जर्नल ऑफ़ कंप्यूटर साइंस एंड सिस्टम्स बायोलॉजी

A tendency of the eye to become myopic with long hours focusing at a near distance has been reported often [1-8]. Myopia development, as any refractive development, is described by a first order feedback system. A first order feedback system is defined by its transfer function F(s) = 1/(1+ks) [1,2]. This function anticipates an exponential development of refractive state and the effect of lenses. Near work is myopizing, as it is equivalent to wearing a negative lens. Using a digital computer, first-order equations have been solved previously to describe and predict myopia progression [1,3]. An analogue circuit can simulate myopia progression vs. time R(t) because the response of the feedback system is the same as the capacitor voltage in a R-C (Resistor-Capacitor) circuit, as shown in Figure 1. When near work is involved a negative square-wave represents the daily accommodative demand as represented in the inset in Figure1[3]. The R-C circuit solves the problem without any computations.

There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1-norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars.

A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost.