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1) "Mavica" -- As to mavica fd Mavica FD5, the first digital model. Mavica is a brand of Sony cameras which use removable disks as the main recording media. The brand is most associated with digital cameras that record on floppy disks, but the name was first used for a line of analog still video cameras announced in 1981, and there were later digital models that recorded onto CDs. The first Digital Mavicas recorded onto floppy disks, a feature that made them very popular in the North-American market. With the evolution of consumer digital camera resolution (megapixels), the advent of the USB interface and the rise of high-capacity storage media, Mavicas started to offer other alternatives for recording images: the floppy-disk (FD) Mavicas began to be Memory Stick compatible (initially through a Memory Stick Floppy Disk adapter, but ultimately through a dedicated Memory Stick slot), and a new CD Mavica series — which uses 8 cm CD-R/CD-RW media — was released in 2000. The first CD Mavica (MVC-CD1000), notable also for its 10x optical zoom, could only write to CD-R discs, but it was able to use its USB interface to read images from CDs not completely written (CDs with incomplete sessions). Subsequent models are more compact, with a reduced optical zoom, and are able to write to CD-RW discs. The Mavica line has been discontinued, and succeeded by the Cyber-shot series, which uses Memory Stick technology for storage. 1 Mavica models 1.1 3.5" floppy 1.2 CD 2 Cameras of similar concept 3 See also MVC-FD5 (late 1997, early 1998, fixed focal length lens) MVC-FD7 (late 1997, early 1998, 10X optical zoom lens) MVC-FD75 10X optical zoom lens MVC-FD73 MVC-FD71 (mid 1998, 10X optical zoom lens) MVC-FD51 (mid 1998, fixed focal length lens) MVC-FD87 MVC-FD92 MVC-FD83 MVC-FD81 MVC-FD85 MVC-FD90 MVC..." 2) "Fd" -- As to mavica fd FD Function: abbreviation fire department Pronunciation Symbols Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. 1 Definition and relation to other tasks 2 Face detection as a pattern-classification task 3 Challenges in pattern classification for face detection 4 Applications 5 Popular algorithms 6 External links Face detection can be regarded as a specific case of object-class detection; In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face detection can be regarded as a more general case of face localization; In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information. Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multiview face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation),or both. Many algorithms --including the ones mentioned in this article's external links section-- implement the face-detection task as a binary pattern-classification task. That is, the content of a given part of an image is transformed into features, after which a classifier trained on example faces decides whether that particular region of the image is a face, or not. Often, a window-sliding technique is employed. That is, the abovementioned classifier is used to classify the (usually square or rectangular) portions of in image, at all locations..." 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