VideoSnaps Description:

VideoSnaps provides for 'one touch' automatic extraction of distinct high quality photos, from a Video file - no manual picking of frames is required - Neural Net based image matching automatically looks for unique pictures in the video stream, and creates a shortlist of photos which the user can pre-view, then selectively print or discard - ideal for getting those 'magic moments' from any home video recording - without the effort of manually scanning through thousands of near duplicate frames.

First time released as shareware, the VideoSnaps technology was previously used in professional video production for producing posters, cover art, etc. The same ease of use is now available for getting unique photos from any home video recording.

VideoSnaps main features in summary:

* One touch scan of a recorded video file to automatically extract unique photo snaps.
* Default settings to suit most video recordings; optional controls to to tweak the behaviour.
* Automatically scales-up smaller frame sizes to Full Frame size - for larger prints.
* Built-in automatic, adaptive normalization filters reduce scaling artefacts and noise.
* Preview 'found' photos in a slide show, manually capture additional frames.
* Purge unwanted items to get the desired Photo List for batch-printing or other use.

VideoSnaps is a great tool to discover and preserve the hidden treasure-trove of quality photos contained within video recordings from holidays, parties, special functions, etc.

For smaller frame size video typical of digital camcorders, web'ized video, or live video phone recordings, VideoSnaps allows for automatic scaling of scanned photos up to full frame size.

Any home video recording is a store-house of potentially hundreds of high quality photographs - such that would require several film rolls and/ or perfect snap shoots with a conventional or digital still camera. VideoSnaps now lets you discover these at the press of a button.

Higher editions of VideoSnaps also support extraction of snaps based on contextual search criteria.