Research on pose estimation for stereo vision measurement system by an improved method: uncertainty weighted stereopsis pose solution method based on projection vector

Camera pose estimation algorithm involving weighted measurement uncertainty of feature points based on rotation parameters

To solve the perspective-n-point problem in visual measurement, we present a camera pose estimation algorithm involving weighted measurement uncertainty based on rotation parameters. The method does not involve the depth factor, and the objective function is converted into a least-squares cost function that contains three rotation parameters. Furthermore, the noise uncertainty model enables a more accurate estimated pose, which can be directly calculated without initial values. Experimental results prove the high accuracy and good robustness of the proposed method. In the space of 1.5m×1.5m×1.5m, the maximum estimation errors of rotation and translation are better than 0.04° and 0.2%.

Online stereo vision measurement based on correction of sensor structural parameters

Vibration can easily affect the structure of long baseline binocular vision sensors, resulting in changes in the external parameters of the binocular calibration model and the failure of measurement method. This paper presents an online stereo vision measurement based on correction of sensor structural parameters. The flexible structure model based on calibration model and iterative gradient descent nonlinear optimization model based on 3D redundant information are established. The optimal estimation of external parameters and object position measurement are realized according to multi-information constraints. Experiments show that this method can effectively solve the measurement failure caused by vibration in stereo vision measurement.

Optical character recognition (OCR) using partial least square (PLS) based feature reduction: an application to artificial intelligence for biometric identification

Purpose

In artificial intelligence, the optical character recognition (OCR) is an active research area based on famous applications such as automation and transformation of printed documents into machine-readable text document. The major purpose of OCR in academia and banks is to achieve a significant performance to save storage space.

Design/methodology/approach

A novel technique is proposed for automated OCR based on multi-properties features fusion and selection. The features are fused using serially formulation and output passed to partial least square (PLS) based selection method. The selection is done based on the entropy fitness function. The final features are classified by an ensemble classifier.

Findings

The presented method was extensively tested on two datasets such as the authors proposed and Chars74k benchmark and achieved an accuracy of 91.2 and 99.9%. Comparing the results with existing techniques, it is found that the proposed method gives improved performance.

Originality/value

The technique presented in this work will help for license plate recognition and text conversion from a printed document to machine-readable.