Researcher, 2009;1(5) http://www.sciencepub.net/researcher researcher135@gmail.com 62 Image Segmentation based Quality Analysis of Agricultural Products using Emboss Filter and Hough Transform in Spatial Domain Mamta Juneja 1 , Parvinder Singh Sandhu 2 1 & 2: RBIEBT, Kharar under Punjab Technical University, Punjab India er_mamta@yahoo.com Abstract: Very few technologies developed less than 30 years ago have permeated our lives as much as the image processing. Development in the field of image processing especially in its field image segmentation which is used to extract regions of interest has proven wonders in various applications like Signature verification, Face recognition, Thumb impression verification, Automatic character recognition, Industrial machine vision for assembly and inspection etc. But the potential of image segmentation in the field of agriculture is yet to be exploited for the daily use. In this paper efforts are focused on the role of image segmentation in the field of agriculture to analyze the fruit quality on the basis of its color, size and weight. In the proposed algorithm, the desired conclusions/analysis can be made by comparing various inputs received (color, size, weight) with predefined parameters, which can be further used for grading, packaging and chopping of fruits in agriculture. [Researcher. 2009;1(5):62-68]. (ISSN: 1553-9865). Keywords: Spatial domain, Frequency domain, Linear spatial filtering, Color models, Histogram, segmentation, Edge detection, Prewitt and Emboss filter, Hough transform 1. Introduction Digital Images are electronic snapshots taken of a scene or scanned from documents, such as photographs, manuscripts, printed texts, and artwork. The digital image is sampled and mapped as a grid of dots or picture elements (pixels). Each pixel is assigned a tonal value (black, white, shades of gray or color), which is represented in binary code (zeros and ones). The binary digits ("bits") for each pixel are stored in a sequence by a computer and often reduced to a mathematical representation [1]. An image may be considered to contain sub-images sometimes referred to as regions-of- interest, ROIs, or simply regions. [2]. In searching the region of interest, the Edge detection is one of the most commonly used technique in image analysis, and there are probably more algorithms in the literature for enhancing and detecting edges than any other single subject [3] and [4].To extract features from digital images, it is useful to be able to find simple shapes - straight lines, circles, ellipses and the like - in images. In order to achieve this goal, one must be able to detect a group of pixels that are on a straight line or a smooth curve [5] [6] and [7]. 1.2. Color Models Each color model is oriented towards either specific hardware (RGB, CMY, YIQ), or image processing applications (HSI). 1.1.1 RGB Color Model The RGB (Red, Green, Blue) color model is an especially important one in digital image processing because it is used by most digital imaging devices (e.g., monitors and color cameras). In the RGB model, a color is expressed in terms that define the amounts of Red, Green and Blue light it contains. Figure 1: RGB Color Model [8] 1.1.2 HSI Color Model The HSI (Hue, Saturation, Intensity) color model describes a color in terms of how it is perceived by the human eye. This is useful when processing images to compare two colors, or for changing a color from one to another. The HSI model is also a more useful model for