skin disease classification
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This document proposes developing a computer application to classify common skin diseases from images in order to assist medical facilities in remote areas without access to skin specialists. The application would acquire skin images, classify the condition, and allow uploading classified images to a web app for further expert opinion. It would be designed to classify conditions like acne, scabies, psoriasis, eczema, and more. The document outlines requirements, approaches, a flow chart, and testing details for the proposed application.
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object-oriented JavaScript is somewhat redundant, as the JavaScript language is completely object-oriented and is impossible to use otherwise.
Objects are the foundation of JavaScript. Virtually everything within the language is an object. Much of the power of the language is derived from this fact.
Design patterns are advanced object-oriented solutions to commonly occurring software problems. Patterns are about reusable designs and interactions of objects.
Operator Overloading
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PyCon 2013 : Scripting to PyPi to GitHub and More
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This presentation was used for a session to increase awareness about Python inside our organization, Synerzip.
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This document provides instructions for installing Python and various Python libraries on Mac OS X. It describes downloading and installing Python from python.org. It also explains how to install pip and then use pip to install popular Python packages like NumPy, SciPy, Matplotlib, Scikit-learn, TensorFlow, Theano, PyGame, OpenCV, and Requests. The user is instructed to open Terminal and run pip3 install package_name to install these libraries for Python 3.X. Detailed steps are provided for compiling and installing OpenCV from source.
Lesson1 python an introduction
Python is a simple yet powerful programming language that can be used for a wide range of tasks. The document provides an introduction to Python, discussing why it is a good language to learn, its history and examples of common uses. It covers Python's syntax, data types, modules, object oriented programming and how to interface Python with databases and other languages. The goal is to illustrate Python's versatility and ease of use through examples.
Introduction to python
This document provides an introduction and overview of Python including sections on syntax, lists and dictionaries, for loops, and implementation examples. The introduction covers that Python code is indented using whitespace rather than brackets, semicolons are optional, and data types do not need declaration. Lists are described as arrays that can hold different data types indexed by number, while dictionaries hold key-value pairs indexed by keys that can be numbers, strings, or other data types. For loops in Python iterate over each item in a list or dictionary without needing an explicit condition. The implementation section provides examples of using for loops over lists, creating and accessing a dictionary of fruits, and making lists of dictionaries to demonstrate Python concepts.
Python master class part 1
This document provides an introduction and overview of Python and the Flask web framework. It discusses key Python concepts like objects, references, memory management and data types. It also covers Flask topics like routing, templates, request objects, sessions and authentication. The document aims to give readers a high-level understanding of Python and how to build basic web applications with Flask.
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Face recognition
Face recognition is a type of biometric software that uses analysis of facial patterns to identify individuals. It has various applications including security, law enforcement, and social media photo tagging. The technology works by measuring nodal points on faces like eye and nose position to create unique numerical faceprints for identification and verification. While effective, face recognition depends on clear images and has limitations with expressions, lighting, or obscured faces. It is increasingly being implemented in areas like access control, immigration, and banking due to lower costs.
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Expert systems are computer applications that solve complex problems in a particular domain at an expert human level using artificial intelligence. They were developed in the 1970s by computer scientist Edward Feigenbaum. Expert systems can explain their reasoning, deal with uncertainty without full information, make errors, and display intelligent behavior, while not being able to refine their own knowledge bases or always being difficult and expensive to develop and maintain. They have applications in medical diagnosis, vehicle fault detection, design, virus detection, and loan analysis, providing advantages like speed, reduced errors and risks, while always being available.
Face Chk - Face Recognition
The fear of touching public surfaces sparked by the Covid -19 outbreak is spurring a shift from fingerprint sensors to Facial Recognition Systems for allowing access to employees across offices in India.
Face recognition is a unique solution in the recent context, where touching a surface by multiple people has emerged as a potential cause of infection. Traditional Biometric attendance devices are dead upgrade with contact less/Hygienic/Social distancing AI deep Learning based Facial recognition system which includes Face Detection + Mask Detection + Door Control + Attendance Management.
