is created by increasing of phytoplankton population, influenced by different factors such as climate condition changes, utrification hydrological factors and can leave sever and undesired ecological and economical effects behind itself in the case of durability. Coast line of Hormozgan is about 900km from east to west, within the range of geographical coordinates of 56 16 23.8, 26 58 8.8 to 54 34 5.33 and 26 34 32 eastern longitude and northern latitude, seven sampling stations were considered and sampled for a period of one year from October 2008 to October 2009. after the analysis of Satellite images, monthly, during the best time. In several stages, samplings were performed. In each station, three samples were collected for identification and determination of Bloom- creating species abundance. Cochlodinium polykrikoides was the species responsible for the discoloration which occurred at October 2008 in Hormozgan marine water. Environmental parameters such as sea surface temperature, pH, salinity, Dissolved Oxygen concentration, Total Dissolved Solids (T.D.S.), conductivity, nitrate, nitrite and phosphate and also chlorophyll a were measured and calculated. Kruscal Wallis test was used to compare the densities between different months, seasons and the studied stations. Mann-whitney test from Nonparametric Tests was used for couple comparison. Pearson correlation coefficient was used to determine the relationship between physical and chemical data set and the abundance of Cochlodinium polykrikoides. Multivariate Regression and analysis of variance (ANOVA) also were used to obtain the models and equations of red tide occurrence relationship, environmental parameters and nutrient data. The highest density was 26 million cells per liter in Qeshm station. A meaningful difference was observed between sampling months and seasons but there was no between sampling stations which indicates that in favorable conditions, the occurrence of this phenomenon by the studied species is probable. Regarding to β coefficients of nitrate, temperature, phosphate, Total Dissolvable Solutions (T.D.S) and pH these parameters are effective on the abundance of this species and red tide occurrence. Increase in these factors can represent the effects and outcomes of human activities and increase in marine pollution.

Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.

Automatic object segmentation from calibrated images

This paper addresses the problem of automatically obtaining the object/background segmentation of a rigid 3D object observed in a set of images that have been calibrated for camera pose and intrinsics. Such segmentations can be used to obtain a shape representation of a potentially texture-less object by computing a visual hull. We propose an automatic approach where the object to be segmented is identified by the pose of the cameras instead of user input such as 2D bounding rectangles or brush-strokes. The key behind our method is a pairwise MRF framework that combines (a) foreground/background appearance models, (b) epipolar constraints and (c) weak stereo correspondence into a single segmentation cost function that can be efficiently solved by Graph-cuts. The segmentation thus obtained is further improved using silhouette coherency and then used to update the foreground/background appearance models which are fed into the next Graph-cut computation. These two steps are iterated until segmentation convergences. Our method can automatically provide a 3D surface representation even in texture-less scenes where MVS methods might fail. Furthermore, it confers improved performance in images where the object is not readily separable from the background in colour space, an area that previous segmentation approaches have found challenging. © 2011 IEEE.

MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features

We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-clustered. Co-clustering is performed in a way that maximises discrimination of object images from non-object images, thus emphasizing discriminative features. This provides a way of obtaining perceptual joint-clusters of object images and features. We tackle the problem by simultaneously boosting multiple strong classifiers which compete for images by their expertise. Each boosting classifier is an aggregation of weak-learners, i.e. simple visual features. The obtained classifiers are useful for object detection tasks which exhibit multimodalities, e.g. multi-category and multi-view object detection tasks. Experiments on a set of pedestrian images and a face data set demonstrate that the method yields intuitive image clusters with associated features and is much superior to conventional boosting classifiers in object detection tasks.