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Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]
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Major DBMSs, including SQLite, [5] MySQL, [6] Oracle, [7] IBM Db2, [8] Microsoft SQL Server [9] and PostgreSQL [10] support prepared statements. Prepared statements are normally executed through a non-SQL binary protocol for efficiency and protection from SQL injection, but with some DBMSs such as MySQL prepared statements are also available using a SQL syntax for debugging purposes.
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]
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.
Compared to JSON, BSON is designed to be efficient both in storage space and scan-speed. Large elements in a BSON document are prefixed with a length field to facilitate scanning. In some cases, BSON will use more space than JSON due to the length prefixes and explicit array indices. [2]
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 .
Object databases based on persistent programming acquired a niche in application areas such as engineering and spatial databases, telecommunications, and scientific areas such as high energy physics [13] and molecular biology. [14] Another group of object databases focuses on embedded use in devices, packaged software, and real-time systems.