Abstract
One of the difficult tasks in the image processing field and still not solved is segmentation of object from background of 2D image accurately. Therefore, a new method has been proposed for the purpose of segmenting the object from its background for the purpose of enhancing the images and obtains characteristics of the object without the rest of the region of the image. This process is important to provide optimal classification in the process of pattern recognition. Therefore, this paper proposed the method that includes several tasks, after loading the six files of images; this work applies the segmentation al- gorithm depending on the border and the color of the object. Finally, 2D median filtering algorithm was employed to remove noisy objects of various shapes and sizes. The algo- rithm was tested on variety images, and the results are high precision. In the other words, the proposed method is able to segment the objects from the background with promising results.

1 -Introduction
Before the application of any recognition technique, it requires some manipulation to the images. This process prepares the image and improves its quality in order to eliminate irrelevant information and to enhance the selection of the important features for recog- nition, this is known as pre-processing. Image segmentation is considered the most im- portant field in image processing, which is used in many scientific fields such as pattern recognition, robotic systems, medical imaging for diagnosis, and different applications in engineering and technology [1] [2]. Therefore, one of the great challenges of computer vision is image segmentation [3], which is performed to improve the robustness of features to be extracted that assisting in the classification process. It is the process that divides the digital image into regions, which are share according to the color of object, intensity or texture [3].
Several methods have been proposed in order to recognize the objects in the image [4]. Some of authors depended on multiple threshold to seg- ment objects such as [5]. The other used the color for object segmen- tation, where the object has been classified by using artificial neural networks (ANN) [6]. Coins segmentation depended on local entropy and gray value ranges is proposed by [7]. Furthermore, image segmenta- tion is used in the medical field to divide the image into various regions to determine the tumors [2] [8] for example [2] they focused on two categories that are deformable models- and classification approaches. This approach is based on the RGB color and the border for segmenting object, as important factors for dividing the object from its background.
Therefore, this paper is structured as follows: Materials and methods are drawn in section 2. Section 3 discusses the experimental setup; analysis and evaluation the method are presented in section 4, and the conclusions are given in section 5.