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Journal articles on the topic 'CNN'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Aditya, Kakde Nitin Arora Durgansh Sharma. "A COMPARATIVE STUDY OF DIFFERENT TYPES OF CNN AND HIGHWAY CNN TECHNIQUES." Global Journal of Engineering Science and Research Management 6, no. 4 (2019): 18–31. https://doi.org/10.5281/zenodo.2639265.

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In recent years, convolutional networks have shown breakthrough performance in image classification and detection. The main reason behind the performance of convnets is that they are inspired from the mammal’s visual cortex. In this paper, we have investigated the performance of four models that are Alexnet, Highway Convolutional Neural Network, Convolutional Neural Network and an evolutionary approach on highway convolutional neural network on the basis of train loss, test loss, train accuracy and test accuracy. These models are tested on two datasets that are WANG dataset and Simpsons

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Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Convolutional neural networks for leaf image-based plant disease classification." International Journal of Artificial Intelligence (IJ-AI) 8, no. 4 (2019): 328–41. https://doi.org/10.11591/ijai.v8.i4.pp328-341.

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Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system imp

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Sylvester, Judith, and Suzanne Huffman. "CNN." Newspaper Research Journal 24, no. 1 (2003): 22–30. http://dx.doi.org/10.1177/073953290302400102.

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Ghansham, More Omkar Patil Omkar More Mihir More Samadhan Suryavanshi Manisha Mali. "Comparison of Object Detection Algorithms CNN, YOLO and SSD." International Journal of Scientific Research and Technology 1, no. 11 (2024): 137–44. https://doi.org/10.5281/zenodo.14186397.

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Since 2015, numerous studies have concentrated on object detection, a crucial element of computer vision, using convolutional neural networks (CNN) and their various architectures. Key methods for object detection done by “YOLO (You Only Look Once)”, “CNN”, and “SSD (Single Shot Multibox Detector)”. This paper explores three representative series of methods based on “CNN, YOLO, and SSD”, providing solutions to challenges like bounding box prediction in CNNs. The strength of these algorithms are measured in terms of accuracy, processing speed, and

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Bertoni, Federico, Giovanna Citti, and Alessandro Sarti. "LGN-CNN: A biologically inspired CNN architecture." Neural Networks 145 (January 2022): 42–55. http://dx.doi.org/10.1016/j.neunet.2021.09.024.

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Bertoni, Federico, Giovanna Citti, and Alessandro Sarti. "LGN-CNN: A biologically inspired CNN architecture." Neural Networks 145 (January 2022): 42–55. http://dx.doi.org/10.1016/j.neunet.2021.09.024.

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7

Sheet, Sinan S. Mohammed, Tian-Swee Tan, Muhammad Amir As'ari, et al. "Convolution neural network model for fundus photograph quality assessment." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 915–23. https://doi.org/10.11591/ijeecs.v26.i2.pp915-923.

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The excellent quality of color fundus photograph is crucial for the ophthalmologist to process the correct diagnosis and for convolutional neural network (CNN) models to optimize output classification. As a result of main causes as acquire devises efficiency and experience of a physician most fundus photographs can have uneven illuminance, blur, and bad contrast, in addition to micro-features of retinal diseases, which need to force their contrast. Fundus photograph quality assessment method is proposed to find out the perfect enhanced color fundus Technique in fundoscopy photographs-based CNN

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Zimmermann, Patricia R. "Beyond CNN." Afterimage 33, no. 2 (2005): 15–16. http://dx.doi.org/10.1525/aft.2005.33.2.15.

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Zhan, Zhiwei, Guoliang Liao, Xiang Ren, et al. "RA-CNN." International Journal of Software Science and Computational Intelligence 14, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijssci.311446.

