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Efficiency: any progressive image presentation, compression and filtering seeks efficient decoding and presentation. Compression: images can be compressed efficiently and consistently. Easiness: the implementation of the standard is easy. Interchangeability: any PNG decoder that follows the standards can read all PNG data streams.
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]
An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are ...
High Efficiency Image File Format (HEIF) is a digital container format for storing individual digital images and image sequences. The standard covers multimedia files that can also include other media streams, such as timed text, audio and video. [1] HEIF can store images encoded with multiple coding formats, for example both SDR and HDR images.
The dictionary describes the properties of the image, and the stream contains the image data. (Less commonly, small raster images may be embedded directly in a page description as an inline image.) Images are typically filtered for compression purposes. Image filters supported in PDF include the following general-purpose filters:
Tag Image File Format image TLB [11] Type library A binary file with information about a COM or DCOM object so other applications can use it at runtime. Created by Visual C++ or Visual Studio. Used by many Windows applications. TLZ [10] tar archive compressed with LZMA
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The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers.
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