Color segmentation SYSTEM CONFIGURATION

Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 116

2. SYSTEM CONFIGURATION

Figure 1 shows the configuration of the system to implement user’s comamnd. An input scene is segmented and labeled by using image processing process to find the features of objects. From image segmentation and labeling result, the robot gets a features of object. It will use it to confirm tho the user when it finds some ambiguos condition. The system generate an effective and efficient dialog by processing this result according to general rule and dynamic database that obtained from specific characteristic of each features and some stored previous outputs. Figure 1. Configuration System

2.1. Color segmentation

We use robust approach of features space method for color image segmentation [6]. This method reduces the number of color in the image and divide image to several region. For getting a best result, we merge some nearest color region by using hue method in Hue Saturation and Intensity HIS color space. [7]. The HSI system separates color information of an image from its intensity information. Color information is represented by hue and saturation values, while intensity describes the brightness of an image, is determined by the amount of the light. Hue represents basic colors, and is determined by the dominant a b c Figure 2. Color segmentation result .a Original Image. b. Segmentation results c HIS result after segmentation process wavelength in the spectral distribution of light wavelengths. The formulas for hue, saturation and intensity are : ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ − − + − − + − = B G B R G R B R G R H 2 5 . arccos 1 ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ + + = 3 B G R Int 2 B G R B G R Sat + + − = , , min 3 1 3 Some threshold of hue value is determined or to remove background and merging of nearest color for achieving small number region. In this paper we fix some threshold of hue value that estimated from 0 to 360 . For example, blue is 240 , yellow is 60 , green Feature characteristics Ask and remove method Dynamic Knowledge base Input Output Color segmentation Labeling and shape detection General rule Image processing part Dialog part Comfortable Dialog for Object Detection – Rahmadi Kurnia ISSN 1858-1633 2005 ICTS 117 is 120 , red is 0 magenta is 300 etc. The result of these processes is shown by Figure 2. 2.2.Labeling and shape detection Some values are put to the image to label the similar color in one region for detecting the number of objects. The shape of object is detected by determine its size, area, surrounding by using curve ratio equation. [8]. 2 4 perimeter area S π = We get some curve ratio is specific for each standard shape objects from experiments. For example, the curve ratio of circle is 0.8, square is around 0.79, rectangular is 0.7, triangle and other irregular shape are less than 0.58

3. FEATURE CHARACTERISTICS AND GENERAL RULE