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Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance. Traditional methods usually require human intervention, while deep learning always has a trade-off between local and global features. To solve the problem that deep learning models generalize poorly for emotion analysis, this article proposed a semantic-enhanced method called RA-CNN, a classification model under a multi-semantic environm

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Khaydarova, Rezeda, Dmitriy Mouromtsev, Vladislav Fishchenko, Vladislav Shmatkov, Maxim Lapaev, and Ivan Shilin. "ROCK-CNN." International Journal of Embedded and Real-Time Communication Systems 12, no. 3 (2021): 14–31. http://dx.doi.org/10.4018/ijertcs.2021070102.

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The paper is dedicated to distributed convolutional neural networks on a resource constrained devices cluster. The authors focus on requirements that meet the users' needs. Based on this, architecture of the system is proposed. Two use cases of CNN computations on a ROCK-CNN cluster are mentioned, and algorithms for organizing distributed convolutional neural networks are described. Experiments to validate proposed architecture and algorithms for distributed deep learning computations are conducted as well.

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Wang, Peng-Shuai, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong. "O-CNN." ACM Transactions on Graphics 36, no. 4 (2017): 1–11. http://dx.doi.org/10.1145/3072959.3073608.

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12

Hayworth, Gene. "CNN/Money." Journal of Business & Finance Librarianship 10, no. 3 (2005): 53–60. http://dx.doi.org/10.1300/j109v10n03_06.

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13

Shwetambari Pandurang, Waghmare, Renu Praveen Pathak, and Imtiyaz Ahmad Wani. "BSO-CNN." Tehnički glasnik 19, no. 2 (2025): 203–14. https://doi.org/10.31803/tg-20231116101632.

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Urban water distribution networks must use pressure management to reduce water leakage by modifying storage tank pressure levels in response to variations in water demand. Since each demand node usually restricts the maximum pressure that may be applied, addressing pressure issues at individual nodes is also crucial. To overcome these difficulties, a brand-new Convolutional Neural Network (CNN) Pressure Optimization Model is proposed. This model collects real-time data on water levels and pressure by utilizing level and pressure sensors, and a Backtracking Search Optimization (BSO) model is us

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Javier, O. Pinzón-Arenas, and Jiménez-Moreno Robinson. "Comparison between handwritten word and speech record in real-time using CNN architectures." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4313–21. https://doi.org/10.11591/ijece.v10i4.pp4313-4321.

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This paper presents the development of a system of comparison between words spoken and written by means of deep learning techniques. There are used 10 words acquired by means of an audio function and, these same words, are written by hand and acquired by a webcam, in such a way as to verify if the two data match and show whether or not it is the required word. For this, 2 different CNN architectures were used for each function, where for voice recognition, a suitable CNN was used to identify complete words by means of their features obtained with mel frequency cepstral coefficients, while for

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Arena, P., S. Baglio, L. Fortuna, and G. Manganaro. "Chua's circuit can be generated by CNN cells." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 42, no. 2 (1995): 123–25. http://dx.doi.org/10.1109/81.372854.

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Liu, Hengshuai, Jianjun Li, Yuhong Tang, et al. "Spatiotemporal Action Detection Using 2D CNN and 3D CNN." Computers and Electrical Engineering 120 (December 2024): 109739. http://dx.doi.org/10.1016/j.compeleceng.2024.109739.

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17

Manatunga, Dilan, Hyesoon Kim, and Saibal Mukhopadhyay. "SP-CNN: A Scalable and Programmable CNN-Based Accelerator." IEEE Micro 35, no. 5 (2015): 42–50. http://dx.doi.org/10.1109/mm.2015.121.

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Kaur, Kamaljit, and Parminder Kaur. "BERT-CNN: Improving BERT for Requirements Classification using CNN." Procedia Computer Science 218 (2023): 2604–11. http://dx.doi.org/10.1016/j.procs.2023.01.234.

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19

Maulenov, K. S., and S. A. Kudubaeva. "COMPARATIVEANALYSISOFFACEDETECTORSHAAR, HOG, CNN." SERIES PHYSICO-MATHEMATICAL 5, no. 339 (2021): 74–82. http://dx.doi.org/10.32014/2021.2518-1726.87.

