By Michael A. Smith
Multimodal Video Characterization and Summarization is a helpful learn device for either execs and academicians operating within the video box. This ebook describes the technique for utilizing multimodal audio, picture, and textual content know-how to represent video content material. This new and groundbreaking technological know-how has ended in many advances in video figuring out, similar to the improvement of a video precis. functions and method for developing video summaries are defined, in addition to user-studies for review and checking out.
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Extra resources for Multimodal Video Characterization and Summarization (The International Series in Video Computing)
6 Camera Focus The detection of changes in camera focus is useful for isolating a region of interest in a video frame. By altering the focal point of the camera, a producer can select which elements will remain in focus and clear for viewer inspection. Several common examples of camera focus effects are illustrated below. 1. Pulling Focus - This procedure occurs when the point of camera focus changes during the course of a scene. Selective focusing can be used as a creative tool to shift emphasis from one part of the scene to another.
A logo representing the subject is often placed in a corner next to an anchorperson during dialogue. Detection of graphics is a useful method for finding changes in semantic content; its appearance may serve as a scene break. Recognition of corner regions for graphics detection may be possible through an extension of the shot change detection technique, such as histogram analysis of isolated image regions instead of the entire image. Procedures for graphics detection are discussed in chapter 3.
If the text of interest is fixed in a specific location, a time-based average may correct false detections that occur when moving captions or special effects are placed in a shot. Procedures for text detection are discussed in chapter 3. For some fonts a generic optical character recognition (OCR) package may accurately recognize video captions. For most OCR systems, the input is an individual character. This presents a problem in digital video since most characters degrade during recording, digitization and compression.