Shifted fovea multiresolution geometries

Camacho, P.; Arrebola, Fabián; Sandoval, F.

doi:10.1109/icip.1996.559494

Cited by 21 publications

(14 citation statements)

References 5 publications

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“…Results obtained with both algorithms are similar, but the level sequential one is faster than the iterative (Arrebola, 1998;Bandera, 1994). These types of algorithms have been adapted to process exponential Cartesian topologies with reconfigurable structures for shifted and adaptive fovea Camacho et al, 1996.…”

Section: Introductionmentioning

confidence: 99%

Hardware architecture for hierarchical segmentation in foveal images

Coslado

Camacho

González

et al. 2004

Int J Imaging Syst Tech

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Foveal sensors can substantially increase the performance of active vision systems because of their ability to handle wide field of view and simultaneously reduce the data/bandwidth with space variant sensing. To process the multiresolution images and associated data structures, a new hierarchical processing has been applied to minimize data communications and retrieval. In this article, we present a hardware platform that implements a level sequential segmentation algorithm in one of these hierarchical structures based on a Cartesian lattice topology. The platform operates in real time, at speeds in the range of 25 to 85 frames/s, using a digital uniformresolution camera as the source to generate and process the multiresolution images.

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Section: Introductionmentioning

confidence: 99%

Hardware architecture for hierarchical segmentation in foveal images

Coslado

Camacho

González

et al. 2004

Int J Imaging Syst Tech

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“…Alternative techniques at the optics level were also proposed (Kathman and Johnson, 1992). Based on the selective data reduction achieved with the imager, our approach is oriented to applications requiring fast processing of stereo images, using hierarchical data structures, corresponding to retinotopologies with adaptive characteristics (Camacho et al, 1996(Camacho et al, , 1997, to emulate retinae found in nature with more than one fovea, and to profit the many algorithms developed for rectangular images, unsuitable for non-Cartesian images or requiring complex transformations to adapt them. The algorithms and techniques used at the systems level are beyond the scope of the article.…”

Section: Introductionmentioning

confidence: 99%

Multifoveal imager for stereo applications

Camacho

Coslado

González

et al. 2002

Int J Imaging Syst Tech

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A multiresolution imager based on adaptive retinal structures, with data compressions above 85%, is presented in this article. The main goal of the imager platform is to speed up image processing with the use of selective data reductions to shorten the vision systems' tasks in stereo applications. Implemented on a fieldprogrammable gate array, the platform can be configured as the front end of active vision systems, with use of adaptive foveal sensing on uniresolution images to cover wide fields of view, being also a development tool for multiresolution applications with different image formats and interfaces. The multifoveal imager provides the hierarchical data structures related to multiresolution levels, following instructions to control sensor parameters or to perform adaptive fovea fixations in real time, adapting its operation to the constraints of the active vision systems. It also uses intermediate resolution data to implement in hardware an efficient background extractor to cooperate with image processors in motion detection tasks and attention mechanisms. Some platform configurations are explained and experimental results are discussed in relation to the advantages of the adaptive retinal structures.

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“…According to the sampling theorem, the highest frequency that can be represented without aliasing by the display, or the display Nyquist frequency, is half of the display resolution cycles degree (6) Combining (3) and (6), we obtain the cutoff frequency for a given location by…”

Section: A Foveation Resolution and Error Sensitivity Modelsmentioning

confidence: 99%

“…A very commonly used approach is the logmap [3] superpixel method. In [3]- [6], local pixel groups are averaged and mapped into superpixels, whose sizes are determined by the retinal sampling density. In [7], a multistage superpixel approach is introduced, where a progressive transmission scheme is implemented by using variable sizes of superpixels in each stage.…”

Section: Introductionmentioning

confidence: 99%

Embedded foveation image coding

Wang

Bovik

2001

IEEE Trans. on Image Process.

174

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Abstract-The human visual system (HVS) is highly space-variant in sampling, coding, processing, and understanding. The spatial resolution of the HVS is highest around the point of fixation (foveation point) and decreases rapidly with increasing eccentricity. By taking advantage of this fact, it is possible to remove considerable high-frequency information redundancy from the peripheral regions and still reconstruct a perceptually good quality image. Great success has been obtained recently by a class of embedded wavelet image coding algorithms, such as the embedded zerotree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) algorithms. Embedded wavelet coding not only provides very good compression performance, but also has the property that the bitstream can be truncated at any point and still be decoded to recreate a reasonably good quality image.In this paper, we propose an embedded foveation image coding (EFIC) algorithm, which orders the encoded bitstream to optimize foveated visual quality at arbitrary bit-rates. A foveation-based image quality metric, namely, foveated wavelet image quality index (FWQI), plays an important role in the EFIC system. We also developed a modified SPIHT algorithm to improve the coding efficiency. Experiments show that EFIC integrates foveation filtering with foveated image coding and demonstrates very good coding performance and scalability in terms of foveated image quality measurement.

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Shifted fovea multiresolution geometries

Cited by 21 publications

References 5 publications

Hardware architecture for hierarchical segmentation in foveal images

Hardware architecture for hierarchical segmentation in foveal images

Multifoveal imager for stereo applications

Embedded foveation image coding

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