May write to us at info@primeinfoserv.com for further details
Facial recognition
Facial recognition is a type of biometric system that identifies individuals by analyzing patterns in images of their faces. The presentation summarizes how facial recognition systems work by detecting faces, normalizing them, extracting distinguishing features to create a template, and then matching templates to identify individuals. It notes advantages like convenience but also challenges like difficulty with changes in appearance over time. Applications discussed include security, banking, and voter verification.
Face recognition
This presentation summarizes facial recognition technology. It begins by explaining how facial recognition works, including face detection and recognition approaches like feature extraction, holistic, and hybrid methods. It discusses challenges like handling variations in expressions and illumination. It also provides examples of applications that use facial recognition, such as time and attendance tracking, access control, PC security, web login, and social media/gaming. In general, the presentation aims to describe the key concepts and methods in facial recognition as well as popular use cases.
Movie on face recognition in e attendace
This case study examines face recognition technology for e-attendance. It discusses the history of face recognition, defines biometrics, and explains why face recognition was chosen over other biometrics. The key components of face recognition systems are described, including how the systems work by detecting, aligning, normalizing, and matching facial features. Current and potential future applications of the technology are also outlined.
Facial Recognition: The Science, The Technology, and Market Applications
Ravi Das
Technical Writer
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FACE RECOGNITION TECHNOLOGY
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
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Using Artificial Intelligence For Cytology Screening
Using Artificial Intelligence Visual Recognition and Machine Learning can improve speed and accuracy of screening cytology slides
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This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
Face recognition Technology By Rohit
This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
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Foreigners Authentication Based on Multi-Biometric System for Iraq
Multi-authentication system built using ZFM-20 fingerprint sensor and Haar cascade classifier to face detection and local binary pattern histogram (lbph) face recognition system implementation on Linux platform on raspberry pi 3 microcomputer
ACET.pptx
This document provides an overview of facial recognition technology and its applications. It discusses how facial recognition systems work by using nodal points on faces to recognize individuals. The objectives are to design software that can accurately detect faces from images without physical interaction and allow for high identification and verification rates. The research methodology involves a workflow for the facial recognition system. Potential applications mentioned include using facial recognition for computer and border security, voting verification, and commercial uses like residential security and banking.
ppt minor project.pptx
Its a power point presentation on face recognition system . In the covid time biometrics is not a good option thats why we need a face recognition system
Facial_recognition_systtem.pptx
This document provides an overview of facial recognition technology. It begins with an introduction to biometrics and how facial recognition works as a biometric method. It then describes the process of facial recognition which includes image acquisition, processing, locating distinctive facial characteristics, template creation, and template matching. The document discusses implementations, strengths, weaknesses and applications of facial recognition technology. It concludes by noting some of the challenges but also potential of the technology.
Markus Paasovaara: Face recognition
The state of the art and how we applied it at Finnair. This presentation was held in Futurice Munich Beer & Tech event: Brave new world, AI applied. https://www.meetup.com/Munchen-Beer-Tech-Meetup/events/240575870/
Deep learning on face recognition (use case, development and risk)
1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
Face recognition and math
This document discusses the math behind computer-aided face recognition. It explains that most algorithms fall into two groups: feature-based, which uses geometric features like eye distance, and image-based. 3D recognition is more accurate as it can recognize subjects from different angles and in darkness by creating a 3D model. The process involves acquiring an image, detecting the face, measuring curves to create a template, and matching it to databases for verification or identification. While it has advantages for security, there are also disadvantages like privacy concerns and accuracy being impacted by lighting and pose.
