Uniform Gray Scale Research Articles

The Optimization of LCD Electrooptical Behavior using MOUSE-LCD Software

Modeling Universal System of Liquid Crystal Displays (MOUSE—LCD) software is demonstrated for the calculation of LC electrooptical behavior and optimization of the new LCD prototypes. In our paper we will demonstrate the effectiveness of the software on various important examples enlisted below. 1. Birefringent color TN and STN-LCDs with a high brightness. 2. Fast LC shutters based on 3π/2 supertwist cells. 3. One-polarizer “guest-host” LCDs with a high contrast and brightness and wide viewing angles. 4. AM-LCDs with low controlling voltages and good gray scale uniformity. 5. Subtractive STN-LCD color system with improved B/W and color contrast. The present version of MOUSE-LCD software includes the new effective method of optical calculations (modified 2×2 Jones matrix approach) and allows for a “back flow” effect in a dynamic response of LC layer.

Image segmentation using evolutionary computation

Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented.

Weed Detection in Lawn Field Based on Gray-Scale Uniformity.

It is necessary that the weed area is discriminated from lawn area if selective spraying is applied to maintain a lawn field. However, both weed and lawn usually have similar green color in summer. In this paper, a color independent method using gray-scale uniformity in image was proposed for detecting the weed area. This method works based on different levels of uniformity for weed and lawn surfaces and could precisely detect only the weed leaves. The analysis results showed that a weed could be detected if its leaves are at least 5 pixels wide in image, while its length is enough to generate a minimum size blob. It was also found that the image had to be acquired at a short camera distance so that the soil among the lawn was visible and the lawn area had less uniformity. After weed leaf detection, a morphological image analysis was performed on a group the adjacent blobs as result of leaf detection. It was observed that the morphological image analysis was useful in locating a representative center-point for grouped blobs in correspondence to main root location of the weeds.

Feasibility of weed detection in lawn field based on gray-scale uniformity

Feasibility of weed detection in lawn field based on gray-scale uniformity