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Thomas F. Schatzki - One of the best experts on this subject based on the ideXlab platform.

  • Machine Recognition of navel orange worm damage in X-ray images of pistachio nuts
    Lwt - Food Science and Technology, 2002
    Co-Authors: Pamela M. Keagy, Bahram Parvin, Thomas F. Schatzki
    Abstract:

    Abstract Insect infestation increases the probability of aflatoxin contamination in pistachio nuts. A non destructive test is currently not available to determine the insect content of pistachio nuts. This paper presents the use of film X-ray images of various types of pistachio nut to assess the possibility of Machine Recognition of insect-infested nuts. Histogram parameters of four derived images are used in discriminant functions to select insect-infested nuts from specific processing streams.

  • Machine Recognition of Navel Orange Worm Damage in X-ray Images of Pistachio Nuts
    Optics in Agriculture Forestry and Biological Processing, 1995
    Co-Authors: Pamela M. Keagy, Bahram Parvin, Thomas F. Schatzki
    Abstract:

    Insect infestation increases the probability of aflatoxin contamination in pistachio nuts. A non- destructive test is currently not available to determine the insect content of pistachio nuts. This paper uses film X-ray images of various types of pistachio nuts to assess the possibility of Machine Recognition of insect infested nuts. Histogram parameters of four derived images are used in discriminant functions to select insect infested nuts from specific processing streams.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

  • Machine Recognition of weevil damage in wheat radiographs
    Optics in Agriculture and Forestry, 1993
    Co-Authors: Pamela M. Keagy, Thomas F. Schatzki
    Abstract:

    An image processing algorithm has been developed for Machine Recognition of weevils and/or weevil damage in film x-ray images of wheat kernels [8 bits, (0.25 mm)2/pixel]. The 8 bit grey scale image is converted to a binary image of interior edges and lines using a Laplacian mask, zero threshold, and background removal. In undamaged kernels the predominant feature of this image is a line representing the central crease of the kernel. In insect-damaged kernels this feature is disrupted and additional edges or lines are seen at angles to the crease. The algorithm uses convolution masks to look for intersections (45 or 90 degree angles with 4 or 5 pixel length sides) at 8 orientations. Recognition varies with insect stage; at least 50% of infested kernels are Machine recognized by the 4th instar (26 - 28 days). This is comparable to 50% Recognition by humans at 25.5 days for images of similar resolution. False positive responses are limited to 0.5%.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Pamela M. Keagy - One of the best experts on this subject based on the ideXlab platform.

  • Machine Recognition of navel orange worm damage in X-ray images of pistachio nuts
    Lwt - Food Science and Technology, 2002
    Co-Authors: Pamela M. Keagy, Bahram Parvin, Thomas F. Schatzki
    Abstract:

    Abstract Insect infestation increases the probability of aflatoxin contamination in pistachio nuts. A non destructive test is currently not available to determine the insect content of pistachio nuts. This paper presents the use of film X-ray images of various types of pistachio nut to assess the possibility of Machine Recognition of insect-infested nuts. Histogram parameters of four derived images are used in discriminant functions to select insect-infested nuts from specific processing streams.

  • Machine Recognition of Navel Orange Worm Damage in X-ray Images of Pistachio Nuts
    Optics in Agriculture Forestry and Biological Processing, 1995
    Co-Authors: Pamela M. Keagy, Bahram Parvin, Thomas F. Schatzki
    Abstract:

    Insect infestation increases the probability of aflatoxin contamination in pistachio nuts. A non- destructive test is currently not available to determine the insect content of pistachio nuts. This paper uses film X-ray images of various types of pistachio nuts to assess the possibility of Machine Recognition of insect infested nuts. Histogram parameters of four derived images are used in discriminant functions to select insect infested nuts from specific processing streams.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

  • Machine Recognition of weevil damage in wheat radiographs
    Optics in Agriculture and Forestry, 1993
    Co-Authors: Pamela M. Keagy, Thomas F. Schatzki
    Abstract:

