This paper describes a graph-based approach to image processing, intended for use with images obtained from sensors having space variant sampling grids. The connectivity graph (CG) is presented as a fundamental framework for posing image operations in any kind of space variant sensor. Partially motivated by the observation that human vision is strongly space variant, a number of research groups have been experimenting with space variant sensors. Such systems cover wide solid angles yet maintain high acuity in their central regions. Implementation of space variant systems pose at least two outstanding problems. First, such a system must be active, in order to utilize its high acuity region; second, there are significant image processing problems introduced by the non-uniform pixel size, shape and connectivity. Familiar image processing operations such as connected components, convolution, template matching, and even image translation, take on new and different forms when defined on space variant images. The present paper provides a general method for space variant image processing, based on a connectivity graph which represents the neighbor-relations in an arbitrarily structured sensor. We illustrate this approach with the following applications: (1) Connected components is reduced to its graph theoretic counterpart. We illustrate this on a logmap sensor, which possesses a difficult topology due to the branch cut associated with the complex logarithm function. (2) We show how to write local image operators in the connectivity graph that are independent of the sensor geometry. (3) We relate the connectivity graph to pyramids over irregular tessalations, and implement a local binarization operator in a 2-level pyramid. (4) Finally, we expand the connectivity graph into a structure we call a transformation graph, which represents the effects of geometric transformations in space variant image sensors. Using the transformation graph, we define an efficient algorithm for matching in the logmap images and solve the template matching problem for space variant images. Because of the very small number of pixels typical of logarithmic structured space variant arrays, the connectivity graph approach to image processing is suitable for real-time implementation, and provides a generic solution to a wide range of image processing applications with space variant sensors.

Space Variant Image Processing

Solid-state array detectors are revolutionizing the ® eld of atomic and molecular spectroscopy.1,2 They enable the analyst to acquire vast amounts of spectrally and/or spatially resolved data in short periods of time. These detectors offer high quantum ef® ciency, low read noise, wide dynamic range, low power requirements, and rugged, but compact, packaging. In addition, as solid-state technology improves at a rapid rate, the cost of these detectors is continuously decreasing, while exibility in design is increasing. As a result, a wide variety of arrays are currently commercially available, with even more on the horizon. The purpose of this article is, ® rst, to provide the reader with an introduction to the variety and the nature of solidstate array detectors and, second, to show how the design of complementary detectors and dispersion optics provides increased analytical capabilities. This article is not intended to be a comprehensive survey. The instruments chosen as examples were selected to illustrate the impact of solid-state array detectors on spectrometry.

https://journals.sagepub.com/doi/pdf/10.1366/0003702971941971

Solid-State Array Detectors for Analytical Spectrometry

Space-variant imaging sensors have many advantages over conventional raster imaging sensors. They provide a large field of view for a given pixel count while maintaining a high resolution at the centre of the field of view and, in addition, produce a mapping that is scale and rotation invariant. The effectiveness of the sensor depends greatly upon the geometry used and the sampling methods employed. In this paper, we define a sensor geometry and introduce an ideal weighted sampling method, where the pixels in the image lying at the intersection of sensor cells, are subdivided into smaller sub-pixels, and an interpolation method using a variable width interpolation mask, whose size varies exponentially with the size and shape of the cells in the sensor array. We compare the computational requirements of these methods, and show that they are scale and rotation invariant, when the image is scaled or rotated about its centre, giving the sensor a functionality similar to that provided by the retinal mapping present in the mammalian retina. These results illustrate the advantages that can be obtained in real-time tracking applications in computer vision, where computational and memory requirements need to be kept to a minimum.

Sensor Geometry and Sampling Methods for Space-Variant Image Processing

A system and method for making an imaging model and an imaging apparatus. An apparatus for image processing may include an optical element having a field of view, and image sectoring element coupled to the optical element, the image sectoring element configured to sector the field of view in a plurality of areas, and an image processor coupled to the image sectoring element, the image processor configured to process an image in accordance with the plurality of areas. Methods to make the foregoing apparatus are also described.

Imaging model and apparatus

Progress in microoptic technology and concepts that blend epitaxial layers of analog circuitry, microoptics, microlaser arrays, and detector arrays with neural retinal designs are discussed. The sensor developments that can lead to tightly coupled and adaptive focal plane arrays are reviewed. Characteristic amacrine functions, such as motion detection, edge enhancement, and space-variant dynamic range compression, are outlined. It is shown that, with optically cross-linked detector arrays, competitive nonlinear center-surround designs that form the basis of biological amacrine functions can bridge the image processing chasm that previous technologies have failed to bridge. >

Wireless focal planes 'On the road to amacronic sensors'

The retina is a smart sensor, but in the sense of intelligent design and not on-chip computing power. It uses a unique layout and elementary charge computing elements to implement in hardware a polar-exponential transform on visual data. The final chip includes a large section of photosites arranged in a circular pattern. Further, the pixels grow m size as radial distance increases. The retina also has a fovea (a high resolution area at the chip's center) and the computational circuitry. The sensor works and will serve as the key component of a real-time imaging system.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Retinalike space variant CCD sensor

Arranging the photosensitive elements of an imaging sensor in a log-polar grid automatically samples an image in a logpolar space. The Retina project is a chip with such a spatially varying layout that can produce the advantages of image processing in the new space at real-time speeds. The actual chip is a small part of a complete imaging system. The system is part of a class of imagers called foveal sensors and these sensors have distinct and significant computational savings over conventional imagers as many as 3-10 orders of magnitude improvement in processing time and memory. The design maintains a large region of high-resolution data although it is still only a fraction of the total photosensitive area.

Design and characterization of a space-variant CCD sensor

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Retinalike space variant CCD sensor

Design and characterization of a space-variant CCD sensor