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Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation.
Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other. The most common method for blob detection is by using convolution .
It is a collection of character data in a database management system, usually stored in a separate location that is referenced in the table itself. Oracle and IBM Db2 provide a construct explicitly named CLOB, [1] [2] and the majority of other database systems support some form of the concept, often labeled as text, memo or long character fields.
Object storage (also known as object-based storage [1] or blob storage) is a computer data storage approach that manages data as "blobs" or "objects", as opposed to other storage architectures like file systems, which manage data as a file hierarchy, and block storage, which manages data as blocks within sectors and tracks. [2]
Change data capture both increases in complexity and reduces in value if the source system saves metadata changes when the data itself is not modified. For example, some Data models track the user who last looked at but did not change the data in the same structure as the data. This results in noise in the Change Data Capture.
A data lake is a system or repository of data stored in its natural/raw format, [1] usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., [2] and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine ...
Blobs provide a complementary description of image structures in terms of regions, as opposed to corners that are more point-like. Nevertheless, blob descriptors may often contain a preferred point (a local maximum of an operator response or a center of gravity) which means that many blob detectors may also be regarded as interest point operators.
The Nested Set model is appropriate where the tree element and one or two attributes are the only data, but is a poor choice when more complex relational data exists for the elements in the tree. Given an arbitrary starting depth for a category of 'Vehicles' and a child of 'Cars' with a child of 'Mercedes', a foreign key table relationship must ...