Comparative analysis of hyperspectral feature extraction methods in vegetation classification

2017 ◽  
Author(s):  
Mertalp Ocal ◽  
Kazim Ergun ◽  
Gozde Bozdagi Akar
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Extraction Methods ◽  
Vegetation Classification

Comparative analysis of feature extraction methods in satellite imagery

2017 ◽  
Vol 11 (04) ◽  
pp. 1 ◽  
Author(s):  
Shahid Karim ◽  
Ye Zhang ◽  
Muhammad Rizwan Asif ◽  
Saad Ali
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Satellite Imagery ◽  
Extraction Methods

scholarly journals Face Tracking Performance in Head Gesture Recognition System

2020 ◽  
Vol 9 (5) ◽  
pp. 1096-1099
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Face Detection ◽  
Gesture Recognition ◽  
Recognition System ◽  
Extraction Methods ◽  
Face Tracking ◽  
Tracking Performance ◽  
Adaboost Algorithm ◽  
Camshift Algorithm

This paper describes the comparative analysis of different face tracking methods in the head gesture recognition system. The major constraints of head gesture recognition system, i.e. face detection, feature extraction, tracking, and recognition are explained. We used adaboost algorithm for detection, and Camshift algorithm for tracking with different feature extraction methods. We performed extensive experimentations and presented a comparative analysis of tracking performance of head gesture recognition system under cluttered backgrounds, shadow and sunshine conditions. Experimental results show the robustness in face detection, tracking and direction recognition of the proposed method.

scholarly journals Hybrid CNN-LSTM Model for Answer Identification

2019 ◽  
Vol 8 (3) ◽  
pp. 1163-1166
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Extraction Methods ◽  
Learning Methods ◽  
Automatic Feature Extraction

User quest for information has led to development of Question Answer (QA) system to provide relevant answers to user questions. The QA task are different than normal NLP tasks as they heavily depend to semantics and context of given data. Retrieving and predicting answers to verity of questions require understanding of question, relevance with context and identifying and retrieving of suitable answers. Deep learning helps to produce impressive performance as it employs deep neural network with automatic feature extraction methods. The paper proposes a hybrid model to identify suitable answer for posed question. The proposes power exploits the power of CNN for extracting features and ability of LSTM for considering long term dependencies and semantic of context and question. Paper provides a comparative analysis on deep learning methods useful for predicting answer with the proposed method .The model is implemented on twenty tasks of babI dataset of Facebook .

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