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  • 1
    Electronic Resource
    Electronic Resource
    Stop-sign recognition based on color/shape processing (1993)
    Springer
    Machine vision and applications 6 (1993), S. 206-208 
    Details
    ISSN: 1432-1769
    Keywords: Stop-sign recognition ; Color/shape processing ; Advanced driver information systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a robust vision-based stop-sign reconition technique based on sequential processing of color and shape. The primary red-green-blue color coordinate system is first transformed into the saturation-hue-brightness color coordinate system. This color coordinate system allows the red color area of a stop sign to be bounded under various brightness conditions caused by weather, sun angle, or shadows. A combination of a median filter, a morphological filter, Sobel edge operator, and Hough transform is then employed to obtain the boundary contour. It is demonstrated that the parameters of eight straight lines representing the octagonal sides are sufficient for this purpose. Experimental results indicate that stop signs are successfully distinguished from other traffic sighs and background clutter.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01212298
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Estimation of tip shape for carrot classification by machine vision
    Amsterdam : Elsevier
    Journal of Agricultural Engineering Research 53 (1992), S. 123-139 
    Details
    ISSN: 0021-8634
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1016/0021-8634(92)80078-7
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Estimation of tip shape for carrot classification by machine vision
    Amsterdam : Elsevier
    Journal of Agricultural Engineering Research 53 (1992), S. 123-139 
    Details
    ISSN: 0021-8634
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1016/0021-8634(92)80078-7
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Classification of brain compartments and head injury lesions by neural networks applied to MRI (1995)
    Springer
    Neuroradiology 37 (1995), S. 535-541 
    Details
    ISSN: 1432-1920
    Keywords: Head injury ; Magnetic resonance imaging ; Neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and “unknown.” A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00593713
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    Paper (German National Licenses)
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  • 5
    Electronic Resource
    Electronic Resource
    Classification of brain compartments and head injury lesions by neural networks applied to MRI (1995)
    Springer
    Neuroradiology 37 (1995), S. 535-541 
    Details
    ISSN: 1432-1920
    Keywords: Key words Head injury ; Magnetic resonance imaging ; Neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and “unknown.” A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00593713
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 6
    Electronic Resource
    Electronic Resource
    A String Matching Computer-Assisted System for Dolphin Photoidentification (2000)
    Springer
    Annals of biomedical engineering 28 (2000), S. 1269-1279 
    Details
    ISSN: 1573-9686
    Keywords: Dolphin photographic identification ; Computer-assisted system ; Dorsal fin curvature representation ; Syntactic/semantic curve modeling ; String matching ; Dolphin image database
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine , Technology
    Notes: Abstract This paper presents a syntactic/semantic string representation scheme as well as a string matching method as part of a computer-assisted system to identify dolphins from photographs of their dorsal fins. A low-level string representation is constructed from the curvature function of a dolphin's fin trailing edge, consisting of positive and negative curvature primitives. A high-level string representation is then built over the low-level string via merging appropriate groupings of primitives in order to have a less sensitive representation to curvature fluctuations or noise. A family of syntactic/semantic distance measures between two strings is introduced. A composite distance measure is then defined and used as a dissimilarity measure for database search, highlighting both the syntax (structure or sequence) and semantic (attribute or feature) differences. The syntax consists of an ordered sequence of significant protrusions and intrusions on the edge, while the semantics consist of seven attributes extracted from the edge and its curvature function. The matching results are reported for a database of 624 images corresponding to 164 individual dolphins. The identification results indicate that the developed string matching method performs better than the previous matching methods including dorsal ratio, curvature, and curve matching. The developed computer-assisted system can help marine mammalogists in their identification of dolphins, since it allows them to examine only a handful of candidate images instead of the currently used manual searching of the entire database. © 2000 Biomedical Engineering Society. PAC00: 8780Tq, 4230Sy, 0705Pj
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1114/1.1317532
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    Paper (German National Licenses)
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  • 7
    Electronic Resource
    Electronic Resource
    Assisting Manual Dolphin Identification by Computer Extraction of Dorsal Ratio (1999)
    Springer
    Annals of biomedical engineering 27 (1999), S. 830-838 
    Details
    ISSN: 1573-9686
    Keywords: Photoidentification ; Image database ; Feature extraction ; Curve representation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine , Technology
    Notes: Abstract Marine biologists use a measurement called the “Dorsal Ratio” in the process of manual identification of bottlenose dolphins. The dorsal ratio denotes the relative distances of the two largest notches from the tip on the dorsal fin. The manual computation of this ratio is time consuming, labor intensive, and user dependent. This paper presents a computer-assisted system to extract the dorsal ratio for use in identification of individual animals. The first component of the system consists of active contour modeling where the trailing edge of the dorsal fin is detected. This is followed by a curvature module to find the characteristic fin points: tip and two most prominent notches. Curvature smoothing is performed at various smoothing scales, and wavelet coefficients are utilized to select an appropriate smoothing scale. The dorsal ratio is then computed from the curvature function at the appropriate smoothing scale. The system was tested using 296 digitized images of dolphins, representing 94 individual dolphins. The results obtained indicate that the computer extracted dorsal ratio can be used in place of the manually extracted dorsal ratio as part of the manual identification process. © 1999 Biomedical Engineering Society. PAC99: 8718Bb, 4230Va
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1114/1.235
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    Paper (German National Licenses)
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