EDGE DETECTION IN COLOUR IMAGE REPORT MINOR PROJECT- I DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING‚ JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY‚ NOIDA SUBMITTED BY: SHEFALI JAIN (10102216) PARTH KHANDURI (10102171) CERTIFICATE This is to certify that the work titled “EDGE DETECTION IN COLOUR IMAGE” submitted by PARTH KHANDURI (10102171) & SHEFALI JAIN (10102216) in partial fulfillment for the award of degree of B.Tech of Jaypee Institute of Information Technology
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Multimedia applications [10] (c) A Simple Transform Encoding procedure maybe described by the following steps for a 2x2 block of monochrome pixels: 1. Take top left pixel as the base value for the block‚ pixel A. 2. Calculate three other transformed values by taking the difference between these (respective) pixels and pixel A‚ i.e. B-A‚ C-A‚ D-A. 3. Store the base pixel and the differences as the values of the transform. Given the above transform: (i) (ii) What is the inverse transform? [2] How may such
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Mathematical Handwriting Recognition with a Neural Network and Calculation Author: Tyler Sondag Date: 4/22/07 For Dr. Pokorny ’s CSI 490 Course Abstract The goal of this project was to create a software system that recognizes handwritten mathematical expressions and computes the answer. No special syntax or formatting was to be required for these expressions‚ since a major goal of this system was for users to be able to use the system without having to learn anything new. Support was desired for
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worldwide. The high performance Nokia N-Series has got several members like Nokia N70‚ N71‚ N72‚ N73‚ N75‚ N76‚ N77‚ N80‚ N90‚ N91‚ N91 8GB‚ N92‚ N93‚ N93i‚ N95. N-Series mobile handsets incorporate fun features like high resolution cameras (2-3.2 mega pixel cameras)‚
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JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY INSTITUTE OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY BSc COMPUTER TECHNOLOGY Literature Review On DIGITAL IMAGE CRYPTOSYSTEM WITH ADAPTIVE STEGANOGRAPHY NAME REG NO : JOHN NJENGA : CS 282-0782/2009 SUPERVISORS: DR. OKEYO MR. J WAINAINA 1 DECLARATION I declare that all materials presented here are my own original work‚ or fully and specifically acknowledged wherever adapted from other sources. The work has not been submitted previously
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A PROJECT REPORT ON “FINGERPRINT RECOGNITION AND IMAGE ENHANCEMENT USING MATLAB” Submitted in partial fulfillment Of the requirements for the award of the degree in BACHELOR OF TECHNOLOGY IN APPLIED ELECTRONICS AND INSTRUMENTATION ENGINEERING SUBMITTED BY: SHAKTI ABHISHEK- 0803112 SATISH GOYAL - 0803064 ROHIT DASH - 0803086 MD. IRFAN ARIF RAHMAN - 0803117 [pic] DEPARTMENT OF APPLIED ELECTRONICS AND INSTRUMENTATION ENGINEERING GANDHI INSTITUTE OF ENGINEERING AND TECHNOLOGY Biju Patnaik University
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equalization can give rise to. This is achieved by limiting the contrast enhancement of AHE. The contrast amplification in the vicinity of a given pixel value is given by the slope of the transformation function. This is proportional to the slope of the neighborhood cumulative distribution function (CDF) and therefore to the value of the histogram at that pixel value. CLAHE limits the amplification by clipping the histogram at a predefined value before computing the CDF. This limits the slope of the CDF
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object could have contributions from the background. This contribution is incorporated into a compositing equation in the form of opacity α of a pixel; the equation expresses the observed color value of a pixel as a convex combination of foreground (F) and background (B) colors. The opacity takes value in the range [0‚ 1]‚ with 0 indicating that the pixel is from the background and 1 indicating that it is from the foreground. Estimating the digital matte is useful in image and video editing tasks
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PRI: We introduce a probabilistic version of the well-known Rand Index (RI) for measuringthe similarity between two partitions‚ called Probabilistic Rand Index (PRI)‚ in which agreements and disagreements at the object-pair level are weighted according to the probability of their occurring by chance. We then cast consensus clustering as an optimization problem of the PRI value between a target partition and a set of given partitions‚ experimenting with a simple and very efficient stochastic optimization
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segmentations. 2. A universal skin-color map is derived and used on the chrominance component of the input image to detect pixels with skin-color appearance . 3. Then‚ based on the spatial distribution of the detected skin-color pixels and their corresponding luminance values‚ the algorithm employs a set of novel regularization processes to reinforce regions of skin color pixels that are more likely to belong to the facial regions and eliminate those that are not. 4. The performance of the face segmentation
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