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Multi-module Image Classification System [chapter]

Wonil Kim, Sangyoon Oh, Sanggil Kang, Dongkyun Kim
2006 Lecture Notes in Computer Science  
In this paper, we propose an image classification system employing multiple modules.  ...  The image first categorized into one of the two classes in the global module. The corresponding local module is selected accordingly, and then used in the local classification step.  ...  Our approach we present in this paper employs multi-module neural networks for the sports image classification system.  ... 
doi:10.1007/11766254_42 fatcat:tjb47ktdargjpnwzz37fudganq

A Novel Multi-Attention Driven System for Multi-Label Remote Sensing Image Classification

Gencer Sumbul, Begum Demir
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
The last module exploits these descriptors for multi-label RS image classification.  ...  This paper presents a novel multi-attention driven system that jointly exploits Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in the context of multi-label remote sensing (RS) image  ...  CONCLUSION This paper proposes a novel multi-attention driven system for classification of RS images with multi-labels.  ... 
doi:10.1109/igarss.2019.8898188 dblp:conf/igarss/SumbulD19 fatcat:ifquu4ewfjbddipxgsje6xeisy

Semantic Annotation of Land Cover Remote Sensing Images Using Fuzzy CNN

K. Saranya, K. Selva Bhuvaneswari
2022 Intelligent Automation and Soft Computing  
This paper presents a novel fuzzy logic based Convolution Neural Network intelligent classifier for accurate image classification.  ...  To annotate remote sensing images, the CNN method effectively creates semantics and categorises images.  ...  Image data set module is the first module of a proposed system. It allows use of the UC Merced data set, with 70% of the images being used for training and 30% for testing the proposed system.  ... 
doi:10.32604/iasc.2022.023149 fatcat:qodg2fvehre7fb53s2uhhg4hbi

Smart Glove and Hand Gesture-based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

Kianoush Haratiannejadi, Rastko R. Selmic
2020 IEEE Access  
Three validation layers have been implemented, including a human-based validation, classification validation, and the system validation.  ...  This paper introduces an adaptable, human-computer interaction method to control multirotor aerial vehicles in unsupervised, multi-subject environments.  ...  Subsequently, the system validation module transfers the discipline command to the re-trainer in the classification module.  ... 
doi:10.1109/access.2020.3045858 fatcat:pr5uwvbacrcvphmkgsmgdw6brq

Hardware acceleration of multi-view face detection

Junguk Cho, Bridget Benson, Ryan Kastner
2009 2009 IEEE 7th Symposium on Application Specific Processors  
In our architecture, the multi-view face detection system generates rotated image windows and their integral image windows for each classifier which perform parallel classification operations to detect  ...  non-upright (rotated) and non-frontal (profile) faces in the images.  ...  It is the critical module of the whole multi-view face detection system.  ... 
doi:10.1109/sasp.2009.5226339 dblp:conf/sasp/ChoBK09 fatcat:fc5v7xe2kzbcjhaplojbrmnwqu

Preliminary Design of a Recognition System for Infected Fish Species Using Computer Vision [chapter]

Jing Hu, Daoliang Li, Qingling Duan, Guifen Chen, Xiuli Si
2012 IFIP Advances in Information and Communication Technology  
For the purpose of classification of fish species, a recognition system was preliminary designed using computer vision.  ...  Secondly, color and texture features are extracted for those selected texture rectangle fish skin images. Finally, all the images were classified by multi-class classifier named SVMs.  ...  The computer vision system mainly consists of GPRS modem, image receiver module, image pre-processed module, image analysis module and image recognition module.  ... 
doi:10.1007/978-3-642-27281-3_60 fatcat:5uszwxni2jcdlf6xklcnqeayzi

MultiBiometric Fusion: Left and Right Irises based Authentication Technique

Leila Zoubida, Réda Adjoudj
2017 International Journal of Image Graphics and Signal Processing  
The obtained results have confirmed that the multi-biometric systems are better than the mono-modal systems according to their performance.  ...  Then the scores obtained are normalized by Min-Max method and the fusion is performed at score level by the combination of two methods: a combination method with a classification method.  ...  CLASSIFICATION OF MULTI-BIOMETRICS SYSTEMS The multi-biometric system integrated more than one biometric system for the identification or the authentication mode.  ... 
doi:10.5815/ijigsp.2017.04.02 fatcat:2oesp6t4jjb3tmg4ndgi6yp5cm

Misogynistic Meme Detection using Early Fusion Model with Graph Network [article]

