Image processing examples

This is more efficient since fewer pixels need to be examined at each stage. As a further image processing example, here is a simple edge detector ... 

There are of course a number of methods that can be classific d a- methods for the visualization of airflow and contaminant dispersion. This i.hapter describes some of these that are useful for designers of industrial vcntilatiou. Methods that not are presented in more detail here are, for example, to fill small soap bubbles or ordinary balloons with helium in order to stuiiy the airflow field in large rooms. A large number of textbooks focus on flow- visualization. The research in this area can also be followed in The lournal of Floif Visualization and Image Processing. -... 

One is in an even better situation with regard to the identification of a reactive intermediate when one can examine pairs of difference spectra for the formation and the disappearance of a compound, because one can then readily discern the mirror-image patterns that must belong to the targeted species from other spectral changes that may accompany one or the other of these processes. Examples for this will be given in Sections 7.2-7.4 on IR and UV-vis spectroscopy. 

To be sure, linear methods have value where fast computation is necessary. They perform reasonably well when the experimental data are not band limited, and in trials with computer-generated data devoid of noise. Spectroscopic data are often band limited, however, and computation time is becoming less of a problem with advances in computer hardware. The quantity of data required in spectroscopy is far less than that in image processing, for example, another field that has given much attention to deconvolution. Image processing problems are two and sometimes three-dimensional, whereas spectral problems are usually one dimensional. 

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. 

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