Digital Image Processing: Fundamentals and Recent Advances

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Digital Image Processing: Fundamentals and Recent Advances

Digital Image Processing: Fundamentals and Recent Advances

Authors: Prof. Pradeep C Garg, Dr. Vijendra Kumar Maurya and Mr. Sandeep Changeriwal

ISBN: 978-81-983268-6-7

DOI: https://doi.org/10.59646/ip/297

Date of Publication: December 24, 2024

About the Book:

The book “Digital Image Processing: Fundamentals and Recent Advances” is a comprehensive textbook that provides an in-depth understanding of digital image processing concepts, techniques, and applications. The book covers a wide range of topics, including image acquisition and sampling, image enhancement and restoration, image segmentation, feature extraction, and object recognition. It also explores recent advances in image processing, including deep learning, machine learning, and emerging trends and applications. With a focus on practical applications and real-world examples, this book is ideal for students, researchers, and professionals in the field of digital image processing. The book includes a summary of MATLAB commands and provides a thorough analysis of various image processing techniques, making it a valuable resource for anyone looking to gain a deeper understanding of digital image processing.

References

Chapter I

Fundamentals of Digital Image Processing

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Fundamentals of Digital Image Processing. Pearson, Upper Saddle River, NJ.
  2. Jain, A. K. [1989]. Fundamentals of Digital Image Processing. Prentice Hall, Upper Saddle River, NJ.
  3. Lim, J. S. [1990]. Fundamentals of Digital Image Processing. McGraw-Hill, New York.
  4. Pratt, W. K. [1991]. Fundamentals of Digital Image Processing, 2nd ed., Wiley-Interscience, New York.
  5. Castleman, K. R. [1996]. Fundamentals of Digital Image Processing. Prentice Hall, Upper Saddle River, NJ.
  6. Jahne, B. [1997]. Fundamentals of Digital Image Processing: Concepts, Algorithms, and Scientific Applications. Springer-Verlag, New York.
  7. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Fundamentals of Digital Image Processing, Analysis, and Computer Vision. PWS Publishing, New York.
  8. Schalkoff, R. J. [1989]. Fundamentals of Digital Image Processing and Computer Vision. John Wiley & Sons, New York.
  9. Jain, R., Rangachar, K., and Schunk, B. [1995]. Fundamentals of Digital Image Processing and Computer Vision. McGraw-Hill, New York.
  10. Geladi, P., and Grahn, H. [1996]. Fundamentals of Digital Image Processing: Multivariate Image Analysis. John Wiley & Sons, New York.

 Chapter II

Image Acquisition and Sampling

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Image Acquisition and Sampling. Pearson, Upper Saddle River, NJ.
  2. Jahne, B. [1997]. Digital Image Acquisition and Sampling: Concepts, Algorithms, and Applications. Springer-Verlag, New York.
  3. Pratt, W. K. [1991]. Digital Image Acquisition and Sampling, 2nd ed., Wiley-Interscience, New York.
  4. Castleman, K. R. [1996]. Image Processing Fundamentals: Acquisition and Sampling. Prentice Hall, Upper Saddle River, NJ.
  5. Lim, J. S. [1990]. Two-Dimensional Signal and Image Acquisition and Sampling. Prentice Hall, Upper Saddle River, NJ.
  6. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Image Acquisition and Sampling. PWS Publishing, New York.
  7. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Acquisition and Sampling. Prentice Hall, Upper Saddle River, NJ.
  8. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Image Acquisition and Sampling. John Wiley & Sons, New York.
  9. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Image Acquisition and Sampling, vols. 1 & 2. Addison-Wesley, Reading, MA.
  10. Gonzalez, R. C., and Thomason, M. G. [1978]. Syntactic Pattern Recognition: Image Acquisition and Sampling. Addison-Wesley, Reading, MA.