Harshvardhan Srivastava
2022 arXiv   pre-print
pretrained models to get an effective image representation.  ...  The model receives as input meme image with its text transcription with a target vector.  ...  We focused our efforts on our primary approach of building a Multi-Modal-Multi-Task module that uses features from both images and text.  ... 
arXiv:2203.16781v1 fatcat:up7v3evu4rbsthlk5iwp5sh5aq

LPI Radar Waveform Recognition Based on Multi-resolution Deep Feature Fusion

Xue Ni, Huali Wang, Fan Meng, Jing Hu, Changkai Tong
2021 IEEE Access  
CLASSIFICATION METHOD The classification mehod based on the deep convolutional network have four modules: multi-resolution network structure, interactive feature fusion module, classification fusion and  ...  SYSTEM STRUCTURE The classification system consists of three parts: signal processing, feature extraction and fusion, and classification, as shown in Figure 1 .  ... 
doi:10.1109/access.2021.3058305 fatcat:2tf32a2yrjahpfgyaxkpqysbfe

Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation [article]

Tong Shen, Guosheng Lin, Chunhua Shen, Ian Reid
2017 arXiv   pre-print
This simple but effective module can be easily integrated into any semantic segmentation systems.  ...  We address this issue by proposing a dense multi-label network module that is able to encourage the region consistency at different levels.  ...  Here we propose a dense multi-label module to take advantage of multi-label classification and integrate it into semantic segmentation systems.  ... 
arXiv:1701.07122v1 fatcat:bn74e6p3szg35b5blcsnrrlmqu

Multi-Mission Earth Observation Data Processing System

Mhangara, Mapurisa
2019 Sensors  
The modularized, multi-mission data processing enabled seamless end-to-end image processing, as demonstrated by the capability of the multi-mission system to execute geometric and radiometric corrections  ...  We have presented the architectural framework for the multi-mission processing system, which is comprised of five processing modules, i.e., the data ingestion module, a radiometric and geometric processing  ...  algorithms that have been integrated into the multi-mission data processing system.  ... 
doi:10.3390/s19183831 fatcat:7i4fly3lfzg7pmzngxw5ju37cq

New convolutional neural network model for screening and diagnosis of mammograms

Chen Zhang, Jumin Zhao, Jing Niu, Dengao Li, Ruxandra Stoean
2020 PLoS ONE  
Our work mainly focuses on the construction of multi-scale convolution module and attention module.  ...  In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature  ...  Influence of multi-scale convolution module on classification performance.  ... 
doi:10.1371/journal.pone.0237674 pmid:32790772 fatcat:726h3wjwingxhe3kgecltvtp7a

Efficient Traffic-Sign Recognition with Scale-aware CNN [article]

Yuchen Yang, Shuo Liu, Wei Ma, Qiuyuan Wang, Zheng Liu
2018 arXiv   pre-print
The classification network fuses multi-scale features as representation and adopts an "Inception" module for efficiency.  ...  The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images.  ...  The system adopted an FCN with a dual multi-scale CNN architecture to detect traffic signs of different scales, and a concise CNN structure to fuse multi-scale features for classification.  ... 
arXiv:1805.12289v1 fatcat:tik7pgexbvhbvk3h42igaa5vpu

AG-MIC: Azure-Based Generalized Flow for Medical Image Classification

Sohini Roychowdhury, Matthew Bihis
2016 IEEE Access  
viz., binary classification, multi-class learning, regression and so on.  ...  Medical image-based research requires heavy computational workload associated with image analysis and collaborative device independent platforms to incorporate expert opinions from multiple institutions  ...  ACKNOWLEDGMENT The authors would like to thank Devin Nakahara and Vinh Le for their contributions on 'R' module development.  ... 
doi:10.1109/access.2016.2605641 fatcat:nh7jfgnbvzcihod6cyndyk47le

Wavelet Packet Texture Descriptors Based Four-class BIRADS Breast Tissue Density Classification

Indrajeet Kumar, H.S. Bhadauria, Jitendra Virmani
2015 Procedia Computer Science  
The present work proposes a computer aided diagnostic (CAD) system for four-class BIRADS breast tissue density classification.  ...  From each sub-band image three multi-resolution texture descriptors (i.e., mean, standard deviation and energy descriptors) are computed , resulting in texture feature vector of length 48 (16 X 3) for  ...  Proposed CAD System The CAD system consists of mainly three modules: (1) ROI extraction module (2) feature extraction module and (3) classification module.  ... 
doi:10.1016/j.procs.2015.10.042 fatcat:rfluan4jrngabaruj2agknl7s4
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