Blob detection

Two alternative approaches for GCxGC blob detection are (1) use traditional one-dimensional chromatographic peak detection along each second-column chromatogram and then form two-dimensional blobs from the unions of adjacent one-dimensional peaks [18,19] or (2) perform detection in both dimensions... 

Nguyen, T. B., Chung, S. T. (2009, October). An improved real-time blob detection for visual surveillance. In 2nd International Congress on Image and Signal Processing, 2009. CISP 09 (pp. 1 -5). 

Then the code uses the fast blob detection (Sklansky 1978) to classify features which might be aggregates. As the microstructure can have composite aggregates and similar grey levels both in the cement paste and in the aggregates, the classification is done in two steps. 

As an example, a bottle (A, B, C, D) with a (paper) label should be detected as one single object, not as two separate entities with a piece of paper between (A, B) or on top (C) and a different polymer part next to it (PE cap in object A, PP caps in objects B, C, D). Otherwise, based on the classification result, a subsequent sorting or turnout stage would try to handle all these as separate objects (as indicated by the blob centre markers in Fig. 7.4(c)) instead of one object. This requires an image processing step to process the classification results and deliver the correct size and shape of the different objects, regardless of the presence of, for example, interfering paper labels that partially obscure the objects surfaces. 

To achieve that, an object reconstruction algorithm detects connected components (blobs) in the binary images of each material class. In a first step, blobs that do not meet pre-defined size restrictions are considered as classification errors and filtered out (e.g. the black line due to a faulty camera pixel in object D). The binary image of each material class (material and overlay) is then morphologically dilated... 

Manga (1996) noted that clumping of blobs is likely to occur and form large-scale heterogeneities, and this process is expected to work against the careful size balance required to avoid increased thermal-driven buoyancy and/or seismic detection. 

After blobs are detected (or even as they are detected), important statistical features of the blobs can be computed. Most important for quantification, the integration or sum of all of a peak s intensity values is indicative of the relative amount of the compound inducing the peak (subject to the responsivity of... 

In water-NAPL systems, NAPL may exist as a continuous mass (mobile phase or free phase), which can flow under a hydraulic gradient, or as individual ganglia or blobs (residual phase), which are more difficult to mobilize hydraulically. The residual saturation, S, defines the NAPL saturation (VNAPL/I voids) below which NAPL distribution is discontinuous. Typical residual saturation values for soils and sands range from 26% to 75% (Thibodeaux 1996), but have been reported as low as 15-25% (Bedient et al., 1997). In separate field studies, free-phase DNAPL (Pankow and Cherry, 1996) and LNAPL (Huntley et al., 1994) were detected at saturation values as low as 15% and 25%, respectively. In unsaturated soil, typical residual saturation values are between 5% and 20% (Bedient et al., 1997), whereas in saturated soil, residual saturation may be 15-50% of the total pore space (Mercer and Cohen, 1990). 

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