Three-dimensional Measurement of Loam Clods Using Machine Vision

Yonekawa, Satoshi; Sakai, Nicole; Kitani, Osamu

doi:10.13031/2013.28171

Cited by 2 publications

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“…(1994) have analysed the images of travellers boarding and alighting from buses. One of the richest areas of development of image processing algorithms is agriculture, for recognizing weeds (Woebbecke et al ., 1995), for three‐dimensional reconstruction of clods of earth (Yonekawa et al ., 1994), for tracking piglets (McFarlane & Schofield, 1995) and making pigs comfortable (Wouters et al ., 1990; Geers et al ., 1991) and for estimating the weight of pigs and cows from ‘projected images’ and by stereo‐projection (Minagawa, 1994; Minagawa & Ichikawa, 1994; Schofield, 1990). Even oysters have not been neglected (Tojeiro & Wheaton, 1991; Li & Wheaton, 1992).…”

Section: Introductionmentioning

confidence: 99%

The evolution of electron image processing and its potential debt to image algebra

Hawkes

1998

Journal of Microscopy

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The types of digital electron microscope image processing of greatest interest are not the same in the three major types of electron microscope. In the TEM, electron tomography and crystallography, particularly for 3‐D reconstruction of biological specimens, and procedures that enable the physicist to establish the atomic structure, and especially defects in crystalline material, from one or more of a host of different records — bright‐ and dark‐field images, traditional or convergent‐beam diffraction patterns, energy‐loss spectra, holograms — have attracted the greatest attention. In the SEM, image enhancement and image analysis (counting and measurement of geometrical or compositional features of the image) have always been important and the earliest attempts to process SEM images were in these fields. In the STEM, it is the possibility of using all the information in the far‐field diffraction pattern from every element of the object that has provoked the most exciting and original ideas. Electron images began to be processed by computer or by analog means just 30 years ago and during those years, images of every kind have likewise been enhanced or restored or analysed: in astronomy and medicine, in forensic science and in agriculture, in textile studies and the geosciences, similar algorithms have been developed, often with little awareness of related work. In order to harmonize all this material, an attempt to find a common language for representing it has been sought and the result is ‘image algebra’. This is a simple mathematical structure in which the basic element is not the pixel value but the image, which is a general notion: an array, the elements of which may be real or complex numbers or one‐ or two‐dimensional arrays of such numbers. An image whose pixel values are themselves images plays a very important part and is referred to as a ‘template’. We show how the various image processing algorithms that have been found useful in electron microscopy can be expressed in terms of this algebra. Moreover, image algebra is not merely a convenient way of representing these algorithms; its simplicity and conciseness make it easy to recognize generalizations and variants of the original procedures. We draw attention to some of these and point out promising directions for future work, including the use of a new transform, the ‘slope transform’, for interpreting scanning tunnelling microscope images.

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Section: Introductionmentioning

confidence: 99%

The evolution of electron image processing and its potential debt to image algebra

Hawkes

1998

Journal of Microscopy

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“…This may be accomplished in one of two ways. The objects may be rotated in normal or near infrared light, x-ray, laser light or structured light and viewed by one or two camera systems (Loctronic 1989, Samro 1995, Marchant et al 1990, Crowe 1996, Yonekawa et al 1994. Alternatively, several cameras, or a mirror viewing system may be positioned around a channel, where the potatoes pass through one by one (Weimar Werk 1996, Mörtl 1992).…”

Section: Introductionmentioning

confidence: 99%

A ring sensor system using a modified polar coordinate system to describe the shape of irregular objects

Gall¹

1997

Meas. Sci. Technol.

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For agricultural produce such as potatoes, size and shape determination is very important in grading lines. If electronic sensor systems are installed, weighing cells and optical sensors such as cameras or simple shadow solutions are predominantly used. These systems are limited with regard to the numbers of 'views' of the object, which determines the accuracy for the determination of geometrical parameters. A ring sensor system is described consisting of a large number of optical transmitters and receivers which are arranged alternately. The emitters strobe infrared light in sequence round the ring, creating a large number of 'viewpoints'. Optical connections to each receiver are made. As an object passes through the ring, a shadow zone of each transmitter beam is created. As a result, the tangential chords can be found. A modified polar coordinate system, an algorithm and trigonometrical functions are described which allows the contour of the convex hull of three-dimensional objects to be formed. One advantage of this system is that the data from the ring is in the form of components of vectors and can be used to describe the whole object. As a result an enveloping spiral of the object is created.

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Three-dimensional Measurement of Loam Clods Using Machine Vision

Cited by 2 publications

References 0 publications

The evolution of electron image processing and its potential debt to image algebra

The evolution of electron image processing and its potential debt to image algebra

A ring sensor system using a modified polar coordinate system to describe the shape of irregular objects

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