Implementation of Leaf Image Recognition System Based on LBP and B/S Framework

2015 ◽  
pp. 661-670
Author(s):  
Sen Zhao ◽  
Xiao-Ping Zhang ◽  
Li Shang ◽  
Zhi-Kai Huang ◽  
Hao-Dong Zhu ◽  
...  
Keyword(s):  
Image Recognition ◽  
Recognition System ◽  
Leaf Image

scholarly journals PLANT LEAF IMAGE RECOGNITION SYSTEM

2021 ◽  
Vol 1 (1) ◽  
pp. 35-44
Author(s):  
Gaurav Kulkarni ◽  
◽  
Chandrashekhar Kumbhar
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Image Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Rare Plant ◽  
Plant Recognition ◽  
Identification Techniques ◽  
Leaf Image ◽  
End Results

Plants play a vital role in our day-to-day life. Hence, a good understanding of plants is needed to help in identifying new or rare plant species. Such identification will in turn improve the drug industry, balance the ecosystem as well as the agricultural productivity and sustainability. We often come across various plants with different variety of leaves and flowers every single day. We try to recognize it, but we fail. So we need some system which can tell us about the leaf/flower instantly. So, to solve such problems, we introduce a plant recognition system (PRS) which tells you the details about a leaf by just uploading the image of the leaf. For this system, we use image processing and some identification techniques which can recognize the leaf by its structure, colour, shape etc and fetch the details about it and provide the details of it to the user. This paper gives a understanding about the different methods used under image processing and various methods and algorithm used to identify that leaf in a short and simple way. Object recognition and detection are techniques with similar end results and implementation approaches. Therefore, it requires heavy pre-processing and implements various processes to obtain the end results.

scholarly journals Face Mask Recognition System with YOLOV5 Based on Image Recognition

2020 ◽  
Author(s):  
Guanhao Yang ◽  
Wei Feng ◽  
Jintao Jin ◽  
Qujiang Lei ◽  
Xiuhao Li ◽  
...  
Keyword(s):  
Image Recognition ◽  
Face Mask ◽  
Recognition System

Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy

2016 ◽  
Vol 83 (3) ◽  
pp. 643-649 ◽  
Author(s):  
Yoko Kominami ◽  
Shigeto Yoshida ◽  
Shinji Tanaka ◽  
Yoji Sanomura ◽  
Tsubasa Hirakawa ◽  
...  
Keyword(s):  
Image Recognition ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Recognition System ◽  
Colorectal Polyp ◽  
Magnifying Colonoscopy ◽  
Computer Aided ◽  
Real Time Image ◽  
Time Image ◽  
Aided Diagnosis

Design and Implementation of Contour Tracing Image Recognition System

2014 ◽  
Vol 543-547 ◽  
pp. 2209-2212
Author(s):  
Chun Hua Xiong ◽  
You Jie Zhou ◽  
Gao Jun An ◽  
Chang Bo Lu
Keyword(s):  
Embedded System ◽  
Image Recognition ◽  
Integrated Design ◽  
Recognition System ◽  
Image System ◽  
Intelligent Monitoring ◽  
Contour Tracing ◽  
The Embedded System ◽  
Imaging Sensor ◽  
Very High

Based on the existing contour tracing image recognition technology, combining the embedded system technology and the computer storage control technology, the author makes an integrated design, adopts the image processing chip, USB controller, the imaging sensor and other hardware circuits and develops an intelligent image system. The system can make real-time monitoring the size and change of millimeter-sized irregular target objects. Its applicable value in the fields such as intelligent monitoring of oil equipment, medical imaging and criminal investigation is very high.

