Farshid PirahanSiah

Character recognition based on global feature extraction

This paper presents a enhanced feature extraction method which is a combination and selected of two feature extraction techniques of Gray Level Co occurrence Matrix (GLCM) and Edge Direction Matrixes (EDMS) for character recognition purpose. It is apparent that one of the most important steps in a character recognition system is selecting a better feature extraction technique, while the variety of method makes difficulty for finding the best techniques for character recognition. The dataset of images that has been applied to the different feature extraction techniques includes the binary character with different sizes. Experimental results show the better performance of proposed method in compared with GLCM and EDMS method after performing the feature selection with neural network, bayes network and decision tree classifiers

Adaptive image segmentation based on peak signal-to-noise ratio for a license plate recognition system

The objective of this paper is to propose an adaptive threshold method based on peak signal to noise ratio (PSNR). Nowadays, PSNR has been widely used as stopping criteria in multi level threshold method for segmenting images. Alternatively, we apply the PSNR as criteria to find the most suitable threshold value. We evaluate this proposed method on license plate recognition application. At the same time, we compare this proposed algorithm with multi-level and multi-threshold methods as the benchmark. Via the proposed technique, it could relatively change according to environment such as when there is a high or low contrast situation.

License plate recognition with multi-threshold based on entropy

Among all the existing segmentation techniques, thresholding technique is one of the most popular one due to its simplicity, robustness and accuracy. Multi-thresholding is an important operation in many analyses which is used in many applications. Selecting correct thresholds to get better result is a critical issue. In this research, a multilevel thresholding method is proposed based on combination of maximum entropy. The maximum entropy thresholding algorithm selects several threshold values by maximizing the cross entropy between the original image and the segmented image. This method can effectively integrate partial range of the image histogram. The proposed algorithm is compared with single thresholding method based on maximum entropy and multilevel thresholding method The proposed multi thresholding method is tested on license plate application. From the experiment, multi-threshold method further improved to increase the segmentation accuracy in the future.

Comparison single thresholding method for handwritten images segmentation

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, separating objects from background, decreasing the capacity of data consequently increases speed. Handwritten recognition is one of the important issues, which have various applications in mobile devices. Peak signal noise ratio (PSNR) is one of the methods for measurement the quality of images. Our proposed method applies peak signal noise ratio (PSNR) as one of the indicator to segment the images. We also compare our proposed method with other existing methods and the results are comparable. This algorithm can be optimized to increase the performance. The result indicates that the proposed method works in average handwritten images because the PSNR value of proposed method is better than other methods.

Camera calibration for multi-modal robot vision based on image quality assessment

Multi-dimension robot vision in autonomous humanoid robot is still an open issue as it performs less effective when dealing with different environments. Robot vision becomes more challenging as image quality degrades. Unlike human vision, current robot vision is yet to calibrate automatically when image quality changes abruptly. This may result in poor accuracy due to false negative input data points, and the user needs recapturing new calibration images to compensate. Therefore, this study emphasizes on proposing an automatic calibration for multimodal robot vision based on quality measures. We organize our research methodology into three steps. First, we capture a series of image patterns by using our calibration pattern equipment. Second, we employ Image Quality Assessment Function (IQAF) that includes PSNR and SSIM to measure points of image abruption simultaneously. In the experiment, we observed differences between real distance and computed distance and compared them to those of the selfcollected original database and the blur database.