CHAPTER 1 INTRODUCTION 1.1 PROBLEM BACKGROUND Egg is considered as one of the most typical food stuff in the country due to its affordability and high nutritive value. Egg’s size is a very important quality parameter for the consumers’ evaluation preferences. Consumers would want eggs with accurate designated size as much as possible. Correct size classification is also useful for the people in a poultry business in terms of sorting‚ packaging planning and in imposing the price
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with additive noise. The filter consists of two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of membership functions. The filter can be applied iteratively and effectively reduce heavy noise. In particular‚ the shape of the membership functions is adapted according to the remaining
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E-Paper Technology Abstract of E-Paper Technology E-paper is a revolutionary material that can be used to make next generation I‚ electronic displays. It is portable reusable storage and display medium that look like paper but can be repeatedly written one thousands of times. These displays make the beginning of a new area for battery power information applications such as cell phones‚ pagers‚ watches and hand-held computers etc. Two companies are carrying our pioneering works in the field of
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resizing but there is a loss of content by using these methods. Toeradicate this loss in content‚ Seam Carving was introduced. A Seam is an 8 connected path of pixelswhich traverses an image either from top to bottom or from left to right. Each pixel is assigned an energyusing an Energy function and vertical/horizontal seams are generated on this basis. They are then removedor inserted on the basis of their energy‚ in an optimal order. This leads to the change in aspect ratio of animage‚ without
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challenged by steganalysis. The most notable steganalysis algorithm is the RS attack which detects the steg-message by the statistic analysis of pixel values. To ensure the security against the RS analysis‚ we presents a new steganography based on genetic algorithm in this paper. After embedding the secret message in LSB (least significant bit) of the cover image‚ the pixel values of the steg-image are modified by the genetic algorithm to keep their statistic characters. Thus‚ the existence of the secret message
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on distortion constraint are provided to discriminate the authentication of the line in the image; 3) a fake saliency map is used to maximum fake detection density‚ and based on the fake saliency map‚ an energy function is provided to achieve the pixel-level forgery object via graph cut. Experimental results on simulated
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OpenGL Programming Guide (Addison-Wesley Publishing Company) Chapter 1 Introduction to OpenGL Chapter Objectives After reading this chapter‚ you’ll be able to do the following: Appreciate in general terms what OpenGL does Identify different levels of rendering complexity Understand the basic structure of an OpenGL program Recognize OpenGL command syntax Identify the sequence of operations of the OpenGL rendering pipeline Understand in general terms how to animate graphics in an OpenGL program
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TOPIC: THE DIGITAL CAMERA SUBTOPICS: -HISTORY -TYPES OF DIGITAL CAMERAS -PARTS OF A DIGITAL CAMERA -RELATIONSHIP BETWEEN ANALOGUE AND DIGITAL CAMERAS (Advantages and Disadvantages of Each). HISTORY OF DIGITAL CAMERAS The World‘s First Digital Camera by Kodak and Steve Sasson The Evolution of Digital Cameras A film-free camera was patented as early as 1972 by Texas Instruments‚ but Kodak
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2. A medium-sized engineering firm has three separate engineering offices. In each office‚ a local LAN supports all the engineers in that office. Due to the requirement for collaboration among the offices‚ all the computers should be able to view and update the data from any of the three offices. In other words‚ the data storage server within each LAN should be accessible to all computers‚ no matter where they are located. Draw a network diagram that will support this configuration. 3. A small
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4. List of features measured for each centerline pixel C.AVR Calculation and Disease Detection Early retinal vessel caliber changes may predict risk of diabetic retinopathy. These can be determined by calculating the arteriolar-to-venular diameter ratio(AVR) [17]-[19]. For normal retinal images
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