Image Pattern Recognition
Fundamentals and Applications
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-The text introduces two models of Directional local extrema patterns viz.,
Integration of color and directional local extrema patterns
Integration of Gabor features and directional local extrema patterns.
-Provides a framework to extract the features using quantization method
-Discusses the local quantized extrema collected from two oppugnant color planes
-Illustrates the mesh structure with the pixels at alternate positions.
Forlag: CRC Press
Format: 23 x 16 cm
PATTERNS FOR CBIR 3.1 Introduction 3.2 Local patterns 3.3 Directional local extrema Patterns 3.4 Improved directional local extrema patterns 3.5 Conclusion 4. LOCAL QUANTIZED EXTREMA PATTERNS 4.1 Introduction 4.2 Local quantized extrema patterns 4.3 Experimental results and discussion 4.4 Conclusions 5. LOCAL COLOR OPPUGNANT QUANTIZED EXTREMA PATTERNS 5.1 Introduction 5.2 Local color oppugnant quantized extrema patterns 5.3 Experimental results and discussion 5.4 Conclusions 6. LOCAL MESH QUANTIZED EXTREMA PATTERNS 6.1 Introduction 6.2 Local mesh quantized extrema patterns 6.3 Experimental results and discussion 6.4 Conclusions 7. LOCAL PATTERNS FOR FEATURE EXTRACTION 7.1 Quantized Neighborhood Local Intensity Extrema Patterns For Image Retrieval 7.1.1 Major advantages over other methods 7.1.2 Framework of proposed retrieval system 7.1.3 Image Similarity measurement 7.1.4 Experimental results and discussion 7.1.5 Conclusion 7.2 Magnitude Directional Local Extrema Patterns 7.2.1 Introduction 7.2.2 Different types of local patterns 7.2.3 Proposed CMDLEP System 7.2.4 Experimental Results 7.2.5 Conclusion 7.3 Combination of CDLEP and Gabor Features 7.3.1 Introduction 7.3.2 Local Patterns and Variations 7.3.3 Proposed Gabor CDLEP System 7.3.4 Experimental Results 7.3.5 Conclusion 7.4 LEMP: A Robust Image Feature Descriptor for Retrieval Applications 7.4.1 Introduction 7.4.2 Related Local Patterns 7.4.3 Proposed Framework 7.4.4 Conclusion 7.5 Multiple Color Channel Local Extrema Patterns for Image Retrieval 7.5.1 Introduction 7.5.2 Relevant Work 7.5.3 Proposed Method 7.5.4 Experimental Results and Discussions 7.5.5 Conclusion and future scope 7.6 Integration of MDLEP and Gabor Function as a Feature Vector for Image Retrieval System 7.6.1 Introduction 7.6.2 Local Patterns & Variations 7.6.3 Proposed CMDLEP System 7.6.4 Experimental Results 7.6.5 Conclusions 7.7 Local Co-occurrence Patterns 7.7.1 Introduction 7.7.2 Local Patterns 7.7.3 Framework of the proposed system 7.7.4 Experimental Results and Discussions 7.7.5 Conclusion 7.8 Color Based Multi-Directional Local MOTIF XOR Patterns for Image Retrieval 7.8.1 Introduction 7.8.2 Feature Extraction Methods 7.8.3 Proposed Feature Descriptors 7.8.4 Experimental Results and Discussions 7.8.5 Conclusion 7.9 Quantized Local Trio Patterns for Multimedia Image Retrieval System. 7.9.1 Introduction 7.9.2 A Review of local patterns 7.9.3 Proposed Method 7.9.4 Experimental Results and Discussions 7.9.5 Conclusion 8. CONCLUSIONS AND FUTURE SCOPE 8.1 Summary 8.2 Salient features 8.3 Future scope REFERENCES
Md Zia Ur Rahman is presently working as a professor, department of electronics and communication engineering, K L University, Andhra Pradesh, India. His current research interests include adaptive signal processing, biomedical signal processing, medical imaging, array signal processing, MEMS, Nanophotonics. He has published more than 100 research papers in various journals and proceedings and authored 2 books. He is serving in various editorial boards in the capacity of editor in chief, associate editor, reviewer for publishers like IEEE, Elsevier, Springer, American Scientific Publishers, etc.
P Rohini is currently working as an assistant professor, department o computer science engineering, ICFAI University, Hyderabad, India. She has 14 years of teaching experience. Her research interests include image processing, data mining, and deep learning.