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20

Paula, Useche, Jimenez-Moreno Robinson, and Martínez Baquero Javier. "Algorithm of detection, classification and gripping of occluded objects by CNN techniques and Haar classifiers." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4712–20. https://doi.org/10.11591/ijece.v10i5.pp4712-4720.

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The following paper presents the development of an algorithm, in charge of detecting, classifying and grabbing occluded objects, using artificial intelligence techniques, machine vision for the recognition of the environment, an anthropomorphic manipulator for the manipulation of the elements. 5 types of tools were used for their detection and classification, where the user selects one of them, so that the program searches for it in the work environment and delivers it in a specific area, overcoming difficulties such as occlusions of up to 70%. These tools were classified using two CNN (convol

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21

Nadia, Shamsulddin Abdulsattar, and Nasser Hussain Mohammed. "Facial expression recognition using HOG and LBP features with convolutional neural network." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1350~1357. https://doi.org/10.11591/eei.v11i3.3722.

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In computer vision, automatic facial expression recognition (FER) continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary pattern (LBP) and histogram of oriented gradient (HOG), in which the classifier's hyperparameters are tailored to produce the best recognition accuracies across a single database or a small set of similar databases. This paper integrates the power of deep learning techniques with the LBP and HOG. The LBP and HOG are estimated from each image in the dataset. The resulting dataset is

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22

Wang, Peng-Shuai, Chun-Yu Sun, Yang Liu, and Xin Tong. "Adaptive O-CNN." ACM Transactions on Graphics 37, no. 6 (2019): 1–11. http://dx.doi.org/10.1145/3272127.3275050.

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23

Lule, Jack. "CNN at 25." Critical Studies in Media Communication 22, no. 4 (2005): 339. http://dx.doi.org/10.1080/07393180500288469.

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24

He, Kaiming, Georgia Gkioxari, Piotr Dollar, and Ross Girshick. "Mask R-CNN." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 2 (2020): 386–97. http://dx.doi.org/10.1109/tpami.2018.2844175.

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25

Chua, L. O., and T. Roska. "The CNN paradigm." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 40, no. 3 (1993): 147–56. http://dx.doi.org/10.1109/81.222795.

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26

ITOH, MAKOTO, and LEON O. CHUA. "DESIGNING CNN GENES." International Journal of Bifurcation and Chaos 13, no. 10 (2003): 2739–824. http://dx.doi.org/10.1142/s0218127403008375.

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A systematic design methodology for finding CNN parameters with prescribed functions is proposed. A given function (task) is translated into several local operations, and they are realized as stable states of the CNN system. Many CNN parameters (CNN genes) with the same functions can be easily derived by using this design methodology. A genetic algorithm based CNN gene design methodology is also proposed. Two new genetic "activation and inactivation" operations are introduced to generate CNN genes effectively. Many useful CNN genes can be obtained systematically from known genes by using these

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ITOH, MAKOTO, and LEON O. CHUA. "MULTIPURPOSE HYSTERESIS CNN." International Journal of Bifurcation and Chaos 14, no. 12 (2004): 4035–73. http://dx.doi.org/10.1142/s021812740401179x.

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In this paper, we propose a multipurpose hysteresis CNN (cellular neural network) made of first-order cells with hysteresis switches. The hysteresis CNN has applications not only in image processing, but also in pattern formation, nonlinear wave propagation and associative and dynamic memories, because each hysteresis CNN cell has two operating modes, namely, a bistable multivibrator mode and a relaxation oscillator mode.

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28

Moyo, Last. "The CNN defect." Journal of International Communication 17, no. 2 (2011): 121–38. http://dx.doi.org/10.1080/13216597.2011.589365.

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29

DOGARU, RADU, and LEON O. CHUA. "UNIVERSAL CNN CELLS." International Journal of Bifurcation and Chaos 09, no. 01 (1999): 1–48. http://dx.doi.org/10.1142/s021812749900002x.

