Space variant imaging

Ferrari, F.; Nielsen, Jakob Gaardbo; Questa, Paolo; Sandini, Giulio

doi:10.1108/02602289510085552

Cited by 42 publications

(15 citation statements)

References 8 publications

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“…"Morphological computation" is also exploited in the design of artificial retinas and has a long history, as well as generally in the field of space-variant sensing (e.g. Hutchinson et al, 1988;Ferrari et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

New Robotics: Design Principles for Intelligent Systems

2005

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New Robotics designates an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e.g. from control theory), designing agents in the New Robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics or design principles, that on the one hand capture theoretical insights about intelligent -adaptive -behavior, and on the other provide guidance in actually designing and building systems. In this paper we provide an overview of all the principles but focus on the principles of "ecological balance" which concerns the relation between environment, morphology, materials, and control, and "sensory-motor coordination" which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we will argue, artificial evolution together with morphogenesis is not only "nice to have" but is in fact a necessary tool for designing embodied agents.

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Section: Introductionmentioning

confidence: 99%

New Robotics: Design Principles for Intelligent Systems

2005

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“…This bandwidth reduction helps us process a high resolution image only at the focus of attention while aware of a wider field of view. Several researchers have designed log-polar sensors for active and real-time vision applications [27]- [31]. These efforts sought to make the leap from biological hardware to VLSI hardware.…”

Section: B Log-polar Transformmentioning

confidence: 99%

Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations

Zokai

Wolberg

2005

IEEE Trans. on Image Process.

171

101

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Abstract-This paper describes a novel technique to recover large similarity transformations (rotation/scale/translation) and moderate perspective deformations among image pairs. We introduce a hybrid algorithm that features log-polar mappings and nonlinear least squares optimization. The use of log-polar techniques in the spatial domain is introduced as a preprocessing module to recover large scale changes (e.g., at least four-fold) and arbitrary rotations. Although log-polar techniques are used in the Fourier-Mellin transform to accommodate rotation and scale in the frequency domain, its use in registering images subjected to very large scale changes has not yet been exploited in the spatial domain. In this paper, we demonstrate the superior performance of the log-polar transform in featureless image registration in the spatial domain. We achieve subpixel accuracy through the use of nonlinear least squares optimization. The registration process yields the eight parameters of the perspective transformation that best aligns the two input images. Extensive testing was performed on uncalibrated real images and an array of 10,000 image pairs with known transformations derived from the Corel Stock Photo Library of royalty-free photographic images.

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“…The Log-Polar transform, one of the most frequently used techniques [11], [6], is formulated as follows: z H log z if jzj b r f , otherwise z H z. Where z re i is the complex variable corresponding to the representation of a point xY y in the Descartes plane, thus x r os and y r sin ; r f is the radius of the fovea in the shape of a circle.…”

Section: Log-polar Transformsmentioning

confidence: 99%

On active camera control and camera motion recovery with foveate wavelet transform

Wei

2001

IEEE Trans. Pattern Anal. Machine Intell.

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AbstractÐIn this paper, a new variable resolution technique±Foveate Wavelet Transform (FWT) is proposed to represent digital images in an effort to efficiently represent visual data. Compared to existing variable resolution techniques, the strength of the proposed scheme encompasses its linearity preservation, orientation selectivity, and flexibility while supporting interesting behaviors resembling the animate vision system. The linearity preservation of the FWT is due to the fact that only low and/or high-pass filterings are carried out in different regions of an image in the transform. The orientation selectivity indicates the fact that details along the horizontal, vertical, and diagonal directions are readily available in the FWT representation. The flexibility of this new representation technique is witnessed by the readiness of its extensions to represent foveae of different number, shape, and locations. To demonstrate the efficacy of the FWT, two applications are presented. First, an FWT-based active camera control scheme is developed, where the computer can move a camera to track the moving object in the scene. Second, an FWT-based method purporting to recover pan/tilt/ zoom camera movements from video clips is developed. Experiments of these two applications have shown encouraging performances.

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Space variant imaging

Cited by 42 publications

References 8 publications

New Robotics: Design Principles for Intelligent Systems

New Robotics: Design Principles for Intelligent Systems

Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations

On active camera control and camera motion recovery with foveate wavelet transform

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