Mathematical Morphology and its Applications to Image and Signal Processing

End-to-End Pixel-Wisely Detection of Oceanic Eddy on SAR Images With Stacked Attention Network

“…Specifically, the Open and Close operation is imported and imposed successively to the direct output of the network. The Open operation is defined as first Dilating and then Eroding, and the Close operation is the opposite, first Eroding and then Dilating, both based on the interrelationship of pixels in the binary image [63]. Simply put, the Open operation will remove the individual white dots on the background and on the eddy boundaries.…”

Multidimensional Median Filters for Finding Bumps in Chemical Sensor Datasets

“…local maxima) in digital images via various processing techniques (e.g. local maxima) in digital images via various processing techniques [10][11][12][13][14][15]. Examples of common MM functions include opening, closing, thinning, binning, thresholding, and watershed methods, and have been employed in numerous applications including pedestrian detection [16], tumor mass detection [17], and facial feature detection [18,19].…”

“…Choosing c too small lead a largely variable residual image (missing small spot while choosing c too large leads to a residual im with a large bias term (too many spurious spots). Similarly, the optimal choice of c is related to se k) can determine "opt al" smoothing strategies, while other procedures det ine smoothing parameters from examining figures suc as mode trees [40] or estimates of the mean squared [41]. To improve the ability of our MM operator i e presence of noise, we have explored applying stan d image smoothing techniques to the image prior to ap ying the MM filter.…”

“…Unfortunately, due to the cost of these technologies and the lack of a gold standard for measuring e signal of the chemical sensor, it is difficult to design statistically appropriate benchmarks or quality tions, the success of our proposed method will be de ndent on the choice of smoothing parameter, c. It is ou e the scope of this manuscript to perform a thorough parison of competing spot finding algorithms agai a set of noise distributions. For future work, we propo erforming comparisons such as those in [42,43] veral other problems in statistics, the optimal choice of bandwidth in kernel density estimation [38], and the amount of times to smooth a dataset [39]. Various strategies that estimate error quantities (ris control studies to assess these image analysis techniques for a given chemical sensor.…”