Camera Assisted ProjectionThe recent availability of cheap, small, and bright projectors has made it practical to use them for a wide range of applications such as creating large seamless displays [8,11,12,25] and immersive environments [5,21]. By introducing a camera into the system, and applying techniques from computer vision, the projection system can operate taking its environment into account. For example, it is possible to allow users to interact with the projected image creating projected interfaces [1,10,24]. The camera can assist projection by taking into account distortions due to surface geometry [18,19], or eliminate shadows cast on the projected image [9,22]. The camera can also assist projection by color correcting a homogeneous colored surface [6], or by correcting for spatially varying color and texture [14,3]. Related methods have been applied to improve the recovery of 3D geometry from structured light [4], to neutralize the appearance of a painting [2], to restore color-damaged paintings [26], and to control the appearance of 3D objects [7].All previous work that takes into account both geometric and photometric properties of projection has assumed both a static scene and projection system. The assumption that the scene and system remain static is very restrictive. This is especially true when we consider recently presented applications that require hand-held or mobile projection systems such as iLamps and RFIG Lamps [18,20]. Even when the projector is not mobile, there are systems that use computer-controlled motors to steer the projected image onto any surface in an environment [13,16]. For applications in which the projector is fixed, the scene may be dynamic, such as with illuminating clay (a 3D tangible interface) [17]. It is thus critical for projectorcamera systems to be able to take into account both geometric and photometric changes.One approach that was proposed to handle photometric changes is direct dynamic feedback from the camera for each pixel [14]. This method requires several frames to converge, limiting its use to projecting static images on static scenes. A method for projection of video on static scenes was proposed in [7,14] using a photometric model. The parameters of this model are determined off-line by projecting and capturing a sequence of calibration images. Arbitrary images (video) are then compensated on-line, before projection, using the same model; the model is valid as long as the scene remains static. Whenever the scene changes, however, the calibration images must be re-projected making this method impractical in a dynamic environment.In this work, we present a novel hybrid method which combines a model based approach with dynamic feedback

Camera Assisted ProjectionThe recent availability of cheap, small, and bright projectors has made it practical to use them for a wide range of applications such as creating large seamless displays [8,11,12,25] and immersive environments [5,21]. By introducing a camera into the system, and applying techniques from computer vision, the projection system can operate taking its environment into account. For example, it is possible to allow users to interact with the projected image creating projected interfaces [1,10,24]. The camera can assist projection by taking into account distortions due to surface geometry [18,19], or eliminate shadows cast on the projected image [9,22]. The camera can also assist projection by color correcting a homogeneous colored surface [6], or by correcting for spatially varying color and texture [14,3]. Related methods have been applied to improve the recovery of 3D geometry from structured light [4], to neutralize the appearance of a painting [2], to restore color-damaged paintings [26], and to control the appearance of 3D objects [7].All previous work that takes into account both geometric and photometric properties of projection has assumed both a static scene and projection system. The assumption that the scene and system remain static is very restrictive. This is especially true when we consider recently presented applications that require hand-held or mobile projection systems such as iLamps and RFIG Lamps [18,20]. Even when the projector is not mobile, there are systems that use computer-controlled motors to steer the projected image onto any surface in an environment [13,16]. For applications in which the projector is fixed, the scene may be dynamic, such as with illuminating clay (a 3D tangible interface) [17]. It is thus critical for projectorcamera systems to be able to take into account both geometric and photometric changes.One approach that was proposed to handle photometric changes is direct dynamic feedback from the camera for each pixel [14]. This method requires several frames to converge, limiting its use to projecting static images on static scenes. A method for projection of video on static scenes was proposed in [7,14] using a photometric model. The parameters of this model are determined off-line by projecting and capturing a sequence of calibration images. Arbitrary images (video) are then compensated on-line, before projection, using the same model; the model is valid as long as the scene remains static. Whenever the scene changes, however, the calibration images must be re-projected making this method impractical in a dynamic environment.In this work, we present a novel hybrid method which combines a model based approach with dynamic feedback

In an active noise control system that uses the Filtered‐x method as an adaptive algorithm, the impulse response on the error path is observed and the result is assigned as a coefficient of an error path filter before the system is started. Such an impulse response can obviously change after the system is started. The change can increase the difference between the assigned coefficient and the inherent coefficient of the error path filter, and thus render the operation of noise control unstable. This paper proposes a noise control filter coefficient renewal method that does not require the calculation of an error path filter coefficient by focusing on the fact that, when a set of two different coefficients is assigned to a noise control filter, the system consisting of components from a noise detection microphone to an error detection microphone establishes two independent equations whose variables are impulse responses for the feedforward path and the error path system. The coefficient of the noise control filer can be renewed by solving the simultaneous equations by iteration. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(12): 101–108, 2002; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/ecjc.1132

In this study, we verify the performance of the simultaneous equations method using an experimental active noise control system. The simultaneous equations method is based on a priciple different from the filtered-x algorithm requiring a filter modeled on a secondary path from a loudspeaker to an error microphone. Instead of the filter, called the secondary path filter, this method uses an auxiliary filter identifying the overall path consisting of a primary path, a noise control filter and the secondary path. As inferred from the configuration of the overall path, the auxiliary filter can provide two independent equations when two different coefficient vectors are given to the noise control filter. The method thereby estimates the coefficient vector of the noise control filter minimizing the output of the error microphone. In this paper, we propose the application of a frequency domain adaptive algorithm to the identification of the overall path. An improvement in the noise reduction speed is thereby expected. In this paper, we also present computer simulation results demonstrating that the simultaneous equations method can automatically recover the noise reduction effect degraded by path changes, and finally, using an experimental system, we indicate that the method successfully works in practical systems.

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