In this chapter we are going to discuss the problem of image encoding and compression. We will present the classical methods for image compression based on transformation to the frequency domain (i.e. Discrete Cosine Transform) and exploiting multiresolution decomposition (i.e., the Wavelet Transform). These methods are employed respectively on the JPEG compression and JPEG 2000 compression standards.
Techniques of filming using special effects have existed since the 1920s, well before the advent of computers. Two of them are known as Back Projection—when an actor acts in front of a screen that reproduces other footage (very common in train scenes), and Blue Screen—when an actor acts in front of a blue wall for later composition with another scene (Fielding, 1985). However, it was computer graphics and the technological advance of the computers that made possible the great evolution in this area. Virtual Sets or Virtual Studios are denominations given to the integrated use of computer-generated elements with real actors and objects in a studio. Its main advantages are: more flexibility in changing the scene, risky scenes can be made safely, allowing the production of complex special effects and also providing economy in the production of sophisticated designs, along with flexibility in making quick changes. With the advent of high-speed networks, there is the possibility of remote operation. Real-time Virtual Sets is a very recent area for computer graphics with potential applications in the film and television industry. The literature about this topic is scarce although there are few commercial systems available, which will be described later. This work approaches Virtual Sets, describing its conceptualization and showing its correlation with other areas in computer graphics. The Virtual Sets’ pertinent technologies are identified in computer graphics and have their given solutions and unsolved problems argued.
Phalange rods (1), guitar fiducial (2), bad (3) and good (4) arrangement of notes. MotivationInterfacing with the guitar using the audio signal is one of the oldest problems in Computer Music, and advances in the area were astonishing. In our days it is possible to simulate a huge range of amplifiers, apply many filter effects and evaluate the pitch of a plucked string robustly, to mention a few useful applications.However, there are problems very hard to solve using audio, like recognizing the chord being played when the musician is not downor up-stroking all strings at once, but picking them one at a time.In this work we explore the visual interface of the guitar, a subject that only in recent years has received the proper attention. Three aspects are treated. The first has just been mentioned: the problem of chord recognition when not all notes of the chord are played. The second relates to the implementation of an automatic composition algorithm inspired on the bi-dimensional nature of the representation of the diatonic scale in the guitar fretboard. Finally, the knowledge about the current chord, or simply the rough position of the hand in the guitar fretboard, allows controlling some parameters of the automatic composition algorithm, what becomes specially interesting in live performance. Chord Recognition and Hand LocationA Supervised Machine Learning algorithm is used to learn the patterns of the rough positions of the fingertips (in guitar-fretboard coordinates) corresponding to the chords we want to recognize. To determine the approximate position of the fingertips in the scene, retro-reflexive rods are attached to the back of the finger middle phalanges, by means of elastic ribbons ( Figure 1). To locate the guitar, four circular retro-reflexive fiducials are attached to it (Figure 2). Then, using infrared light and camera, fiducials and rods can be isolated, and a projective transformation is used to estimate the north-most extreme of the rods in guitar fretboard coordinates. The center of mass of the finger points represents the rough position of the hand in the same coordinate system. Automatic CompositionLet us say we want to compose a melody using the diatonic scale, in the key of G. Figure 3 shows almost all the notes of such a scale between the first and the 19th fret of the guitar. Such a representation is not algorithmically friendly, due to the absence of a clear pattern. Fortunately we can rearrange the notes of the scale as shown in Figure 4. This way, it becomes easy to write routines to build a sequence of notes, i.e., a melodic line. We can, for example, implement two independent markovian processes, one for the rows and the the other for the columns of the matrix of points.This kind of interface is more adequate for simulating guitar improvisation, since the availability of a musical note near the current region of improvisation is also important, besides the note itself. The availability of a note changes depending on the dimension of the instrument interface, so a bi-dimensional metho...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.