 Chapter III

Image Enhancement in Spatial Domain

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Image Enhancement in the Spatial Domain. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Spatial Domain Enhancement Techniques, 2nd ed., Wiley-Interscience, New York.
  3. Castleman, K. R. [1996]. Digital Image Processing: Fundamentals and Spatial Domain Enhancement. Prentice Hall, Upper Saddle River, NJ.
  4. Jahne, B. [1997]. Digital Image Processing: Algorithms for Spatial Domain Enhancement. Springer-Verlag, New York.
  5. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Techniques in Spatial Domain Enhancement. Prentice Hall, Upper Saddle River, NJ.
  6. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Spatial Domain Image Enhancement. Addison-Wesley, Reading, MA.
  7. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Spatial Domain Processing. John Wiley & Sons, New York.
  8. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Spatial Domain Enhancement. PWS Publishing, New York.
  9. Gonzalez, R. C., and Thomason, M. G. [1978]. Syntactic Pattern Recognition: Applications in Spatial Domain Image Enhancement. Addison-Wesley, Reading, MA.
  10. Lim, J. S. [1990]. Two-Dimensional Signal and Image Processing: Spatial Domain Enhancement Methods. Prentice Hall, Upper Saddle River, NJ.

Chapter IV

Image Enhancement in Frequency Domain

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Image Enhancement in the Frequency Domain. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Frequency Domain Techniques for Image Enhancement, 2nd ed., Wiley-Interscience, New York.
  3. Castleman, K. R. [1996]. Digital Image Processing: Frequency Domain Fundamentals and Applications. Prentice Hall, Upper Saddle River, NJ.
  4. Jahne, B. [1997]. Digital Image Processing: Algorithms for Frequency Domain Enhancement. Springer-Verlag, New York.
  5. Lim, J. S. [1990]. Two-Dimensional Signal and Image Processing: Frequency Domain Approaches. Prentice Hall, Upper Saddle River, NJ.
  6. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Frequency Domain Processing Techniques. Prentice Hall, Upper Saddle River, NJ.
  7. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Frequency Domain Methods for Image Enhancement. Addison-Wesley, Reading, MA.
  8. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Enhancement in the Frequency Domain. PWS Publishing, New York.
  9. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Frequency Domain Approaches. John Wiley & Sons, New York.
  10. Gonzalez, R. C., and Thomason, M. G. [1978]. Syntactic Pattern Recognition: Applications in Frequency Domain Image Enhancement. Addison-Wesley, Reading, MA.

Chapter V

Geometric Transformations and Image Registration

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Geometric Transformations and Image Registration. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Geometric Transformations and Registration Techniques, 2nd ed., Wiley-Interscience, New York.
  3. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Geometric Transformations and Registration. PWS Publishing, New York.
  4. Jahne, B. [1997]. Digital Image Processing: Geometric Transformations and Scientific Applications. Springer-Verlag, New York.
  5. Castleman, K. R. [1996]. Digital Image Processing: Geometric Transformations and Alignment Methods. Prentice Hall, Upper Saddle River, NJ.
  6. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Geometric Transformations and Image Registration. John Wiley & Sons, New York.
  7. Lim, J. S. [1990]. Two-Dimensional Signal and Image Processing: Geometric Transformations and Registration Approaches. Prentice Hall, Upper Saddle River, NJ.
  8. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Geometric and Registration Methods. Prentice Hall, Upper Saddle River, NJ.
  9. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Geometric Transformations and Image Registration. Addison-Wesley, Reading, MA.
  10. Baxes, G. A. [1994]. Digital Image Processing: Principles and Applications in Geometric Transformations. John Wiley & Sons, New York.

 Chapter VI

Color Image Processing

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Color Image Processing. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Techniques for Color Image Processing, 2nd ed., Wiley-Interscience, New York.
  3. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Color Image Processing Approaches. PWS Publishing, New York.
  4. Jahne, B. [1997]. Digital Image Processing: Algorithms for Color Image Analysis. Springer-Verlag, New York.
  5. Castleman, K. R. [1996]. Digital Image Processing: Principles of Color Image Processing. Prentice Hall, Upper Saddle River, NJ.
  6. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Techniques for Color Processing. John Wiley & Sons, New York.
  7. Lim, J. S. [1990]. Two-Dimensional Signal and Image Processing: Applications in Color Image Analysis. Prentice Hall, Upper Saddle River, NJ.
  8. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Color Image Representation and Processing. Prentice Hall, Upper Saddle River, NJ.
  9. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Color Image Processing Methods. Addison-Wesley, Reading, MA.
  10. Baxes, G. A. [1994]. Digital Image Processing: Principles and Applications in Color Analysis. John Wiley & Sons, New York.

