G06V 10/26

Definition

(Für diese Definition ist die deutsche Übersetzung noch nicht abgeschlossen)

Methods and arrangements for segmenting patterns in images or video frames, e.g. segmentation algorithms. Note: segmentation algorithms divide images or video frames into distinct regions, so that boundaries between neighbouring regions coincide with changes of some image properties.

Segmentation algorithms which operate directly on the image by considering the pixel values and their neighbourhood relationships, e.g. mathematical-morphology based algorithms, such as region growing, watershed methods and level-set methods.

Segmentation algorithms which generate a hierarchy of segmentations by starting with a coarse segmentation, which includes only few segments, and successively refine this coarse segmentation by splitting (possibly recursively) the coarse image segments into finer segments (coarse-to-fine approaches).

Graph-cut algorithms such as normalised cuts or min-cut which use graph-based clustering algorithms for image segmentation.

Region growing algorithms which start with few seed points and iteratively expand these into larger regions until some optimality criterion is fulfilled.

The use of classifiers for foreground-background separation. Note: classifiers calculate a score function which expresses a probability (or belief) that a given region of the image is a foreground object or part of the background. The image is then segmented based on these score values.

Deep learning models, in particular different encoder-decoder architectures based on convolutional neural networks (CNN’s), applied to semantic image segmentation (a task which requires not only splitting the image into regions, but also consistently assigning labels to image object categories, e.g. “sky”, “trees”, “road”).

Detection of occlusion. Note: Sometimes an object (e.g. a trunk of a tree) partly occludes another object e.g. a dog behind the tree, which may cause the other object to be split into multiple disjoint segments; occlusion detection algorithms deal with such situations so as to join semantically linked segments into a single segment.

Other algorithms (e.g. some active contour models) which start from an initial image region, which is large enough to surely enclose an object in the image, and they iteratively shrink this region until its boundary is tightly aligned with the contour of the object.

Notes – technical background

These notes provide more information about the technical subject matter that is classified in this place:

Segmentation algorithms divide images or video frames into distinct regions, so that boundaries between neighbouring regions coincide with changes of some image properties.

Segmentation algorithms may determine regions of homogeneous texture, regions having characteristic colours, regions enclosing individual objects, etc.

Some segmentation algorithms are in essence clustering algorithms. They disregard the spatial arrangement of pixels in the image and compute clusters in a feature space (e.g. by running the k-means algorithm on all colour values in an image). They then group spatially-connected pixels belonging to the same cluster into a region (a “segment”).

Examples

Colour segmentation of a skin region of a face using the clustering in a colour space

Example of a scene frequently encountered in autonomous driving and its semantic segmentation map with regions such as “road”, “sky”, “trees”, etc.

Beziehungen zu anderen Klassifikationsstellen

Variational methods used for object recognition, such as active contour models (ACM, or “snakes”), active shape models (ASM) or active appearance models (AMM) are classified in group G06V 10/74.

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Informative Querverweise

Glossar