Gradient Vector Flow (to the right) calculated on the volume to the left. Gradient Vector Flow (GVF) is a feature-preserving diffusion of gradient information. It was originally introduced by Xu and Prince to drive snakes, or active contours, towards edges of interest in image segmentation. But GVF is also used for detection of tubular structures and skeletonization. In this post I present a simple Matlab implementation of GVF for 3D images which I made because I could not find any online. The implementation is a simple extension of Xu and Prince original 2D implementation found at their website. GVF is the vector field V⃗ (x,y,z)=[u(x,y,z),v(x,y,z),w(x,y,z)] that minimizes the energy function E=∭μ|∇V⃗ |2+|∇f|2|V⃗ −∇f|2dxdydz

where f is the 3D volume itself. This vector field can be found by solving the following Euler equations: μ∇2u−(u−fx)|∇f|2μ∇2v−(v−fy)|∇f|2μ∇2w−(w−fz)|∇f|2=0=0=0 where ∇f=(fx,fy,fz). Solving these equations can be done iteratively with the following Matlab function (GitHub Repository of the code can be found here) 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647| function [u,v,w] = GVF3D(f, mu,...

...Tracing Algorithm¡¨. The algorithm can trace open and closed discontinuous digital shapes and return an ordered set of boundary points that represent the contour of the shape. Unlike other algorithms that return boundary points that are part of the traced shape, our algorithm returns background points that are adjacent to the shape¡¦s contour. Furthermore, the algorithm is not hindered by shapes that are noisy and ill-defined as it can adapt to interruptions in the shape¡¦s contourusing a pre-set tolerance and is able to scan multiple neighbors of a given point. The algorithm has a low complexity and no restrictions on the type or size of the traced shape. The extracted ordered set of boundary points represents the contour of a given shape and is important for curvature-based shape descriptors.
Categories and Subject Descriptors
I.4.6 [ImageProcessing and Computer Vision]: Segmentation ¡V Edge and feature detection, Pixel classification
General Terms
Algorithms.
Keywords
ImageProcessing; Contour Tracing; Shape Boundary Extraction.
1. INTRODUCTION
Contour tracing is an important process in boundary-based shape matching. All shapes are represented by a pattern of pixels and the contour pixels are usually a small subset of that pattern. Curvature-based shape matching methods rely on the contour pixels to describe the irregularities in shapes and a reliable contour-tracing algorithm is...

...Abstract
This paper is about a selected few imageprocessing applications. Optical Character Recognition is the translation of images of handwritten, typewritten or printed text into machine-editable text. Then I have introduced the captcha that we so frequently encounter in common websites. An algorithm trying to solve or break a captcha has been explained.
Face detection is a growing and an important tool in security these days. It must be applied before face recognition. There are many methods for recognizing faces and a few of them are discussed in the paper.
Contents
Topic Pg No
ImageProcessing
Optical character recognition
Captcha
Braking Captcha
Face Detection
Algorithm for Face Detection
References
ImageprocessingImageprocessing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of imageprocessing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.
Typical...

...DIGITAL
IMAGEPROCESSING
(CREATIVE WORLD OF FACE MORPHING)
BY
ABSTRACT
A study on face morphing is proposed.The algorithms explains the extra feature of points on face and based on these feature points, images are portioned and morphing is performed. The algorithms has been used to generate morphing between images of face of different people as well as between images of face of individuals. To do face morphing, feature points are usually specified manually in animation industries. However,this approach involved computation of 3N dimensional probability density function, N being the number of pixels of the image, and we thought the approach was too much computation-demanding.Within the scope of this project, we built up a prototypical automatic animation generator that can take an arbitrary pair of facial images and generate morphing between them.The results of both inter and intra personal morphing are subjectively satisfactory.
1.Introduction
Morphing applications are everywhere. Hollywood film makers use novel morphing technologies to generate special effects, and Disney uses morphing to speed up the production of cartoons. Among so many morphing applications, we are specifically interested in face morphing because we believe face morphing should have much more important applications than other classes of morphing....

...Abstract – ImageProcessing Algorithms are the basis for
Image Computer Analysis and Machine Vision. Employing a
theoretical foundation – Image Algebra – and powerful
development tools – Visual C++, Visual Fortran, Visual
Basic, and Visual Java – high-level and efficient Computer
Vision Techniques have been developed. This paper
analyzes different ImageProcessing Algorithms by
classifying them in logical groups. In addition, specific
methods are presented illustrating the application of such
techniques to the real-world images. In most cases more
than one method is used. This allows a basis for comparison
of different methods as advantageous features as well as
negative characteristics of each technique is delineated.
INTRODUCTION
The Image Algebra [10] forms a solid theoretical
foundation to implement computer vision and imageprocessing algorithms. With the use of very efficient and
reliable high-level computer languages such as C/C++,
Fortran 90, and Java, innumerable imageprocessing and
machine vision algorithms have been written and optimized.
All this code written and compiled has become a powerful
tool available for researchers, scientists and engineers,
which further accelerated the investigation process and
incremented the accuracy of the final results.
The discussion of...