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introduction to python
This document provides an introduction to Python programming for an artificial intelligence lab course. It covers downloading and installing Python and the Anaconda distribution, using Spyder as an IDE, variables and data types in Python, and basic concepts like indentation, variable naming conventions, and Python keywords. The goal is to prepare students to use Python for labs related to artificial intelligence topics.
filters for noise in image processing
The document discusses various types of filters that can be used to reduce noise in digital images, including mean filters, median filters, and order statistics filters. Mean filters include arithmetic, geometric, and harmonic filters, which reduce noise by calculating the mean pixel value within a neighborhood. Median filters select the median pixel value within a neighborhood to reduce salt and pepper noise while retaining edges. Adaptive filters modify their behavior based on statistical properties of local regions in order to better reduce noise without excessive blurring.
Noise Models
The document discusses noise models and methods for removing additive noise from digital images. It describes several types of noise that can affect images, such as Gaussian, impulse, uniform, Rayleigh, gamma and exponential noise. It also presents various noise filters that can be used to remove noise, including mean filters like arithmetic, geometric and harmonic filters, and order statistics filters such as median, max, min and midpoint filters. The filters aim to reduce noise while retaining image detail as much as possible.
Noise Models
This document discusses noise models and additive noise removal in digital image processing. It covers several types of noise that can affect images, including Gaussian, impulse, uniform, Rayleigh, gamma, exponential, and periodic noise. Various noise models are presented, such as definitions and equations for Gaussian, Rayleigh, gamma, exponential, uniform, and impulse noise. Examples of how different noise types affect images and histograms are also shown.
Introduction to machine learningunsupervised learning
The document provides an introduction to machine learning and discusses different types of machine learning algorithms including supervised and unsupervised learning. It provides examples of problems that could be addressed using supervised learning like regression to predict housing prices and classification to detect cancer. Unsupervised learning is used to discover hidden patterns in unlabeled data like grouping customer accounts or news articles.
Opengl texturing
The document discusses OpenGL texturing. It describes how textures are loaded and applied to geometry. Textures are loaded using LoadTexture, which reads in texture data from a file. Textures are then enabled and bound. Texture parameters like filtering and wrapping modes are also set. Texture coordinates are assigned to vertices to map portions of the texture onto the geometry. When finished, textures can be cleaned up by deleting them to free memory.
Mathematics fundamentals
A matrix is an arrangement of numbers organized into rows and columns. It has a specific number of rows and columns called its dimensions. Each individual number in the matrix is called an element, which has a specific location defined by its row and column. A square matrix has the same number of rows and columns. Matrices are useful in computer graphics for transformations and are typically denoted with a capital letter indicating their dimensions. To be equal, two matrices must have the same dimensions and corresponding elements must be identical.
3 d graphics with opengl part 2
The 3D graphics rendering pipeline consists of 4 main stages: 1) vertex processing, 2) rasterization, 3) fragment processing, and 4) output merging. In vertex processing, transformations are applied to position objects in the scene and camera. Rasterization converts vertex data into fragments and performs operations like clipping and scan conversion. Fragment processing handles texture mapping and lighting. Output merging combines fragments and uses the z-buffer to remove hidden surfaces when displaying the final pixels.
3 d graphics with opengl part 1
The document discusses computer graphics hardware and GPU computing. It explains that GPUs have thousands of smaller cores that are optimized for parallel processing compared to CPUs which have a few cores for serial processing. GPU computing uses both the CPU and GPU together, with serial code running on the CPU and parallel code on the GPU. The document also covers topics like the CUDA platform, GPU specifications, raster displays, frame buffers, double buffering, 3D graphics pipelines, and the different types of transformations involved in 3D rendering like model, view and projection transforms.
3 d graphics basics
Human eyes perceive 3D projections in 2D. Coordinate systems including 1D, 2D, and 3D Cartesian systems define locations using reference points and orthogonal axes. 3D systems use x, y, and z coordinates to locate points and define geometries in space, including volumes like cubes and spheres. Coordinate transformations allow changing between reference frames.