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A cellular neural/nonlinear network (CNN) [Chua, 1998] is a biologically inspired system where computation emerges from a collection of simple nonlinear locally coupled cells. This paper reviews our recent research results beginning from the standard uncoupled CNN cell which can realize only linearly separable local Boolean functions, to a generalized universal CNN cell capable of realizing arbitrary Boolean functions. The key element in this evolutionary process is the replacement of the linear discriminant (offset) function w(σ)=σ in the "standard" CNN cell in [Chua, 1998] by a piecewise-lin

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30

Shree G, Gana, Afreen Khanam, Bhavana N.P, Divyashree K.M, Akshay K.S, and Shruthi U. "Traffic_Sign_Classification Using CNN." International Journal of Research Publication and Reviews 03, no. 12 (2022): 1981–86. http://dx.doi.org/10.55248/gengpi.2022.31262.

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Autonomous driving cars are booming these days and the demand for a robust traffic sign recognition system that assures safety by recognizing traffic signs accurately and fast is increasing. We all must have heard about self-driving cars in which the passenger can fully depend on the car for traveling. But to achieve level 5 autonomy, it is necessary for all the vehicles to understand and follow the traffic rules. In the global world of Artificial Intelligence (AI) and advancement in technologies, many researchers and big companies like Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, e

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Shujaat, Muhammad, Abdul Wahab, Hilal Tayara, and Kil To Chong. "pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters." Genes 11, no. 12 (2020): 1529. http://dx.doi.org/10.3390/genes11121529.

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A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and th

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32

Lee, Hyungtae, Sungmin Eum, and Heesung Kwon. "ME R-CNN: Multi-Expert R-CNN for Object Detection." IEEE Transactions on Image Processing 29 (2020): 1030–44. http://dx.doi.org/10.1109/tip.2019.2938879.

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33

Groshek, Jacob. "Homogenous Agendas, Disparate Frames: CNN and CNN International Coverage Online." Journal of Broadcasting & Electronic Media 52, no. 1 (2008): 52–68. http://dx.doi.org/10.1080/08838150701820809.

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Shao, Tianjia, Yin Yang, Yanlin Weng, Qiming Hou, and Kun Zhou. "H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis." IEEE Transactions on Visualization and Computer Graphics 26, no. 7 (2020): 2403–16. http://dx.doi.org/10.1109/tvcg.2018.2887262.

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35

Shustanov, A. V., and P. Y. Yakimov. "Modification of single-purpose CNN for creating multi-purpose CNN." Journal of Physics: Conference Series 1368 (November 2019): 052036. http://dx.doi.org/10.1088/1742-6596/1368/5/052036.

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Ms. Jyoti Pandurang Kshirsagar. "Leveraging Faster CNN (F-CNN) for Effective Breast Cancer Classification." Advances in Nonlinear Variational Inequalities 28, no. 2 (2024): 117–35. http://dx.doi.org/10.52783/anvi.v28.1855.

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Within the scope of this work, a novel classification method for the diagnosis of breast cancer that is based on deep learning is also described. In this particular instance of breast cancer, which is the most common form of cancer in females, early detection is absolutely necessary in order to get better treatment outcomes. Notwithstanding their effectiveness, traditional diagnostic techniques have drawbacks such high expenses and possible errors. The high dimensionality and instability in tumor morphology that are particular problems with breast cancer imaging are intended to be addressed by

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Kumar, Sumit, and Satish Kumar Singh. "Occluded Thermal Face Recognition Using Bag of CNN ($Bo$CNN)." IEEE Signal Processing Letters 27 (2020): 975–79. http://dx.doi.org/10.1109/lsp.2020.2996429.

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38

Anwar, Saeed, Nick Barnes, and Lars Petersson. "A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers." Electronics 12, no. 23 (2023): 4877. http://dx.doi.org/10.3390/electronics12234877.