...SECURE ATM BY IMAGEPROCESSING
-AN ADVANCED APPLICATION OF IMAGEPROCESSING &BIOMETRICS
GRANDHI VARALAKSHMI VENKATARAO INSTITUTE OF TECHNOLOGY
BHIMAVARAM
PRESENTED BY:
D.L.PRATHYUSHA
10JH1A0412
IV E.C.E
REGISTRATION ID:0130072354
Email:prathyusha.dasika@gmail.com
ABSTRACT:
There is an urgent need for improving security in banking region. With the advent of atm though banking became a lot easier it become a lot vunerable .
The chances of misuse of this much hyped insecure baby product (atm) are manifold due to the exponention growth of intelligent criminals day by day
INTRODUCTION:
This paper process an atm insecurity model that would combine a physical access card, apin and electronic facial recognition
It encloses the information regarding the imageprocessing . and discussed one of the major application of imageprocessing in biometrics . bio metrics technology turns your body into your password. We discussed various biometric techniques like finger scan ,retina scan , facial scan , hand scan etc..
Face recognition technology may solve the problem since a face is undeniably connected to its owner making impenetrable system
AUTOMATED TELLER MACHINE(ATM):
An auto mated teller machine (ATM) is a computerized tel communications device that provides the customer4s of a finantial transcations in public space with out the need of a...

...ImageProcessing
Rajasekar S, Mohammed Nizar S
ECE Department, Third Year
RMK Engineering College
raja.peak@gmail.com
smohamednizar@gmail.com
+919789356115
+919840515626
Abstract— This document talks about the use of imageprocessing as one of the most crucial element of developing Futuristic Technologies in various fields.
I. What is Imageprocessing?
Imageprocessing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it
II. Concept behind imageprocessing
It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually ImageProcessing system includes treating images as two dimensional signals while applying already set signal processing methods to them..
III. Purpose of imageprocessing
The purpose of imageprocessing is divided into 5 groups. They are:
1. Visualization - Observe the objects that are not visible.
2. Image sharpening and .restoration - To create a better...

...store the data in first row and can take out the data from first row or store the data in last row and can take out the data from first row. Cant take out the data from the middle rows.
Stack Data Structure having one end from which we can enter data and retrieve data. Stack follow LIFO rule, which means (if "data is entered first it will be retrieve in last" of all existing element. If "data is entered last it will be retrieved first" of all
5, Hashing: Collision Resolution Schemes
• Collision Resolution Techniques
• Separate Chaining
• Separate Chaining with String Keys
• Separate Chaining versus Open-addressing
• The class hierarchy of Hash Tables
• Implementation of Separate Chaining
• Introduction to Collision Resolution using Open Addressing
• Linear Probing2
Collision Resolution Techniques
• There are two broad ways of collision resolution:
1. Separate Chaining:: An array of linked list implementation.
2. Open Addressing: Array-based implementation.
(i) Linear probing (linear search)
(ii) Quadratic probing (nonlinear search)
(iii) Double hashing (uses two hash functions)...

...IMAGEPROCESSING – IDENTIFCATION OF DEAD CHICKEN TO PREVENT DISEASE
G.YUVARAJ,
Ph: 9159970702
SRI SAKTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY,
COIMBATORE-641062
Email:yuvarajdoit@gmail.com
ABSTRACT:
Our paper tries to completely automate the poultry process by continuously monitoring the chickens in the poultry and by the application of imageprocessing, identify the dead chicken using a camera and inform the appropriate person so that the dead chicken can be removed from the corresponding cabin. The chickens will be monitored in each cabin with a regular time interval. In the initial phase the background image of the cabin is stored as the reference and after say 15 minutes another image is taken and stored. The difference image is calculated for each image by the application of image subtraction. For this we employ any of the following two algorithms:
1. Two dimensional cross correlation algorithm
2. Sum of absolute difference (SAD) algorithm
If there is a constant variation in the sum of pixels calculated even after 2 or 3 comparisons, then the particular cabin is said to contain a dead chicken.
Considering this practical problem a solution using digital imageprocessing is...