IMAGE RECOGNITION BY INTEGRATION OF CONNECTIONIST AND SYMBOLIC APPROACHES

1995 ◽  
Vol 09 (03) ◽  
pp. 485-515 ◽  
Author(s):  
F. ROLI ◽  
S. B. SERPICO ◽  
G. VERNAZZA
Keyword(s):  
Image Recognition ◽  
Magnetic Resonance Image ◽  
Contextual Information ◽  
Recognition System ◽  
Recognition Problem ◽  
Training Process ◽  
Neural Architecture ◽  
Recognition Phase ◽  
Abstraction Levels

This paper presents a methodology for integrating connectionist and symbolic approaches to 2D image recognition. The proposed integration paradigm exploits the synergy of the two approaches for both the training and the recognition phases of an image recognition system. In the training phase, a symbolic module provides an approximate solution to a given image-recognition problem in terms of symbolic models. Such models are hierarchically organized into different abstraction levels, and include contextual descriptions. After mapping such models into a complex neural architecture, a neural training process is carried out to optimize the solution of the recognition problem. The so-obtained neural networks are used during the recognition phase for pattern classification. In this phase, the role of symbolic modules consists of managing complex aspects of information processing: abstraction levels, contextual information, and global recognition hypotheses. A hybrid system implementing the proposed integration paradigm is presented, and its advantages over single approaches are assessed. Results on Magnetic Resonance image recognition are reported, and comparisons with some well-known classifiers are made.

FORMALIZATION OF THE PROBLEM OF MATHEMATICAL INTERPRETATION RECOGNITION OF MEDICAL IMAGES FOR EXAMPLE ALOPECIA

2021 ◽  
pp. 126-129
Author(s):  
Ахмед Магомедович Денгаев
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Image Recognition ◽  
Support System ◽  
Medical Knowledge ◽  
Recognition System ◽  
Subject Area ◽  
Main Task ◽  
Medical Image Recognition ◽  
Mathematical Interpretation

Одним из перспективных и эффективных направлений автоматизированной диагностики заболевания является использование системы распознавания медицинских образов. Главная задача - это максимально точная интерпретация изображения. Прежде всего, необходимо правильно формализовать задачу, провести структуризацию основных условий функционирования системы. В статье составлено содержательное описание предметной области, разработано формализованная схема исследуемой системы. Обозначено, что решение задачи структуризации сводится к разработке отдельных классов, сгруппированных по общим признакам и характеристикам болезни. В этом случае точность и информативность диагноза будет зависеть от полноты базы данных конкретного класса. Приведена математическая интерпретация отношений и связей элементов системы. Применение математических моделей и алгоритмов в медицине является важной задачей. Выбор того или иного алгоритма определяется решаемой задачей. Необходимо понимать, что получаемый результат особо ценен, если он подтверждается математическими расчетами. Предложена многоуровневая архитектура системы поддержки принятия решений, где ключевое место отведено модулю автоматизированной диагностики и распознавания изображения. Отмечено, что при создании системы поддержки принятия решения в медицине специалисты сталкиваются с двумя концептуальными барьерами: первый - связан с колоссальным объемом медицинских знаний, а второй - с постоянным обновлением этих знаний и технологий их обработки. Поэтому главной задачей является правильная структуризация и формализация системы поддержки принятия решений для его эффективного применения One of the most promising and effective areas of automated diagnosis of the disease is the use of a medical image recognition system. The main task is to interpret the image as accurately as possible. First of all, it is necessary to properly formalize the task, to structure the basic conditions for the functioning of the system. The article contains a meaningful description of the subject area, and a formalized scheme of the system under study is developed. It is indicated that the solution to the problem of structuring is reduced to the development of separate classes grouped by common signs and characteristics of the disease. In this case, the accuracy and informativeness of the diagnosis will depend on the completeness of the database of a particular class. The mathematical interpretation of the relations and connections of the system elements is given. The application of mathematical models and algorithms in medicine is an important task. The choice of an algorithm is determined by the problem being solved. It is necessary to understand that the result obtained is particularly valuable if it is confirmed by mathematical calculations. A multi-level architecture of the decision support system is proposed, where the key place is given to the module of automated diagnostics and image recognition. It is noted that when creating a decision support system in medicine, specialists face two conceptual barriers: the first one is associated with a huge amount of medical knowledge, and the second one is associated with the constant updating of this knowledge and technologies for their processing. Therefore, the main task is to properly structure and formalize the decision support system for its effective application

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