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Fine-grained classifiers collect information about inter-class variations to best use the underlying minute and subtle differences. The task is challenging due to the minor differences between the colors, viewpoints, and structure in the same class entities. The classification becomes difficult and challenging due to the similarities between the differences in viewpoint with other classes and its own. This work investigates the performance of landmark traditional CNN classifiers, presenting top-notch results on large-scale classification datasets and comparing them against state-of-the-art fin

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Li, Yao, Zhongyuan (Jasper) Zhang, Olli Saarela, Divya Sharma, and Wei Xu. "Mediation CNN (Med-CNN) Model for High-Dimensional Mediation Data." International Journal of Molecular Sciences 26, no. 5 (2025): 1819. https://doi.org/10.3390/ijms26051819.

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Complex biological features such as the human microbiome and gene expressions play a crucial role in human health by mediating various biomedical processes that influence disease progression, such as immune responses and metabolic processes. Understanding these mediation roles is essential for gaining insights into disease pathogenesis and improving treatment outcomes. However, analyzing such high-dimensional mediation features presents challenges due to their inherent structural and correlations, such as the hierarchical taxonomic structures in microbial operational taxonomic units (OTUs), ge

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M., Naga Triveni. "Recognition of Finger mark using CNN." IJRSET JANUARY Volume 10 Issue 1 10, no. 1 (2023): 7–9. https://doi.org/10.5281/zenodo.8434408.

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In present-days, the technological development in the field of data collection, processing, storing along with the field of research in pattern recognition, machine learning and deep learning serves abiometric person recognition processing fingerprint. In this work, the proposed model is a classificationsystem to recognize and match images of fingerprints. ACNN architecture is used to develop a model for detection. The present study uses approach to ensure the performance of the system. Finger print recognition system used for identifies the entity who involved in the database helps to automat

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41

Dinesh, Reddy, and Karthik Abhinav. "Forecasting Stock Price using LSTM-CNN Method." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 1–8. https://doi.org/10.35940/ijeat.A3117.1011121.

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Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information.

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Thyagaraj, T. "Custom Convolution Neural Network for Breast Cancer Detection." International Journal of Engineering and Advanced Technology (IJEAT) 13, no. 2 (2023): 22–29. https://doi.org/10.35940/ijeat.B4334.1213223.

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<strong>Abstract: </strong>Breast cancer remains a serious global health issue. Leveraging the use of deep learning techniques, this study presents a custom Convolutional Neural Network (CNN) framework for the detection of breast cancer. With the specific objective of accurate classification of breast cancer, a framework is made to analyze high-dimensional medical image information. The CNN's architecture, which consists of specifically developed layers and activation components tailored for the categorization of breast cancer, is described in detail. Utilizing the BreakHis dataset, which comp

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Dwivedi, Ratnesh, Lefvre Sarah Partlow, and Swati Bute. "International Terrorism, Electronic Media-Operation and Regulation of TV News Channels During the Terrorism Coverage." PSYCHOLINGUISTICS 22, no. 1 (2017): 70–92. https://doi.org/10.5281/zenodo.1087598.

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<strong><em>ABSTRACT</em></strong> <em>The concept of globalization or internationalization of certain wars, which were result of terrorist activities worldwide, as well as the high attention of terrorism coverage worldwide broadcasting might open up better opportunities to journalists – particularly to those who work in democratic countries like U.S.A and India – to improve their coverage. The context is the key: the context of the operation methodology, follow of regulatory bodies guidelines,</em> <em> the journalistic culture and the global environment.</em> <em>It is very

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Mahesh, Subray Hegde, and Ramakanth Kumar P. Dr. "Server Room Monitoring System using CCTV." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 9 (2023): 18–22. https://doi.org/10.35940/ijitee.G9211.0811922.

