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  • 1
    Digitale Medien
    Digitale Medien
    Springer
    Pattern analysis and applications 3 (2000), S. 31-38 
    ISSN: 1433-755X
    Schlagwort(e): Keywords: Autoassociative neural networks; Error function; Image compression;; Visual difference predictor
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract. Autoassociative Neural Networks (AANNs) are most commonly used for image data compression. The goal of an AANN for image data is to have the network output be ‘similar’ to the input. Most of the research in this area use backpropagation training with Mean-Squared Error (MSE) as the optimisation criteria. This paper presents an alternative error function called the Visual Difference Predictor (VDP) based on concepts from the human-visual system. Using the VDP as the error function provides a criteria to train an AANN more efficiently, and results in faster convergence of the weights, while producing an output image perceived to be very similar by a human observer.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    Digitale Medien
    Digitale Medien
    Springer
    Pattern analysis and applications 2 (1999), S. 251-263 
    ISSN: 1433-755X
    Schlagwort(e): Key words: Computer vision; Object recognition; Saccadic behaviour
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract: The automated recognition of targets in complex backgrounds is a difficult problem, yet humans perform such tasks with ease. We therefore propose a recognition model based on behavioural and physiological aspects of the human visual system. Emulating saccadic behaviour, an object is first memorised as a sequence of fixations. At each fixation an artificial visual field is constructed using a multi-resolution/ orientation Gabor filterbank, edge features are extracted, and a new saccadic location is automatically selected. When a new image is scanned and a ‘familiar’ field of view encountered, the memorised saccadic sequence is executed over the new image. If the expected visual field is found around each fixation point, the memorised object is recognised. Results are presented from trials in which individual objects were first memorised and then searched for in collages of similar objects acting as distractors. In the different collages, entries of the memorised objects were subjected to various combinations of rotation, translation and noise corruption. The model successfully detected the memorised object in over 93% of the ‘object present’ trials, and correctly rejected collages in over 98% of the trials in which the object was not present in the collage. These results are compared with those obtained using a correlation-based recogniser, and the behavioural model is found to provide superior performance.
    Materialart: Digitale Medien
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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