Implicit camera calibration by using resilient neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Adaptive implicit-camera calibration in photogrammetry using anfis
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Multisensory visual servoing by a neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Camera calibration with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using Uncorrupted Neighborhoods of the Pixels for Impulsive Noise Suppression With ANFIS
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
A Neurovision System can be defined as an artificial tool that sees our physical world. The purpose of this paper is to show a novel tool to design a 3D artificial vision system based on Resilient Neural Networks. Camera Calibration (CC) is a fundamental issue for Computational-Vision. Classical CC methods comprise of taking images of objects with known geometry, extracting the features of the objects from the images, and minimizing their 3D backprojection errors. In this paper, a novel implicit-CC model based on Resilient Neural Networks, CR, has been introduced. The CR is particularly useful for 3D reconstruction of the applications that do not require explicitly computation of physical camera parameters in addition to the expert knowledge. The CR supports intelligent-photogrammetry, photogrammetron. In order to evaluate the success of the proposed implicit-CC model, the 3D reconstruction performance of the CR has been compared with two different well-known implementations of the Direct Linear Transformation (DLT). The proposed method is also robust sufficiently for dealing with different cameras because it is capable of fusion of the image coordinates sourced from different cameras once the neural network has been trained.