Chapter VII

Image Segmentation

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Image Segmentation Techniques. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Methods for Image Segmentation, 2nd ed., Wiley-Interscience, New York.
  3. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Segmentation Approaches. PWS Publishing, New York.
  4. Jahne, B. [1997]. Digital Image Processing: Segmentation Algorithms and Applications. Springer-Verlag, New York.
  5. Castleman, K. R. [1996]. Digital Image Processing: Fundamentals of Image Segmentation. Prentice Hall, Upper Saddle River, NJ.
  6. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Image Segmentation Methods. John Wiley & Sons, New York.
  7. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Image Segmentation Techniques. Addison-Wesley, Reading, MA.
  8. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Segmentation Techniques. Prentice Hall, Upper Saddle River, NJ.
  9. Lim, J. S. [1990]. Two-Dimensional Signal and Image Processing: Segmentation Applications. Prentice Hall, Upper Saddle River, NJ.
  10. Baxes, G. A. [1994]. Digital Image Processing: Segmentation Principles and Applications. John Wiley & Sons, New York.

Chapter VIII

Morphological Image Processing

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Morphological Image Processing. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Morphological Techniques for Image Analysis, 2nd ed., Wiley-Interscience, New York.
  3. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Morphological Processing Approaches. PWS Publishing, New York.
  4. Jahne, B. [1997]. Digital Image Processing: Concepts and Applications in Morphological Processing. Springer-Verlag, New York.
  5. Serra, J. [1982]. Image Analysis and Mathematical Morphology. Academic Press, New York.
  6. Castleman, K. R. [1996]. Digital Image Processing: Fundamentals of Morphological Processing. Prentice Hall, Upper Saddle River, NJ.
  7. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Morphological Techniques. John Wiley & Sons, New York.
  8. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Morphological Image Processing Methods. Addison-Wesley, Reading, MA.
  9. Giardina, C. R., and Dougherty, E. R. [1988]. Morphological Methods in Image and Signal Processing. Prentice Hall, Upper Saddle River, NJ.
  10. Baxes, G. A. [1994]. Digital Image Processing: Principles of Morphological Image Analysis. John Wiley & Sons, New York.

 Chapter IX

Image Compression

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Image Compression Techniques. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Compression Methods and Applications, 2nd ed., Wiley-Interscience, New York.
  3. Haskell, B. G., and Netravali, A. N. [1997]. Digital Pictures: Representation, Compression, and Standards. Perseus Publishing, New York.
  4. Rabbani, M., and Jones, P. W. [1991]. Digital Image Compression Techniques. SPIE Optical Engineering Press, Bellingham, WA.
  5. Sayood, K. [2017]. Introduction to Data Compression, 5th ed., Morgan Kaufmann, Burlington, MA.
  6. Strang, G., and Nguyen, T. [1996]. Wavelets and Filter Banks: Applications in Image Compression. Wellesley-Cambridge Press, Wellesley, MA.
  7. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Compression Algorithms. Prentice Hall, Upper Saddle River, NJ.
  8. Salomon, D. [2007]. Data Compression: The Complete Reference, 4th ed., Springer-Verlag, New York.
  9. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Compression Techniques. PWS Publishing, New York.
  10. Taubman, D. S., and Marcellin, M. W. [2002]. JPEG2000: Image Compression Fundamentals, Standards, and Practice. Springer-Verlag, New York.

