Three-dimensional Shape Recognition using a Charge-Simulation Method to Process Primary Image Features

Kanali, Christopher; Murase, Haruhiko; Honami, N.

doi:10.1006/jaer.1998.0265

Cited by 17 publications

(12 citation statements)

References 5 publications

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“…One of the main applications of 3-D machine vision in the future, other than volume estimation, will be in surface area and 3-D shape assessments. Kanali et al (1998) developed a prototype artificial retina machine vision system. In conventional digital image analysis techniques for the inspection of agricultural produce, the image of the produce is first recorded by a video-camera, then the video signals are digitized and finally analyzed.…”

Section: Active 3-d Machine Vision Techniquesmentioning

confidence: 99%

“…A disadvantage of the artificial retina concept is that it cannot calculate fruit diameters, only fruit volume (Gall et al, 1998). The prototype artificial retina machine vision system developed by Kanali et al (1998) transferred the data obtained to a retina model, which was identical to the 'hardware' prototype. This prototype was basically comprised of a hemispherical chamber (artificial retina) equipped with many photosensors, or sensory cells.…”

Section: Other Techniquesmentioning

confidence: 99%

“…The function of sensory cells was to obtain primary shape information, which would then be compressed using the CSM. To assess the performance of their system, Kanali et al (1998) conducted tests on oranges and eggplants. They obtained overall size classification rates of 99% and 74% for oranges and eggplants, respectively.…”

Section: Other Techniquesmentioning

confidence: 99%

“…As apples and oranges consist of more than 80% water, the determination of the volume of water represented an accurate measure of fruit volume, and therefore weight. They measured the microwave attenuation caused by Kanali et al, 1998). The numbers refer to: 1, tungsten halogen lamp; 2, Fresnel lenses; 3, light beam conditioner; 4, artificial retina; 5, inspective fruit; 6, CdS photo sensor; 7, data transfer unit; 8, A/D converter; and 9, computer.…”

Section: Other Techniquesmentioning

confidence: 99%

“…The waveguide cavity concept reported by De Waal et al (1988) is likely to have been associated with some technical or cost problems because there have been no additional reports on this system. Although interesting, the artificial retina machine-vision system of Kanali et al (1998) has the disadvantage of being a desktop system. These authors concluded that more work needs to be done before the technique can be used for automated inspection of fruits and vegetables.…”

Section: Tablementioning

confidence: 99%

See 4 more Smart Citations

Non-destructive technologies for fruit and vegetable size determination – A review

Moreda

Ortiz-Cañavate

García-Ramos

et al. 2009

Journal of Food Engineering

138

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Here, we review different methods for non-destructive horticultural produce size determination, focusing on electronic technologies capable of measuring fruit volume.The usefulness of produce size estimation is justified and a comprehensive classification system of the existing electronic techniques to determine dimensional size is proposed.The different systems identified are compared in terms of their versatility, precision and throughput. There is general agreement in considering that online measurement of axes, perimeter and projected area has now been achieved. Nevertheless, rapid and accurate volume determination of irregular-shaped produce, as needed for density sorting, has only become available in the past few years. An important application of density measurement is soluble solids content (SSC) sorting. If the range of SSC in the batch is narrow and a large number of classes are desired, accurate volume determination becomes important. A good alternative for fruit three-dimensional surface reconstruction, from which volume and surface area can be computed, is the combination of height profiles from a range sensor with a two-dimensional object image boundary from a solid-state camera (brightness image) or from the range sensor itself (intensity image). However, one of the most promising technologies in this field is 3-D multispectral scanning, which combines multispectral data with 3-D surface reconstruction.

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Section: Active 3-d Machine Vision Techniquesmentioning

confidence: 99%

Section: Other Techniquesmentioning

confidence: 99%

Section: Other Techniquesmentioning

confidence: 99%

Section: Other Techniquesmentioning

confidence: 99%

Section: Tablementioning

confidence: 99%

See 3 more Smart Citations

Non-destructive technologies for fruit and vegetable size determination – A review

Moreda

Ortiz-Cañavate

García-Ramos

et al. 2009

Journal of Food Engineering

138

View full text Add to dashboard Cite

show abstract

Quality and Safety Inspection of Food and Agricultural Products by LabVIEW IMAQ Vision

2014

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LabVIEW IMAQ Vision is potentially useful for inspecting food and agricultural products since it combines the merits of both LabVIEW and IMAQ Vision, which have graphical programming environment and rich image processing functions. This paper provides profound introduction to LabVIEW IMAQ Vision: the principal components of LabVIEW IMAQ Vision, calibration, and image processing and analysis using VIs functions are described. In addition, recent advances in the application of LabVIEW IMAQ Vision to food and agricultural products inspection are reviewed, such as defects detection, shape classification, fruit grading and quality evaluation, etc. Finally, current limitations and likely future development trends are discussed. And the results of discussion showed that the utilization of IMAQ Vision module is restricted by the hardware environment, and mixed programming with LabVIEW and MATLAB is the development trend of LabVIEW IMAQ Vision.

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Machine vision system: a tool for quality inspection of food and agricultural products

et al. 2011

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Quality inspection of food and agricultural produce are difficult and labor intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in India are manual which is costly as well as unreliable because human decision in identifying quality factors such as appearance, flavor, nutrient, texture, etc., is inconsistent, subjective and slow. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables, grain quality and characteristic examination and quality evaluation of other food products like bakery products, pizza, cheese, and noodles etc. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on food and agricultural produce.

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Three-dimensional Shape Recognition using a Charge-Simulation Method to Process Primary Image Features

Cited by 17 publications

References 5 publications

Non-destructive technologies for fruit and vegetable size determination – A review

Non-destructive technologies for fruit and vegetable size determination – A review

Quality and Safety Inspection of Food and Agricultural Products by LabVIEW IMAQ Vision

Machine vision system: a tool for quality inspection of food and agricultural products

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