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Computer Vision: Models, Learning, and Inference

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This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. - Covers cutting-edge techniques, including graph cuts, machine learning, and multiple view geometry. - A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition, and object tracking. - More than 70 algorithms are described in sufficient detail to implement. - More than 350 full-color illustrations amplify the text. - The treatment is self-contained, including all of the background mathematics. - Additional resources at www.computervisionmodels.com.


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This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. - Covers cutting-edge techniques, including graph cuts, machine learning, and multiple view geometry. - A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition, and object tracking. - More than 70 algorithms are described in sufficient detail to implement. - More than 350 full-color illustrations amplify the text. - The treatment is self-contained, including all of the background mathematics. - Additional resources at www.computervisionmodels.com.

30 review for Computer Vision: Models, Learning, and Inference

  1. 5 out of 5

    Dani Mexuto

    Xa o acabei. O libro comeza coa necesaria introdución en probabilidade. Isto os telecos temos claro que as descripcións de ruido hainas que explicar primeiro no dominio no tempo e despois no da frecuencia como facía P. Peebles por exemplo. Se colles as descripcións que che fan falta e as presentas sen contexto aplicadas a un sensor como é a cámara é imposíbel que te enteres do que estás facendo. Isto MAL. Na segunda parte o libro asume que o problema da visión por "computadore" é un problema de m Xa o acabei. O libro comeza coa necesaria introdución en probabilidade. Isto os telecos temos claro que as descripcións de ruido hainas que explicar primeiro no dominio no tempo e despois no da frecuencia como facía P. Peebles por exemplo. Se colles as descripcións que che fan falta e as presentas sen contexto aplicadas a un sensor como é a cámara é imposíbel que te enteres do que estás facendo. Isto MAL. Na segunda parte o libro asume que o problema da visión por "computadore" é un problema de machine learning, pattern recognition (como cando saben recoñecerche que eres un patrón) O problema desta idea, dende o meu punto de vista, é que esqueces toda a parte do problema físico, proxección da luz, vision geometry e todo iso ... pero bueno entendo que o libro se teña que especilizar. Aquí o traballo sintetiza a base desta técnica cunha enorme cantidade de conceptos ben resumidos ata chegar aos classification models. Cal é o problema? Que este non é un libro técnico que invite a probar por ti mesmo o que estas aprendendo, eu vin como unha sansoniana tarefa coller un destes párrafos e intentar escribilo en python para ver que saía por min mesmo. E acabei por non facelo. Entón eu non entendo a utilidade dun libro científico con mala narrativa matemática e que aínda por riba non axuda a aplicar o que explica. Por moi sintético e correcto que este todo o explicado. Doulle cinco estrelas aos herois anónimos que escriben tutoriais de python e a xente que che explica probabilidade cunha narrativa matemática. A este libro dous.

  2. 5 out of 5

    Joe

    Great read! Wonderful content and very informed way of presenting it.

  3. 5 out of 5

    juan carlos aguilar lópez

  4. 4 out of 5

    Michael Xu

  5. 4 out of 5

    Dennis Mabrey

  6. 5 out of 5

    Michael Bao

  7. 4 out of 5

    Martin Marek

  8. 5 out of 5

    Odysseas Bouzos

  9. 5 out of 5

    Marc

  10. 4 out of 5

    Rey

  11. 5 out of 5

    Poly74yu

  12. 5 out of 5

    pc

  13. 5 out of 5

    Mario García

  14. 4 out of 5

    Jan Rychter

  15. 4 out of 5

    Ivan

  16. 5 out of 5

    Subhajit Das

  17. 5 out of 5

    Nile Geisinger

  18. 4 out of 5

    Kevin Mcguinness

  19. 4 out of 5

    Maysa-maria Peterson lach

  20. 5 out of 5

    James

  21. 4 out of 5

    Sten Sootla

  22. 5 out of 5

    Andreas Frangopoulos

  23. 5 out of 5

    Tim Tate

  24. 4 out of 5

    Justin Fry

  25. 5 out of 5

    Ken Siebert

  26. 5 out of 5

    Daniyar Turmukhambetov

  27. 5 out of 5

    Koki Saitoh

  28. 5 out of 5

    Xinru Zhang

  29. 5 out of 5

    Zach

  30. 5 out of 5

    Thomas Fan

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