 Chapter- X

Image Restoration

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Image Restoration Techniques. Pearson, Upper Saddle River, NJ.
  2. Pratt, W. K. [1991]. Digital Image Processing: Image Restoration Methods, 2nd ed., Wiley-Interscience, New York.
  3. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Image Restoration Techniques. Prentice Hall, Upper Saddle River, NJ.
  4. Castleman, K. R. [1996]. Digital Image Processing: Image Restoration and Filtering. Prentice Hall, Upper Saddle River, NJ.
  5. Harvey, R. D., and Anastasopoulos, S. G. [1995]. Image Restoration: Theory and Applications. Wiley-Interscience, New York.
  6. Baxes, G. A. [1994]. Digital Image Processing: Restoration Methods and Applications. John Wiley & Sons, New York.
  7. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Restoration Techniques. John Wiley & Sons, New York.
  8. Lim, J. S. [1990]. Two-Dimensional Signal and Image Processing: Restoration Approaches. Prentice Hall, Upper Saddle River, NJ.
  9. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Image Restoration Methods. Addison-Wesley, Reading, MA.
  10. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Restoration Techniques. PWS Publishing, New York.

 Chapter XI

Wavelet Transform and Multire solution Analysis

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Wavelet Transform and Multiresolution Analysis. Pearson, Upper Saddle River, NJ.
  2. Mallat, S. [1999]. A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, San Diego, CA.
  3. Pratt, W. K. [1991]. Digital Image Processing: Multiresolution Analysis Using Wavelets, 2nd ed., Wiley-Interscience, New York.
  4. Strang, G., and Nguyen, T. [1996]. Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, MA.
  5. Daubechies, I. [1992]. Ten Lectures on Wavelets. SIAM, Philadelphia, PA.
  6. Jensen, A. R., and la Cour-Harbo, A. [2001]. Ripples in Mathematics: The Discrete Wavelet Transform. Springer-Verlag, New York.
  7. Lim, J. S. [1990]. Two-Dimensional Signal and Image Processing: Wavelet and Multiresolution Applications. Prentice Hall, Upper Saddle River, NJ.
  8. Jahne, B. [1997]. Digital Image Processing: Wavelet Transform and Multiscale Techniques. Springer-Verlag, New York.
  9. Unser, M. [1995]. Wavelets in Medical Imaging: Multiresolution Techniques for Signal and Image Processing. IEEE Press, New York.
  10. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Multiresolution and Wavelet Techniques. John Wiley & Sons, New York.

 Chapter- XII

Feature Extraction and Object Recognition

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Feature Extraction and Object Recognition. Pearson, Upper Saddle River, NJ.
  2. Jain, A. K. [1989]. Fundamentals of Digital Image Processing: Feature Extraction and Object Recognition Techniques. Prentice Hall, Upper Saddle River, NJ.
  3. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Feature Extraction and Object Recognition. PWS Publishing, New York.
  4. Haralick, R. M., and Shapiro, L. G. [1992]. Computer and Robot Vision: Feature Extraction and Object Recognition. Addison-Wesley, Reading, MA.
  5. Castleman, K. R. [1996]. Digital Image Processing: Techniques for Feature Extraction and Recognition. Prentice Hall, Upper Saddle River, NJ.
  6. Schalkoff, R. J. [1989]. Digital Image Processing and Computer Vision: Object Recognition and Feature Extraction Methods. John Wiley & Sons, New York.
  7. Ballard, D. H., and Brown, C. M. [1982]. Computer Vision: Feature Extraction and Object Recognition. Prentice Hall, Upper Saddle River, NJ.
  8. Davies, E. R. [1997]. Machine Vision: Theory, Algorithms, Practicalities. Academic Press, New York.
  9. Duda, R. O., and Hart, P. E. [1973]. Pattern Classification and Scene Analysis. John Wiley & Sons, New York.
  10. Freeman, H. R., and Garder, D. P. [1996]. Object Recognition and Feature Matching for Computer Vision Applications. Prentice Hall, Upper Saddle River, NJ.

