Lane detection system for day vision using altera DE2

The active safety systems used in automotive field are largely exploiting lane detection technique for warning the vehicle drivers to correct any unintended road departure and to reach fully autonomous vehicles. Due to its ability, to be programmed, to perform complex mathematical functions and its characterization of high speed processing, Field Programmable Gate Array (FPGA) could cope with the requirement of lane detection implementation and application. In the present work, lane detection is implemented using FPGA for day vision. This necessitates utilization of image processing techniques like filtering, edge detection and thresholding. The lane detection is performed by firstly capturing the image from a video camera and converted to gray scale. Then, a noise filtering process for gray image is performed using Gaussian and average filter. Methods from first and second order edge detection techniques have been selected for the purpose of lane edge detection. The effect of manually changing the threshold level on image enhancement has been examined. The results showed that raising threshold level would better enhance the image. The type of FPGA device used in the present work is Altera DE2. Firstly, the version DE2 Cyclone II start with (11xxxxxx-xxxx) together with Genx camera has been used. This camera supports both formats NTSC and PAL, while the above version of FPGA backups only NTSC format. The software of lane detection is designed and coded using Verilog language

Recognition of the unripe strawberry by using color segmentation techniques

In this paper, the efficiency comparison is displayed for recognize the unripe strawberry fruit using two different methods; color thresholding and K-means clustering. Color thresholding technique includes the following steps: color thresholding, morphological enhancement and draw mark for tracking. K-means clustering comprises filtering, transform the image to L*a*b color space, binary thresholding and extract the desired strawberry region. The results explained that color thresholding gets the better of K-means in the aspect of accuracy, effectiveness, and speed of code implementation. Both interested parties are written using MATLAB (R2018a) language