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An extendible material database for Indoor Air Quality and hygrothermal research and applications
Published in Paul Fazio, Hua Ge, Jiwu Rao, Guylaine Desmarais, Research in Building Physics and Building Engineering, 2020
Most importantly the field Data contains the actual data. Now how can a single field in a database table actually contain many different and quite complex data entries? The database field type BLOB (Binary Large Object) allows storage of basically any kind of data. Using this type the flexibility and extendibility requirements of the database can be met. As a consequence the encoding/decoding of the actual data into human readable form has to be done by a database front end. This is a central point in this database design. Data stored in the database is no longer associated with representation to the user (i.e. traditional tables like in Excel, were each row represents a certain material). The database becomes a real storage medium and all interaction with the user is handled in the database front end. Because direct data editing in the database may become difficult, particularly for more complex data formats, a front end (e.g. user interface or data access library) is indispensable.
Digital Image Processing for Machine Vision Applications
Published in Sheila Anand, L. Priya, A Guide for Machine Vision in Quality Control, 2019
Blob stands for “binary large object.” The method of analyzing an image which has undergone binarization processing is called “blob analysis.” Blob analysis is one of the basic methods used for analyzing the shape of an object. In blob analysis, we first separate the different objects in an image and then try to evaluate which object we are looking to recognize. For example, the objective may be to look for circles, squares, or other shapes present in a target image.
A scalable cloud-based cyberinfrastructure platform for bridge monitoring
Published in Structure and Infrastructure Engineering, 2019
Seongwoon Jeong, Rui Hou, Jerome P. Lynch, Hoon Sohn, Kincho H. Law
Similarly, Figure 9(b) shows that sequential image files collected from a traffic video camera are stored in a row by assigning timestamp (e.g. ‘2016-08-23T10:02:08’) as a clustering key. In addition, part of the timestamp (e.g. year and month) is added to the row key to partition image data to several rows based on the year and month of its acquisition. Each image file is encoded in a binary large object (BLOB) data (e.g. ‘/9j/4AAQSkZj … H//Z’) and stored in a single column. The BLOB data can be converted back to the original image file using imaging libraries, such as Python Imaging Library.4