Hilditch Algorithm Research Articles

Neural Net Approach For Image Thinning

Thinning is the operation that seeks to reduce a connected region of pixels of a given property set to a small size. One of the most applications of thinning is the image recognission. In this paper, we proposed a new algorithm for thinning an image using Neural Net approach called (Thinning Back- propagation algorithm, TBP), the results show that the operation considered including Zhang Suen algorithm, Hilditch's algorithm. Applying Zhang Suen and Hilditch's algorithms showed that the output of the Hilditch's algorithm is better from the presence of shape accuracy point of view. The proposed Thinning Back-propagation (TBP) algorithm is superior to other algorithms; the results show that the Hilditch's algorithm gives somewhat better results than Zhang Suen algorithm, therefore the proposed TBP has been implemented depending on the Hilditch's algorithm. The accuracy of the TBP is equivalent to Hilditch's algorithm but it is a little bit faster than Hilditch's algorithm. Experimental results are presented to compare the performance of the algorithms.

A novel quantum binary images thinning algorithm: A quantum version of the Hilditch's algorithm

Skeletonization or thinning process is an important step in pre-processing phase and it is crucial process for many applications such as pattern recognition, data compression/storage, OCR, etc. In classical information, there are several algorithms proposed for the aim of image skeletonization or thinning. One of the widely used algorithm for this purpose is Hilditch's algorithm. In this paper a novel quantum version of Hilditch's algorithm for quantum image thinning is proposed where the novel enhanced quantum representation of digital images (NEQR model) is employed for representation of the quantum images. The proposed algorithm can be considered as the first quantum image thinning algorithm used in any schemes proposed in the growing field of quantum image processing.

English

Computer vision is the science that studies how computers can recognize objects observed, covering the fields of image processing can be applied in human gait analysis. One gait recognition process can be studied through the detection of human body shape in the form of a human skeleton. Framework of the human body shape can be obtained through some digital image processing techniques. These techniques are applied to a computer program in order to make the detection of human body skeleton. Detection program skeleton composed of several processes, ie capturing, background subtraction, filtering, thresholding, dilation, erosion, invert and skeletonizing using Hilditch algorithm. The data used as input in the form of video with the extension. Avi. Each video will take 32 images of each video. Then the image will be processed to get the image of a human skeleton. But the success rate of the detection process skeleton is not necessarily 100%, it is due to various factors. Among them due to poor video quality and low light so it does not get a perfect shape skeleton.

Arabic Character Recognition System Development

Abstract We develop Arabic Optical Character Recognition (AOCR) system that has five stages: preprocessing, segmentation, thinning, feature extraction, and classification. In preprocessing stage, we compare two skew estimation algorithms i.e. skew estimation by image moment and by skew triangle. We also implemented binarization and median filter. In thinning stage, we use Hilditch thinning algorithm incorporated by two templates, one to prevent superfluous tail and the other one to remove unnecessary interest point. In segmentation stage, line segmentation is done by horizontal projection cross verification by standard deviation, sub-word segmentation is done by connected pixel components, and letter segmentation is done by Zidouri algorithm. In the feature extraction stage, 24 features are extracted. The features can be grouped into three groups: main body features, perimeter- skeleton features, and secondary object features. In the classification stage, we use decision tree that generated by C4.5 algorithm. Functionality test showed that skew estimation using moment is more accurate than using skew triangle, median filter tends to erode the letter shape, and template addition into Hilditch algorithm gives a good result. Performance test yield these result. Line segmentation had 99.9% accuracy. Standard deviation is shown can reduce over-segmentation and quasi-line. Letter segmentation had 74% accuracy, tested on six different fonts. Classification components had 82% accuracy, tested by cross validation. Unfortunately, overall performance of the system only reached 48.3%.