Combined Analysis of Image Processing Transforms with Location Averaging Technique for Facial and Ear Recognition System

2018 ◽  
pp. 67-77
Author(s):  
A. Parivazhagan ◽  
A. Brintha Therese
Keyword(s):  
Image Processing ◽  
Recognition System ◽  
Combined Analysis ◽  
Averaging Technique ◽  
Ear Recognition

An Extensive Study on Currency Recognition System Using Image Processing

2018 ◽  
Author(s):  
Georgie V. Rajan ◽  
Dincy M. Panicker ◽  
Nisha Elizabeth Chacko ◽  
Jayalekshmi Mohan ◽  
V.K. Kavitha
Keyword(s):  
Image Processing ◽  
Recognition System ◽  
Extensive Study

scholarly journals Automatic Damaged Number Plate Recognition System In Image Processing

2019 ◽  
Vol 8 (6S2) ◽  
pp. 287-291
Keyword(s):  
Image Processing ◽  
Recognition System ◽  
Planning System ◽  
License Plate ◽  
Restricted Area ◽  
Proof Scheme ◽  
Optical Character ◽  
The Supreme Court ◽  
Security Control ◽  
Real Goal

The use of the NPR (Number Plate Recognition) is a structure expected to assist confirm the number tags of cars. This structure is anticipated to have the real goal of the security system. This structure is based on an image planning system. This scheme helps to distinguish between the number plates of the cars, to prepare them and to use the information taken care of for further methodology such as securing, empowering the car to pass or to expel the car. NPR is an image scheduling development that utilizes the number (license) plate to acknowledge the car. The goal is to structure a gainful altered attested vehicle unmistakable proof scheme by the use of car number plateThe system is carried out along the route of security control of a particularly restricted area, such as military areas or area around the finest public working settings, such as the Parliament, the Supreme Court, etc. First, the produced system receives a image of the car. The region of the vehicle number plate is evacuated using the picture division in the picture. The optical character affirmation method is used to confirm the character. The resulting information is then used to distinguish and record the information in the database. The structure is performed and impersonated in Python, and the execution is tried on a licensed image. It is seen from the outset that the produced structure acknowledges and sees the car amount vividly.

scholarly journals Image Processing: Capturing Student Attendance Data

2017 ◽  
Vol 16 (7) ◽  
pp. 7002-7009
Author(s):  
Hendra - Kurniawan ◽  
Melda Agarina ◽  
Suhendro Yusuf Irianto
Keyword(s):  
Image Processing ◽  
Learning Process ◽  
Teaching And Learning ◽  
Process Model ◽  
Tertiary Education ◽  
Recognition System ◽  
Student Attendance ◽  
Processing Application ◽  
Student Activities ◽  
Facial Recognition System

Role of the student attendance record is very important in the primary, secondary, and tertiary education. The purpose of this record is monitoring student activity in the teaching and learning process and regarded as one of the important learning assessments. Moreover, a data processing for recording the student attendance is currently done in various ways such as fingerprint, radio frequent identification (RFID), facial recognition system, android-based application, and others. However, many conventional ways (i.e., using paper-based system) are still used, especially in Indonesia. This is because several universities still rarely have enough funds for developing innovative systems. In this research, the image processing application for capturing student attendance data was built. The objective of this research was to provide an efficient alternative to monitor student activities in teaching and learning process. The image processing produced the information related to student attendance by scanning the attendance file through jpg/jpeg using learning vector quantization (LVG) as the process model.

scholarly journals Plant Diseases Recognition Based on Image Processing Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Guiling Sun ◽  
Xinglong Jia ◽  
Tianyu Geng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Image Recognition ◽  
Color Image ◽  
Regional Growth ◽  
Recognition System ◽  
Plant Diseases ◽  
Image Feature

A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.

scholarly journals Digital image processing applied on static sign language recognition system/Diseño de un Sistema de Reconocimiento de Gestos No Móviles mediante el Procesamiento Digital de Imágenes

Prospectiva ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 41-48
Author(s):  
Betsy Villa ◽  
Valeria Valencia ◽  
Julie Berrio
Keyword(s):  
Image Processing ◽  
Sign Language ◽  
Digital Image Processing ◽  
Digital Image ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Calidad De Vida

El lenguaje de señas es el autóctono, utilizado por las personas sordas para comunicarse. Se compone de movimientos y expresiones realizadas a través de diferentes partes del cuerpo. En Colombia, hay gran ausencia de tecnologías encaminadas al aprendizaje e interpretación de éste; por ende, es un compromiso social, llevar a cabo iniciativas que promuevan la mejora de la calidad de vida de este grupo social del país, el cual está representado por una minoría considerable. En este artículo, se muestra el proceso de diseño e implementación de un sistema de reconocimiento de gestos no móviles mediante el entorno de Matlab y el método SIFT; a través del cual se visualiza la imagen de la letra adquirida, junto con la traducción de la misma en el lenguaje de señas colombiano, aplicando identificación de puntos claves y comparación con imágenes almacenadas en base de datos. La herramienta realiza el reconocimiento de las 20 letras no móviles de este conjunto, implementando una interfaz gráfica en Matlab para una mejor visualización, fácil acceso al sistema y uso por parte del usuario. Se comprueba una mejor respuesta del sistema mediante la utilización de un elemento estandarizado de la imagen, en este caso, un guante quirúrgico, y se propone la mejora de la herramienta aplicando métodos de redes neuronales para que posteriormente pueda ser desarrollada de forma online; generando un mayor impacto para las necesidades actuales de la población colombiana.

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