2 d transformations
This document discusses 2D transformations including vector addition, dot products, cross products, translation, rotation, and scaling. Vector addition involves adding the x and y components of two vectors. The dot product of two vectors produces a scalar value based on the cosine of the angle between the vectors and can be used to determine if they are perpendicular. The cross product of two vectors produces a new vector perpendicular to both input vectors, determined by the right hand rule. Translation moves every point of an object by the same displacement amounts. Rotation rotates every point of an object by the same angle. Scaling enlarges or shrinks an object by a uniform scaling factor.
Gui
This document discusses the evolution of graphical user interface (GUI) capabilities in the Java programming language. It describes the Abstract Window Toolkit (AWT) introduced in JDK 1.0, which provided basic cross-platform GUI functionality but had limitations. JDK 1.1 improved on AWT with an event delegation model. JDK 1.2 introduced Swing, a richer GUI library that better integrated with native operating system look and feels. Swing components are lightweight compared to heavyweight AWT components. The document also covers GUI component classes, layout managers, menus, labels and event handling in Java GUI programming.
Inheritance
The document discusses object-oriented programming concepts related to inheritance in Java, including:
- Inheritance allows a subclass to inherit attributes and behaviors from its superclass. Subclasses can add, use, or override inherited functionality.
- Abstract classes can contain abstract methods that are declared but not defined. Subclasses must define all abstract methods to be instantiated. Abstract classes prevent direct instantiation.
- Polymorphism allows calling the same method on different object types, with the actual implementation determined at runtime based on the object's type. Method overriding in subclasses enables polymorphism.
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Arrays string handling java packages
This document discusses arrays, strings, and string buffers in Java. It covers:
- Creating and initializing arrays, including single and multi-dimensional arrays
- Common string operations like length(), concatenation, character extraction, comparison, searching, and modifying case
- Constructing and modifying mutable string buffers, which can be changed unlike immutable string objects
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Java basics
There are three main editions of Java: Java SE (Standard Edition), Java EE (Enterprise Edition), and Java ME (Micro Edition). Java SE is for general use and desktop applications. Java EE adds enterprise features like EJBs, JSP, and web services for developing enterprise applications. Java ME is for small devices like phones. The document then discusses Java concepts like classes, objects, fields, methods, constructors, and data types. It provides examples of how to define and use classes, objects, and methods in Java.
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skin disease classification
- 1. Sardar Alam Hassan khan Supervisor Dr. Usama Ijaz Bajwa
- 2. INTRODUCTION The skin covers the whole body. An important function of the skin is to protect the body from infection. We need automated computer application for skin disease classification to be installed at the medical facilities like rural health clinics for remote areas where skin specialists are unavailable.
- 3. SCOPE Our propose project will work in the following way: •Acquire Image. •Classify disease. •Upload Classified Image to Web App for further opinion. •Registered Skin Specialist in Web App who can entertain such requests.
- 4. The Application will be able to classify the following diseases: •Acne Vulgaris •Scabies •Psoriasis •Eczma •Tinea Corprosis •Vitaligo SCOPE
- 5. FUNCTIONAL REQUIREMENTS • Image Acquisition. • Preprocessing steps: • Color gradient generator on an image. • Thresholding and Clustering on image. • Cropping and isolating region of interest (ROI). • Visual Feature Extraction • System Training for skin disease classification • Separate access of application for Admin, Medical Person and Skin Specialist • Interface for Medical person • Interface for Skin specialist • Diagnosis of skin disease • Data retrieval and Data manipulation
- 6. NON-FUNCTIONAL REQUIREMENTS •Software Quality Attributes •Safety and Security Requirements •Accuracy
- 7. APPROACHES •Skin Disease Classification(Structured) •Further Opinion (OO)
- 9. PREPROCESSING , ISOLATED ROI
- 11. TESTING AND TRAINING •Total Data : 664 •Classification : ANN •Training : 70% •Testing : 15% •Validation : 15%
- 14. Thanks