Visual to tactile conversion of vector graphics.

Methods to automatically convert graphics into raised-line images have been recently investigated. In this paper, concepts from previous research are extended to the vector graphics case, producing tactile pictures in which important features are emphasized. The proposed algorithm extracts object boundaries and employs a classification process, based on a graphic's hierarchical structure, to determine critical outlines. A single parameter is introduced into the classification process, enabling users to tailor graphics to their own preferences. The resulting outlines are printed using a Braille printer to produce tactile output. Critical outlines are embossed with raised dots of highest height while other lines and details are embossed with a lower height. Psychophysical experiments including discrimination, identification, and comprehension are utilized to evaluate and compare the proposed algorithm. Results indicate that the proposed method outperforms other methods in all three considered tasks. The results also show that emphasizing important features significantly increases comprehension of tactile graphics, validating the proposed method's effectiveness in conveying visual information.

Quadratic weighted median filters for edge enhancement of noisy images.

Quadratic Volterra filters are effective in image sharpening applications. The linear combination of polynomial terms, however, yields poor performance in noisy environments. Weighted median (WM) filters, in contrast, are well known for their outlier suppression and detail preservation properties. The WM sample selection methodology is naturally extended to the quadratic sample case, yielding a filter structure referred to as quadratic weighted median (QWM) that exploits the higher order statistics of the observed samples while simultaneously being robust to outliers arising in the higher order statistics of environment noise. Through statistical analysis of higher order samples, it is shown that, although the parent Gaussian distribution is light tailed, the higher order terms exhibit heavy-tailed distributions. The optimal combination of terms contributing to a quadratic system, i.e., cross and square, is approached from a maximum likelihood perspective which yields the WM processing of these terms. The proposed QWM filter structure is analyzed through determination of the output variance and breakdown probability. The studies show that the QWM exhibits lower variance and breakdown probability indicating the robustness of the proposed structure. The performance of the QWM filter is tested on constant regions, edges and real images, and compared to its weighted-sum dual, the quadratic Volterra filter. The simulation results show that the proposed method simultaneously suppresses the noise and enhances image details. Compared with the quadratic Volterra sharpener, the QWM filter exhibits superior qualitative and quantitative performance in noisy image sharpening.