Embodiments of the invention relate to a method for detecting an object on an image representable by picture elements.
Embodiments of the invention relate to technical fields of image processing, segmentation, formative or morphological processing, surface inspection as well as adaptive thresholding.
A search for scratch-type (or crack-type or scrape-type) defects on surfaces is a common task in industrial image processing. Scratch-type objects (scratches, cracks, scrapes, flaws, tears, rifts, splits, etc.) have frequently no solid appearance and disintegrate into a plurality of fragments with a different width and contrast. In order to detect those objects and to calculate their real dimensions, it is frequently necessitated to find and collect their fragments.
This can be performed, for example, with segmentation methods from digital image processing. Segmentation is a subsection of digital image processing and machine vision. Generating regions that are contiguous with regard to content by combining neighboring pixels corresponding to a certain homogeneity criterion is referred to as segmentation.
Many methods for automatic segmentation are known. Basically, they are mainly divided into pixel-, edge- and region-oriented methods. Additionally, a distinction is made between model-based methods, where a certain shape of the objects is used as the starting point, and texture-based methods, where an inner homogenous structure of the object is also taken into consideration. There is no clear dividing line between those methods. Also, different methods can be combined for obtaining better results.
Pixel-oriented methods decide for every individual picture element whether the same belongs to a certain segment or not. This decision can be influenced, for example, by the surroundings. The most widespread method is the thresholding method. Edge-oriented methods search an image for edges or object transitions. Edges are mostly between the pixel regions of an image. Region-oriented methods consider pixel sets as an entity and try to find contiguous objects.
An overview over the existing segmentation methods is described in more detail in “Kenneth R. Castleman: Digital Image Processing, Prentice-Hall, 1996, pp. 447-481” [1].
When segmenting images with elongated objects, in particular on an inhomogenous background, such objects are frequently disintegrated into several fragments. For joining the fragments of the object, for example, known morphological operations are used, such as morphological closing, for example according to [1]. Such a method can consist, for example, of the following steps:    1. Initial binarizing the image with a known method (global thresholding or adaptive thresholding).    2. Closing or morphological closing, respectively, for connecting the individual fragments.    3. Finding and describing all appearances of connected marked pixels.    4. Analysis of found objects and selection of the elongated objects.
On a complex background, this method can be erroneous since, during morphological closing, only the distance between marked pixels is relevant, and if, during initial binarizing, a large amount of appearances are marked due to the irregular background, objects that do not belong together can also be joined upon closing. In the case of a high-contrast background, practically the whole surface can be marked after closing.
A further common problem in industrial image processing is the non-uniform illumination or the non-uniform features on the surface itself.
One problem of many segmentation algorithms is the susceptibility to alternating illumination within the image. This can have the effect that only one part of the image is segmented correctly, while in the others the segmentation is unsuitable. Frequent problems are, for example, over-segmentation, i.e. too many segments, and under-segmentation, i.e. too little segments.
For applications for the surface inspection of spurious noisy images, such as in images with heavily fluctuating brightness or in images of mirroring surfaces, morphological opening or closing methods are mostly slow and provide inaccurate results due to the erroneous detection of the segmentation.
A plurality of further methods of finding elongated objects has been developed for vessel segmentation in retinal images. In “X. Jiang, D. Mojon: “Adaptive Local Thresholding by Verification Based Multithreshold Probing with Application to Vessel Detection in Retinal Images, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 25, No. 1, 2003, pp. 131-137” [2], for example, a method for vessel detection is presented. However, this method, also known as “verification-based multi-threshold probing” includes an application-specific algorithm, which is highly complex and not clearly described.
The above method is very expensive regarding computing complexity, and the time for determining the results is very long. For example, the average computing time for an image of the size of 605×700 pixels is stated with 8 to 36 seconds per image, depending on the selected threshold step size, when the method runs on a Pentium 3 processor with 600 MHz clock rate. Thus, this method is hardly suitable for use in industrial applications having real-time requirements regarding image segmentation.