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000823420 019__ $$a1001801012$$a1005138313$$a1011849974
000823420 020__ $$a9783319613161$$q(electronic book)
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000823420 24500 $$aBiologically rationalized computing techniques for image processing applications /$$cJude Hemanth, Valentina Emilia Balas, editors.
000823420 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2018].
000823420 300__ $$a1 online resource.
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000823420 4901_ $$aLecture notes in computational vision and biomechanics,$$x2212-9391 ;$$vvolume 25
000823420 5050_ $$a1 Artificial Bee Colony Algorithm for Classification of Semi-urban LU/LC Features Using High-Resolution Satellite Data; Abstract; 1 Introduction; 2 Maximum Likelihood Classifiers; 3 Artificial Bee Colony; 3.1 Advantages of ABC; 3.2 Factors Affecting the Performance of the Artificial Bee Colony; 4 Extraction of Textural Features; 5 Materials; 5.1 Data Products Used; 5.2 Study Area; 6 Results and Discussion; 6.1 Performance of ABC at Class Hierarchy Level-I and Level-II; 6.2 Texture: Selection of Optimal Window Size and Interpixel Distance
000823420 5058_ $$a6.3 Effectiveness of Texture Feature Combinations6.4 Investigation of Texture at Class Hierarchy Level-I and Level-II; 7 Conclusion; Acknowledgements; References; 2 Saliency-Based Image Compression Using Walsh-Hadamard Transform (WHT); Abstract; 1 Introduction; 2 Backgrounds; 2.1 Saliency Detection; 2.2 Visual Saliency-Based Image Compression; 2.3 Walsh-Hadamard Transform (WHT); 3 Proposed Method; 3.1 WHT-Based Saliency Map Computation; 3.2 Saliency-Based Image Compression; 3.2.1 Preprocessing Stage; 3.2.2 Transform Domain; 3.2.3 Quantitation; 3.2.4 Encoding; 4 Experimental Results
000823420 5058_ $$a4.1 Results of WHT-Based Saliency Detection4.2 Results of Visual Saliency-Based Image Compression; 4.3 Performance Analysis; 5 Conclusion; References; 3 Object Trajectory Prediction with Scarce Environment Information; Abstract; 1 Introduction; 2 The HOLOTECH Model and Prototype; 3 Train, Testing, and Results; 3.1 Image Acquisition and Feature Extraction; 3.2 Classifier Training and Precision Evaluation; 4 Data Flexibility; 5 Conclusions and Future Work; References; 4 A Twofold Subspace Learning-Based Feature Fusion Strategy for Classification of EMG and EMG Spectrogram Images; Abstract
000823420 5058_ $$a1 Introduction2 Method: Multi-view Template-Based Analysis; 2.1 Database Description; 2.2 CCA Learning; 2.3 Variability Measurement (VM); 2.4 Feature Extraction and Dimension Reduction; 2.5 Feature Fusion and Classification Strategy; 2.6 Classification Performance; 3 Method: Multi-view Spectrogram Image Analysis; 3.1 Spectrogram Generation; 3.2 Classification Performance; 3.3 Comparison with State-of-the-Art Methods; 3.4 Limitations; 4 Conclusion; Acknowledgements; Appendix; Performance Evaluation; Fusion; References
000823420 5058_ $$a5 Automatic Detection of Brain Strokes in CT Images Using Soft Computing TechniquesAbstract; 1 Introduction; 1.1 Symptoms of Stroke; 1.2 Risk Factors of Stroke; 1.3 Imaging Modalities; 1.4 Proposed Scheme; 2 Related Work; 3 Methodology; 3.1 Information Acquisition; 3.2 Image Preprocessing; 3.3 Pre-segmentation Method; 3.4 Feature Extraction; 3.4.1 Discrete Wavelet Transform; 3.4.2 Wavelet Packet Transform (WPT); 3.4.3 Gray-Level Co-occurrence Matrix (GLCM); 3.5 Feature Selection Using LDA; 4 Soft Computing; 4.1 Neural Network; 4.2 Artificial Neural Network Classifier
000823420 506__ $$aAccess limited to authorized users.
000823420 520__ $$aThis book introduces readers to innovative bio-inspired computing techniques for image processing applications. It demonstrates how a significant drawback of image processing – not providing the simultaneous benefits of high accuracy and less complexity – can be overcome, proposing bio-inspired methodologies to help do so.  Besides computing techniques, the book also sheds light on the various application areas related to image processing, and weighs the pros and cons of specific methodologies. Even though several such methodologies are available, most of them do not provide the simultaneous benefits of high accuracy and less complexity, which explains their low usage in connection with practical imaging applications, such as the medical scenario. Lastly, the book illustrates the methodologies in detail, making it suitable for newcomers to the field and advanced researchers alike.
000823420 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 28, 2017).
000823420 650_0 $$aImage processing$$xDigital techniques.
000823420 650_0 $$aNatural computation.
000823420 7001_ $$aHemanth, Jude,$$eeditor.
000823420 7001_ $$aBalas, Valentina Emilia,$$eeditor.
000823420 77608 $$iPrint version: $$z9783319613154
000823420 830_0 $$aLecture notes in computational vision and biomechanics ;$$vv. 25.
000823420 852__ $$bebk
000823420 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-61316-1$$zOnline Access$$91397441.1
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