Accurate Feature Extraction and Control Point Correction for Camera Calibration with a Mono-Plane Target

The paper addresses two problems related to 3D camera calibration using a single mono-plane calibration target with circular control marks. The first problem is how to compute accurately the locations of the features (ellipses) in images of the target. Since the structure of the control marks is known beforehand, we propose to use a shape-specific searching technique to find the optimal locations of the features. Our experiments have shown this technique generates more accurate feature locations than the state-of-the-art ellipse extraction methods. The second problem is how to refine the control mark locations with unknown manufacturing errors. We demonstrate in a case study, where the control marks are laser printed on a A4 paper, that the manufacturing errors of the control marks can be compensated to a good extent so that the remaining calibration errors are reduced significantly. 1

Noise Tolerant Descriptor for Texture Classification

International audienceAmong many texture descriptors, the LBP-based representation emerged as an attractive approach thanks to its low complexity and effectiveness. Many variants have been proposed to deal with several limitations of the basic approach like the small spatial support or the noise sensitivity. This paper presents a new method to construct an effective texture descriptor addressing those limitations by combining three features: (1) a circular average filter is applied before calculating the Complemented Local Binary Pattern (CLBP), (2) the histogram of CLBPs is calculated by weighting the contribution of every local pattern according to the gradient magnitude, and (3) the image features are calculated at different scales using a pyramidal framework. An efficient calculation of the pyramid using integral images, together with a simple construction of the multi-scale histogram based on concatenation, make the proposed approach both fast and memory efficient. Experimental results on different texture classification databases show the good results of the method, and its excellent noise robustness, compared to recent LBP-based methods

Multi-feature Fusion Using SIFT and LEBP for Finger Vein Recognition

In this paper, multi-feature fusion using Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP) was proposed to obtain a feature that could resist degradation problems such as scaling, rotation, translation and varying illumination conditions. SIFT feature had a capability to withstand degradation due to changes in the condition of the image scale, rotation and translation. Meanwhile, LEBP feature had resistance to gray level variations with richer and discriminatory local characteristics information. Therefore the fusion technique is used to collect important information from SIFT and LEBP feature.The resulting feature of multi-feature fusion using SIFT and LEBP feature would be processed by Learning Vector Quantization (LVQ) method to determine whether the testing image could be recognized or not. The accuracy value could achieve 97.50%, TPR at 0.9400 and FPR at 0.0128 in optimum condition.  That was a better result than only use SIFT or LEBP feature