07563nam a2200757 i 45000010014000000030004000140050017000180060019000350070015000540080041000690200025001100200024001350200025001590200027001840240035002110350025002460350022002710400040002930500025003330720016003580720023003741000042003972450076004392500020005152640098005353000071006333360021007043370026007253380032007514900027007834900034008105000043008445040041008875050223009285050250011515050522014015050520019235050440024435050270028835050137031535050157032905050306034475050423037535050191041765050332043675050321046995060075050205200727050955210032058225300029058545380036058835380078059195450336059975880061063336300019063946500049064136500030064626500050064927100061065427760049066038300031066528300034066838560059067179420012067769990017067889780750359696IOP20251030124833.0m    eo  d        cr cn |||m|||a241213s2024    enka   fob    000 0 eng d  a9780750359696qebook  a9780750359689qmobi  z9780750359672qprint  z9780750359702qmyPrint7 a10.1088/978-0-7503-5969-62doi  a(CaBNVSL)thg00084075  a(OCoLC)1478800743  aCaBNVSLbengerdacCaBNVSLdCaBNVSL 4aTA16374b.T553 2024eb 7aUYT2bicssc 7aCOM0120502bisacsh1 aThiruvikraman, P. K.,eauthor.99712512aA course on digital image processing with MATLAB /cP.K. Thiruvikraman.  aSecond edition. 1aBristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) :bIOP Publishing,c[2024]  a1 online resource (various pagings) :billustrations (some color).  atext2rdacontent  aelectronic2isbdmedia  aonline resource2rdacarrier1 a[IOP release $release]1 aIOP ebooks. [2024 collection]  a"Version: 20241101"--Title page verso.  aIncludes bibliographical references.  a1. Introduction -- 1.1. The scope and importance of digital image processing -- 1.2. Images -- 1.3. Digital images -- 1.4. Processes involved in image processing and recognition -- 1.5. Applications of image processing8 a2. Image enhancement in the spatial domain -- 2.1. Enhancement of contrast -- 2.2. Gray level transformations -- 2.3. Bit plane slicing -- 2.4. Histogram processing -- 2.5. Filtering in the spatial domain -- 2.6. Sharpening in the spatial domain8 a3. Filtering in the Fourier domain -- 3.1. From the Fourier series to the Fourier transform -- 3.2. Meaning of the Fourier transform -- 3.3. The impulse function -- 3.4. Fourier transform of a train of impulses -- 3.5. The convolution theorem -- 3.6. The discrete Fourier transform (DFT) -- 3.7. Additional properties of the DFT -- 3.8. Filtering in the Fourier domain -- 3.9. Low-pass filters -- 3.10. Other low-pass filters -- 3.11. High-pass filters -- 3.12. The FFT -- 3.13. Comparison of the FFT with convolution8 a4. Image compression -- 4.1. Basics of image compression -- 4.2. Basics of coding theory -- 4.3. Uniquely decodable codes (UDCs), instantaneously decodable codes (IDCs), and all that -- 4.4. Kraft's inequality -- 4.5. Efficiency of instantaneous codes -- 4.6. Information theory -- 4.7. Huffman coding : algorithm -- 4.8. Huffman coding : implementation -- 4.9. Nearly optimal codes -- 4.10. Reducing interpixel redundancy : run-length coding -- 4.11. LZW coding -- 4.12. Arithmetic coding -- 4.13. Transform coding8 a5. Image analysis and object recognition -- 5.1. Image analysis -- 5.2. Detection of points and lines -- 5.3. The Hough transform -- 5.4. Segmentation : edge detection -- 5.5. Thresholding -- 5.6. A global view of image analysis and pattern recognition -- 5.7. Representation of objects -- 5.8. Texture -- 5.9. Skeletonization or medial axis transformation (MAT) -- 5.10. Principal component analysis (PCA) -- 5.11. Pattern recognition8 a6. Image restoration -- 6.1. Analyzing motion blur -- 6.2. Inverse filtering -- 6.3. Noise -- 6.4. Removal of noise by morphological operations -- 6.5. Alternative method for extracting and labeling connected components -- 6.6. Image reconstruction from projections8 a7. Wavelets -- 7.1. Wavelets versus the Fourier transform -- 7.2. The Haar wavelet transform -- 7.3. An alternative view of wavelets8 a8. Color image processing -- 8.1. The RGB color model -- 8.2. The CMY and CMYK color models -- 8.3. The hue, saturation, and intensity (HSI) color model8 a9. Introduction to MATLABª -- 9.1. Introduction -- 9.2. Help with MATLABª -- 9.3. Variables -- 9.4. Mathematical operations -- 9.5. Loops and control statements -- 9.6. Built-in MATLABª functions -- 9.7. Some more useful MATLABª commands and programming practices -- 9.8. Functions8 a10. The image processing toolbox -- 10.1. Introduction -- 10.2. Reading from an image file and writing into an image file -- 10.3. Fourier domain processing -- 10.4. Calculation of entropy -- 10.5. Huffman code -- 10.6. Arithmetic code -- 10.7. Segmentation -- 10.8. Hough transform -- 10.9. Some common error messages in MATLAB -- 10.10. Using the MATLAB AI chat playground to generate MATLAB code -- 10.11. Exercises8 a11. Video processing -- 11.1. Introduction -- 11.2. Extracting frames from a video -- 11.3. Video compression -- 11.4. Detection and analysis of motion : optical flows -- 11.5. Exercises8 a12. Applications of machine learning -- 12.1. Linear discriminant analysis -- 12.2. Clustering using the k-means algorithm -- 12.3. K nearest neighbours -- 12.4. Artificial neural networks -- 12.5. Multilayer feedforward neural networks -- 12.6. Matrix formulation of ANNs -- 12.7. Back propagation for training neural networks8 a13. Error correcting codes and cryptography -- 13.1. Mutual information and channel capacity -- 13.2. Linear codes -- 13.3. Decoding linear codes -- 13.4. Cyclic codes -- 13.5. Galois fields -- 13.6. Cryptography -- 13.7. Encryption techniques -- 13.8. Public key cryptography -- 14. Solutions to selected exercises.  aFull-text restricted to subscribers or individual document purchasers.3 aDigital imaging is now ubiquitous, from the smart phone in your pocket to state-of-the-art medical imaging and satellite imagery. Raw imaging data can be manipulated using computer algorithms to perform a range of functions from reducing noise to complex image analysis and pattern and object recognition. The result is that digital image processing has applications in almost all areas of science and engineering. Designed for a one semester course, the aim of this book is to concentrate on the principles and techniques of image processing. This second edition includes important updates to the first edition, as well as two entirely new chapters, making the book ideal for advanced students in physics and engineering.  aProfessional and scholarly.  aAlso available in print.  aMode of access: World Wide Web.  aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.  aP.K. Thiruvikraman is a professor of physics at the Birla Institute of Technology & Science with a research interest in Digital Image Processing, Pattern Recognition / Machine Vision and Coding Theory. The author has been teaching at BITS Pilani for the last 20 years. He has taught Digital Image Processing for more than a decade.  aTitle from PDF title page (viewed on December 13, 2024).  aMATLAB.995171  aImage processingxDigital techniques.964228 7aImage processing.2bicssc 7aCOMPUTERS / Image Processing.2bisacsh9971262 aInstitute of Physics (Great Britain),epublisher.917702 8iPrint version:z9780750359672z9780750359702 0aIOP (Series).pRelease 24.  aIOP ebooks.p2024 collection.4 uhttps://iopscience.iop.org/book/mono/978-0-7503-5969-6  cE-Books  c68522d68522