 Chapter-XIII

Machine Learning for Image Processing

  1. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Machine Learning Approaches for Image Analysis. Pearson, Upper Saddle River, NJ.
  2. Bishop, C. M. [2006]. Pattern Recognition and Machine Learning. Springer-Verlag, New York.
  3. Goodfellow, I., Bengio, Y., and Courville, A. [2016]. Deep Learning. MIT Press, Cambridge, MA.
  4. Zhang, L., and Li, Z. [2019]. Machine Learning for Image Processing: Applications and Algorithms. Springer, Cham.
  5. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Machine Learning for Image Understanding. PWS Publishing, New York.
  6. Li, X., and Zhang, Z. [2014]. Machine Learning for Computer Vision: Theory and Applications in Image Processing. Wiley, Hoboken, NJ.
  7. Zhang, D., and Yang, J. [2019]. Machine Learning in Image Processing: From Theory to Applications. Springer, New York.
  8. He, K., Zhang, X., Ren, S., and Sun, J. [2016]. Deep Residual Learning for Image Recognition. IEEE, New York.
  9. LeCun, Y., Bengio, Y., and Hinton, G. [2015]. Deep Learning for Image Recognition and Classification. Nature, 521(7553), 436-444.
  10. O’Shea, K., and Nash, R. [2015]. An Introduction to Convolutional Neural Networks for Image Processing. arXiv preprint arXiv:1511.08458.

  Chapter – XIV

Deep Learning in Image Processing

  1. Goodfellow, I., Bengio, Y., and Courville, A. [2016]. Deep Learning. MIT Press, Cambridge, MA.
  2. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Deep Learning Approaches in Image Analysis. Pearson, Upper Saddle River, NJ.
  3. He, K., Zhang, X., Ren, S., and Sun, J. [2016]. Deep Residual Learning for Image Recognition. IEEE, New York.
  4. LeCun, Y., Bengio, Y., and Hinton, G. [2015]. Deep Learning for Image Recognition and Classification. Nature, 521(7553), 436-444.
  5. Zhang, L., and Li, Z. [2019]. Deep Learning for Image Processing: Concepts, Techniques, and Applications. Springer, Cham.
  6. Bengio, Y. [2009]. Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1-127.
  7. Schmidhuber, J. [2015]. Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117.
  8. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Deep Learning for Image Understanding. PWS Publishing, New York.
  9. Yu, F., and Koltun, V. [2016]. Multi-Scale Context Aggregation by Dilated Convolutions. arXiv preprint arXiv:1511.07122.
  10. Zhang, D., and Yang, J. [2019]. Deep Learning for Computer Vision and Image Processing. Springer, New York.

Chapter XV

Emerging Trends and Applications

  1. Goodfellow, I., Bengio, Y., and Courville, A. [2016]. Deep Learning. MIT Press, Cambridge, MA.
  2. Gonzalez, R. C., and Woods, R. E. [2008]. Digital Image Processing: Deep Learning Approaches in Image Analysis. Pearson, Upper Saddle River, NJ.
  3. He, K., Zhang, X., Ren, S., and Sun, J. [2016]. Deep Residual Learning for Image Recognition. IEEE, New York.
  4. LeCun, Y., Bengio, Y., and Hinton, G. [2015]. Deep Learning for Image Recognition and Classification. Nature, 521(7553), 436-444.
  5. Zhang, L., and Li, Z. [2019]. Deep Learning for Image Processing: Concepts, Techniques, and Applications. Springer, Cham.
  6. Bengio, Y. [2009]. Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1-127.
  7. Schmidhuber, J. [2015]. Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117.
  8. Sonka, M., Hlavac, V., and Boyle, R. [1999]. Image Processing, Analysis, and Computer Vision: Deep Learning for Image Understanding. PWS Publishing, New York.
  9. Yu, F., and Koltun, V. [2016]. Multi-Scale Context Aggregation by Dilated Convolutions. arXiv preprint arXiv:1511.07122.
  10. Zhang, D., and Yang, J. [2019]. Deep Learning for Computer Vision and Image Processing. Springer, New York

 

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