In this article, a system for recognizing Persian sign language alphabets is presented. This system is able to recognize 32 hand postures for Persian alphabets and translate it into Persian text. For this purpose, images of hand positions have been considered for each l More
In this article, a system for recognizing Persian sign language alphabets is presented. This system is able to recognize 32 hand postures for Persian alphabets and translate it into Persian text. For this purpose, images of hand positions have been considered for each letter of the alphabet. The database contains 600 images of different people taken by a digital camera. We have transferred all the image data to the binary domain and resized them with a single scale. Image data preprocessing includes image cropping and noise removal. After pre-processing, 3 algorithms are proposed to extract features. The proposed algorithms include the image segmentation algorithm, the distance between border contour points and the center of gravity algorithm, and Radon transformation. Algorithm of the distances between the border contour points and the center of gravity shows how the points are placed on the peripheral curve of the hand in relation to each other and to the center of gravity, and therefore provides suitable structural information for describing states. The next algorithm is based on image segmentation. In this algorithm, the ratio of the number of white pixels to the total number of pixels is calculated in each of the areas. In Radon transformation, in addition to obtaining the general information of the image in each of the modes, we have increased the accuracy of the detection by using the proposed method and discarding additional information. The proposed methods also provided good results on other image databases.
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One of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in More
One of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in an image So that these pixels, the same qualities have with each other And the pixels adjacent parts, have different characteristics. The most important feature used in image segmentation, colors and features. In monochrome images, the gray level is considered as properties But color images, different color spaces used as a color feature. In this study, the color and texture features for image segmentation is considered. Clustering-based methods of are used in image segmentation methods and Gaussian function is similar measure in clustering images. Spectral clustering requires has high computational cost. To save time and accelerate the segmentation of images Using clustering with Super pixels will achieve optimal results And to achieve reliable results approximate and fuzzy algorithm is used. The proposed algorithm is applied on several standard image And the evaluation criteria,Evaluated and evaluated by the indicators are evaluated and compared. The results of the experiments were compared to other fragmentation methods, suggesting a 3.4% superiority in the segmentation accuracy of the proposed algorithm, and all the evaluation indicators of the study have increased to a satisfactory level.
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As the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly us More
As the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. This processing comprises two fundamental and critical steps towards content analysis and image understanding i.e. image segmentation and classification. This paper proposes a robust object based segmentation algorithm using multi-resolution analysis technique and object based supervised image classification using modified cloud basis functions (CBFs) neural network algorithm to identify road features from high resolution satellite remotely sensed images .
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