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  • Bài trích
  • Ký hiệu PL/XG: 006.37 P954
    Nhan đề: Computer vision :
DDC 006.37
Tác giả CN Prince, Simon J. D.
Nhan đề Computer vision : models, learning, and inference / Simon J D Prince
Thông tin xuất bản New York : Cambridge University Press, 2012
Mô tả vật lý 18 p.
Tóm tắt "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. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"
Từ khóa tự do Computers and IT.
Từ khóa tự do Computer vision.
Từ khóa tự do COMPUTERS -- Computer Graphics.
Địa chỉ Thư Viện Đại học Nguyễn Tất Thành
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008201116s2012 nyu eng
0091 0
020 |a9781107011793
039|a20201210132434|btainguyendientu|y20201116144727|znghiepvu
040 |aNTT
041 |aeng
044 |anyu
082 |a006.37|bP954|223
100 |aPrince, Simon J. D.
245 |aComputer vision : |bmodels, learning, and inference / |cSimon J D Prince
260 |aNew York : |bCambridge University Press, |c2012
300 |a18 p.
520 |a"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. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"
541 |aKhoa CNTT tặng
653 |aComputers and IT.
653 |aComputer vision.
653 |aCOMPUTERS -- Computer Graphics.
690 |aKhoa Công nghệ Thông tin
852 |aThư Viện Đại học Nguyễn Tất Thành
890|a0|b0|c0|d0
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