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<strong>Abstract</strong>: Server Room is the main part when it comes to any organization since any malicious activity in the server room may bring down the whole server room bringing the work of organization to halt. Hence, we need a server room monitoring system which works on real time images and monitors using compressed circuit television (CCTV). The framework used for server room monitoring is YOLO (You Only look Once). YOLO employs Convolutional neural networks (CNN) for image processing. In CNN image must pass through different layers like Convolutional layer, pooling layer, ReLu layer

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Ding, Yi-hong, Jin-long Liu, Xu-ri Huang, Ze-sheng Li, and Chia-chung Sun. "C4N: The first CnN radical with stable cyclic isomers." Journal of Chemical Physics 114, no. 12 (2001): 5170–79. http://dx.doi.org/10.1063/1.1351884.

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46

Pallavi, Bora, and Kapil Chaudhary Dr. "Improved Image Denoising Methodology using Deep CNN Bilateral Filter Compared to Additional Methods." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 191–96. https://doi.org/10.35940/ijeat.D2372.0610521.

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Image Denoising techniques are widely used to remove the noise from the images. Due to the ease of the bilateral filter, it is used very often to remove the noise from the images. In this paper, a novel approach has been proposed to enhanced bilateral filter in conjunction with CNN as a booster to eliminate Gaussian noise from Grey images. Studies reveal that standard CNN using a bilateral filter is the best technique to eliminate Gaussian noise from images along with high PSNR values. This paper also performs a comparative study of the various existing techniques for image denoising with the

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P., Rama Santosh Naidu, Lavanya Devi G., and Venkata Ramana Kondapalli. "Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 2 (2020): 143–47. https://doi.org/10.35940/ijitee.B8285.1210220.

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In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient's condition regularly. This study concentrates on various MachineLearning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG sign

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KILIÇ, RECAI. "CHAOS SYNCHRONIZATION IN SC-CNN-BASED CIRCUIT AND AN INTERESTING INVESTIGATION: CAN A SC-CNN-BASED CIRCUIT BEHAVE SYNCHRONOUSLY WITH THE ORIGINAL CHUA'S CIRCUIT?" International Journal of Bifurcation and Chaos 14, no. 03 (2004): 1071–83. http://dx.doi.org/10.1142/s0218127404009600.

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In this work, after giving a complete verification of the continuous synchronization between two identical SC-CNN-based circuits depending on the driving variable, we have investigated the continuous synchronization phenomenon between SC-CNN-based circuit and Chua's circuit. PSpice simulation results confirm that SC-CNN-based circuit can behave synchronously with Chua's circuit in the case when very accurate parameter equalities are provided.

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Jamil, Nursuriati, Ali Abd Almisreb, Syed Mohd Zahid Syed Zainal Ariffin, N. Md Din, and Raseeda Hamzah. "Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?" Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 558. http://dx.doi.org/10.11591/ijeecs.v11.i2.pp558-566.

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Current deep convolution neural network (CNN) has shown to achieve superior performance on a number of computer vision tasks such as image recognition, classification and object detection. The deep network was also tested for view-invariance, robustness and illumination invariance. However, the CNN architecture has thus far only been tested on non-uniform illumination invariant. Can CNN perform equally well for very underexposed or overexposed images or known as uniform illumination invariant? This is the gap that we are addressing in this paper. In our work, we collected ear images under diff

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Nursuriati, Jamil, Abd Almisreb Ali, Mohd Zahid Syed Zainal Ariffin Syed, Md Din N., and Hamzah Raseeda. "Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?" Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 558–66. https://doi.org/10.11591/ijeecs.v11.i2.pp558-566.

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Abstract:

Current deep convolution neural network (CNN) has shown to achieve superior performance on a number of computer vision tasks such as image recognition, classification and object detection. The deep network was also tested for view-invariance, robustness and illumination invariance. However, the CNN architecture has thus far only been tested on non-uniform illumination invariant. Can CNN perform equally well for very underexposed or overexposed images or known as uniform illumination invariant? This is the gap that we are addressing in this paper. In our work, we collected ear images under diff

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