    An image processing algorithm has been developed for Machine Recognition of weevils and/or weevil damage in film x-ray images of wheat kernels [8 bits, (0.25 mm)2/pixel]. The 8 bit grey scale image is converted to a binary image of interior edges and lines using a Laplacian mask, zero threshold, and background removal. In undamaged kernels the predominant feature of this image is a line representing the central crease of the kernel. In insect-damaged kernels this feature is disrupted and additional edges or lines are seen at angles to the crease. The algorithm uses convolution masks to look for intersections (45 or 90 degree angles with 4 or 5 pixel length sides) at 8 orientations. Recognition varies with insect stage; at least 50% of infested kernels are Machine recognized by the 4th instar (26 - 28 days). This is comparable to 50% Recognition by humans at 25.5 days for images of similar resolution. False positive responses are limited to 0.5%.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Bahram Parvin - One of the best experts on this subject based on the ideXlab platform.

  • Machine Recognition of navel orange worm damage in X-ray images of pistachio nuts
    Lwt - Food Science and Technology, 2002
    Co-Authors: Pamela M. Keagy, Bahram Parvin, Thomas F. Schatzki
    Abstract:

    Abstract Insect infestation increases the probability of aflatoxin contamination in pistachio nuts. A non destructive test is currently not available to determine the insect content of pistachio nuts. This paper presents the use of film X-ray images of various types of pistachio nut to assess the possibility of Machine Recognition of insect-infested nuts. Histogram parameters of four derived images are used in discriminant functions to select insect-infested nuts from specific processing streams.

  • Machine Recognition of Navel Orange Worm Damage in X-ray Images of Pistachio Nuts
    Optics in Agriculture Forestry and Biological Processing, 1995
    Co-Authors: Pamela M. Keagy, Bahram Parvin, Thomas F. Schatzki
    Abstract:

    Insect infestation increases the probability of aflatoxin contamination in pistachio nuts. A non- destructive test is currently not available to determine the insect content of pistachio nuts. This paper uses film X-ray images of various types of pistachio nuts to assess the possibility of Machine Recognition of insect infested nuts. Histogram parameters of four derived images are used in discriminant functions to select insect infested nuts from specific processing streams.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Adnan Amin - One of the best experts on this subject based on the ideXlab platform.

  • Machine Recognition of Hand-Printed Chinese Characters
    1997
    Co-Authors: Adnan Amin, Sameer Singh
    Abstract:

    The Recognition of Chinese characters has been an area of great interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character Recognition: Chinese characters are distinct and ideographic, the character size is very large and many structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate.This article presents a new technique for the Recognition of hand-printed Chinese characters using statistical pattern classification. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct, and difficult to make tolerant to variation in writing styles. The article also discusses Chinese character Recognition using dominant point feature extraction, and statistical pattern classification. The system was tested with 500 characters each character has 40 samples, and the rate of Recognition obtained was 84.45%. This strongly supports the usefulness of the proposed measures for Chinese character classification.

  • Machine Recognition of printed Arabic text utilizing natural language morphology
    International Journal of Human-computer Studies \ International Journal of Man-machine Studies, 1991
    Co-Authors: Adnan Amin, Sabah Al-fedaghi
    Abstract:

    We describe a computer system for recognizing the printed Arabic text of multifonts. The system contains four components: acquisition, segmentation, character Recognition and word Recognition. The word Recognition component includes two sub-systems: a morphological spelling checker/correcter, and a morphological word recognizer. With a 95·5% character Recognition rate corresponding to an 81·58% word Recognition rate, tests have resulted in an increase in the rate of word Recognition by 6·97% using the proposed morphologically based method.

Azriel Rosenfeld - One of the best experts on this subject based on the ideXlab platform.

  • face Recognition a literature survey
    ACM Computing Surveys, 2003
    Co-Authors: W Zhao, Rama Chellappa, P J Phillips, Azriel Rosenfeld
    Abstract:

    As one of the most successful applications of image analysis and understanding, face Recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current Machine Recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, Recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face Recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of Machine Recognition of faces. To provide a comprehensive survey, we not only categorize existing Recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.