CT image quality influence on different material Edge Response Functions for accurate metrological applications

The main objective of the proposed work was to analyse the influence of magnification and focal spot size scan settings on X-ray computed tomography (CT) measurements results under commercial threshold-based algorithms. The relationship between spatial resolution and contrast sensitivity in CT scans of different materials and the accuracy of the resulting CT measurement results is discussed. For that purpose, Aluminium, Copper, Inconel 718 and Titanium disk phantoms were scanned. Preliminary measurements showed that deviations can increase up to 0.48% when the scanning magnification was increased while, for a given magnification, the decrease of a focus size from 1mm to 0.4mm slightly improves the differences up to 0.15%, being negligible at low magnifications. Unsharpness (U T ) and contrast-to-noise ratio (CNR) were calculated for each scanning conditions according to standard ASTM E1695 – 20. A new image quality indicator that includes the combined effect of the U T and CNR was proposed in order to relate measurement error with the image quality. The indicator proves that the influence of CNR is much higher than influence of U T on the CT measurements.

Novel Descriptors for Effective Recognition of Face and Facial Expressions

In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.

Inverse-Scaled Lanczos Filtering for Image Sharpening

The edge response in retinal image is the first step for human vision recognizing the outside world. A variety of receptive field models for describing the impulse response have been proposed. Which satisfies the uncertain principle? occupied the interest from a point of minimizing the product (Δx)(Δ w) both in spatial and spectral. Among the typical edge response models, finally Gabor function and 2nd. Gaussian Derivative GD2 remained as strong candidates. While famous D. Marr and R. Young support GD2, many vision researchers prefer Gabor. The retinal edge response model is used for image sharpening.<br/> Different from the conventional image sharpening filters, this paper proposes a novel image sharpening filter by modifying the Lanczos resampling filter. The Lanczos filter is used for image scaling to resize digital images. Usually it works to interpolate the discrete sampled points like as a kind of smoothing filter not as sharpening. The Lanczos kernel is given by the product of sampling Sinc function and the scaled Sinc function. The scaled Sinc function expanded by the scale "s" plays a role of window function. The author noticed that the inverse scaling of Lanczos window can be used not for smoothing but for sharpening filter.<br/> This paper demonstrates how the proposed model works effectively in comparison with Gabor and GD2.