Direct Linear Transformation: Practical Applications and Techniques in Computer Vision

Β· Computer Vision αžŸαŸ€αžœαž—αŸ…αž‘αžΈ 42 Β· One Billion Knowledgeable
αžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž…
169
αž‘αŸ†αž–αŸαžš
αž˜αžΆαž“αžŸαž·αž‘αŸ’αž’αž·
αž€αžΆαžšαžœαžΆαž™αžαž˜αŸ’αž›αŸƒ αž“αž·αž„αž˜αžαž·αžœαžΆαž™αžαž˜αŸ’αž›αŸƒαž˜αž·αž“αžαŸ’αžšαžΌαžœαž”αžΆαž“αž•αŸ’αž‘αŸ€αž„αž•αŸ’αž‘αžΆαžαŸ‹αž‘αŸ αžŸαŸ’αžœαŸ‚αž„αž™αž›αŸ‹αž”αž“αŸ’αžαŸ‚αž˜

αž’αŸ†αž–αžΈαžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αž“αŸαŸ‡

What is Direct Linear Transformation

Direct linear transformation, also known as DLT, is an algorithm that solves a set of variables by using a set of similarity relations as the working set. In the field of projective geometry, this kind of relation is encountered quite frequently. Examples that are applicable to real-world situations include homographies and the relationship between three-dimensional points in a scene and their projection onto the image plane of a pinhole camera.


How you will benefit


(I) Insights, and validations about the following topics:


Chapter 1: Direct linear transformation


Chapter 2: Linear map


Chapter 3: Linear subspace


Chapter 4: Cholesky decomposition


Chapter 5: Invertible matrix


Chapter 6: Quadratic form


Chapter 7: Homogeneous function


Chapter 8: Kernel (linear algebra)


Chapter 9: PlΓΌcker coordinates


Chapter 10: TP model transformation in control theory


(II) Answering the public top questions about direct linear transformation.


(III) Real world examples for the usage of direct linear transformation in many fields.


Who this book is for


Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Direct Linear Transformation.

αžœαžΆαž™αžαž˜αŸ’αž›αŸƒαžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αž“αŸαŸ‡

αž”αŸ’αžšαžΆαž”αŸ‹αž™αžΎαž„αž’αŸ†αž–αžΈαž€αžΆαžšαž™αž›αŸ‹αžƒαžΎαž‰αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ”

αž’αžΆαž“β€‹αž–αŸαžαŸŒαž˜αžΆαž“

αž‘αžΌαžšαžŸαž–αŸ’αž‘αž†αŸ’αž›αžΆαžαžœαŸƒ αž“αž·αž„β€‹αžαŸαž”αŸ’αž›αŸαž
αžŠαŸ†αž‘αžΎαž„αž€αž˜αŸ’αž˜αžœαž·αž’αžΈ Google Play Books αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Android αž“αž·αž„ iPad/iPhone αŸ” αžœαžΆβ€‹αž’αŸ’αžœαžΎαžŸαž˜αž€αžΆαž›αž€αž˜αŸ’αž˜β€‹αžŠαŸ„αž™αžŸαŸ’αžœαŸαž™αž”αŸ’αžšαžœαžαŸ’αžαž·αž‡αžΆαž˜αž½αž™β€‹αž‚αžŽαž“αžΈβ€‹αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€β€‹ αž“αž·αž„β€‹αž’αž“αž»αž‰αŸ’αž‰αžΆαžαž±αŸ’αž™β€‹αž’αŸ’αž“αž€αž’αžΆαž“αž–αŸαž›β€‹αž˜αžΆαž“αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αž αž¬αž‚αŸ’αž˜αžΆαž“β€‹αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžβ€‹αž“αŸ…αž‚αŸ’αžšαž”αŸ‹αž‘αžΈαž€αž“αŸ’αž›αŸ‚αž„αŸ”
αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšβ€‹αž™αž½αžšαžŠαŸƒ αž“αž·αž„αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžš
αž’αŸ’αž“αž€αž’αžΆαž…αžŸαŸ’αžŠαžΆαž”αŸ‹αžŸαŸ€αžœαž—αŸ…αž‡αžΆαžŸαŸ†αž‘αŸαž„αžŠαŸ‚αž›αž”αžΆαž“αž‘αž·αž‰αž“αŸ…αž€αŸ’αž“αž»αž„ Google Play αžŠαŸ„αž™αž”αŸ’αžšαžΎαž€αž˜αŸ’αž˜αžœαž·αž’αžΈαžšαž»αž€αžšαž€αžαžΆαž˜αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžαž€αŸ’αž“αž»αž„αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšαžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ”
eReaders αž“αž·αž„β€‹αž§αž”αž€αžšαžŽαŸβ€‹αž•αŸ’αžŸαŸαž„β€‹αž‘αŸ€αž
αžŠαžΎαž˜αŸ’αž”αžΈαž’αžΆαž“αž“αŸ…αž›αžΎβ€‹αž§αž”αž€αžšαžŽαŸ e-ink αžŠαžΌαž…αž‡αžΆβ€‹αž§αž”αž€αžšαžŽαŸαž’αžΆαž“β€‹αžŸαŸ€αžœαž—αŸ…αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€ Kobo αž’αŸ’αž“αž€αž“αžΉαž„αžαŸ’αžšαžΌαžœβ€‹αž‘αžΆαž‰αž™αž€β€‹αž―αž€αžŸαžΆαžš αž αžΎαž™β€‹αž•αŸ’αž‘αŸαžšαžœαžΆαž‘αŸ…β€‹αž§αž”αž€αžšαžŽαŸβ€‹αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ” αžŸαžΌαž˜αž’αž“αž»αžœαžαŸ’αžαžαžΆαž˜β€‹αž€αžΆαžšαžŽαŸ‚αž“αžΆαŸ†αž›αž˜αŸ’αž’αž·αžαžšαž”αžŸαŸ‹αž˜αž‡αŸ’αžˆαž˜αžŽαŸ’αžŒαž›αž‡αŸ†αž“αž½αž™ αžŠαžΎαž˜αŸ’αž”αžΈαž•αŸ’αž‘αŸαžšαž―αž€αžŸαžΆαžšβ€‹αž‘αŸ…αž§αž”αž€αžšαžŽαŸαž’αžΆαž“αžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αžŠαŸ‚αž›αžŸαŸ’αž‚αžΆαž›αŸ‹αŸ”

αž”αž“αŸ’αžαžŸαŸŠαŸαžšαžΈ

αž…αŸ’αžšαžΎαž“αž‘αŸ€αžαžŠαŸ„αž™ Fouad Sabry

αžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€β€‹αžŸαŸ’αžšαžŠαŸ€αž„αž‚αŸ’αž“αžΆ