2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 7 , Issue 1 , PP: 40-50, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
M. Sumithra 1 * , G. Naveen Sundar 2 , B. Buvaneswari 3 , K. Sridharan 4 , V. D. Ambeth Kumar 5
Doi: https://doi.org/10.54216/JISIoT.070104
With the development of image handling technology, computerized technology, and the theory of image preparation, it has become clear that image processing is a crucial area of computer application. It is frequently used in many logical and designing applications, such as remote detection, medicine, meteorology, exchanges, and so on. However, with the swift development of picture preparation technology, it is becoming more and more important to precisely and successfully evaluate the quality of a picture. Recently, image quality evaluation has grown in importance as a study area in the field of developing picture data, which has attracted a lot of attention from academics. The importance of picture quality primarily takes into account two aspects: picture loyalty and picture coherence. picture quality directly depends on depending on the optical characteristics of the imaging equipment, image contrast, instrument clamor, and other factors. It may provide checking intentions to depict gaining, handling, and various connections through quality assessment. The evaluation of image quality assessment has become one of the essential breakthroughs of picture data designing to create a meaningful assessment of all components of picture preparation. People have needed to learn picture loyalty and the understandability of the quantitative estimation strategy using the picture a lot framework plan as the assessment premise for a very long time, but one of the people on the human visual characteristics is still not fully understood, in particular the description methods of psychological characteristics in human vision is also difficult to learn the quantitative evaluation of image quality, so, extensive investigation is required.
Image quality , Plane image , identification , system , Vehicle detection
[1] Shaofei Wu, A Traffic Motion Object Extraction Algorithm, International Journal of Bifurcation and Chaos,2015,25(14).Article Number1540039.
[2] SonCH， KimYT， LeeCH，etal.Six-colorseparationforimprovinggraininessinamiddle tone region. Electronic Imaging. 2004,5293.110-120.
[3] GAO, Shou-wu; LI, Tao; WANG, Dong. Tristimulus Value Measurement of Textile Materials with Digital Device. Journal of Applied Science and Engineering Innovation, 2017, 4.135-140.
[4] Nasyrova E F, Filatova M V. COLLEGE STUDENTS` ART, AND DESIGN COMPETENCE AS THE BASIS OF GRAPHIC DESIGN TEACHING. Tomsk State Pedagogical University Bulletin, 2017.95-98.
[5] Kawano Y, Yanai K. (2014), Food image recognition with deep convolutional features. 349(12). 589-593.
[6] Li H, Wei Y, Li Ll. (2013), Hierarchical Feature Extraction With Local Neural Response for Image Recognition. IEEE Transactions on Cybernetics, 43(2),412-424.
[7] Kominami Y, Yoshida S, Tanaka S. (2016), Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointestinal Endoscopy, 83(3).643-649.
[8] Nakhmani A, Tannenbaum A. (2013), A New Distance Measure Based on Generalized Image Normalized Cross-Correlation for Robust Video Tracking and Image Recognition. Pattern Recognit Lett, 34(3).315-321.
[9] Li B, Xiong W, Hu W. (2015), Web Horror Image Recognition Based on Context- Aware Multi- instance Learning. IEEE Transactions on Image Processing, 24(12).5193-5205.
[10] Gao Q, Huang Y, Zhang H. (2015), Discriminative sparsity preserving projections for image recognition. Pattern Recognition, 48(8).2543-2553.
[11] Liu C, Cao Y, Luo Y. DeepFood: Deep Learning-Based Food Image Recognition for Computer- Aided Dietary Assessment. 2016.37-48.
[12] Ajay G,Abhishek Kumar,Venkatesan R. (2021). Query-Based Image Retrieval using Support Vector Machine (SVM). Journal of Journal of Cognitive Human-Computer Interaction, 1 ( 1 ), 28-36.
[13] Shi J, Qi C. (2014), From Local Geometry to Global Structure: Learning Latent Subspace for Low-resolution Face Image Recognition. IEEE Signal Processing Letters, 22(5).554-558.
[14] Andronick J, Lewis C, Morgan C. (2015), Controlled Owicki-Gries Concurrency: Reasoning about the Preemptible eChronos Embedded Operating System. Lancet, 196(8057).10-24.
[15] Daum S, Borrmann A. (2014), Processing of Topological BIM Queries using Boundary Representation Based Methods. Advanced Engineering Informatics, 28(4).272-286.
[16] Machchhar J. (2015), A Computational Framework for Boundary Representation of Solid Sweeps. Computer-Aided Design and Applications, 12(2).181-191.
[17] Fitschen J H, Laus F, Steidl G. Dynamic Optimal Transport with Mixed Boundary Condition for Color Image Processing. 2015.558-562.
[18] Yuan Y T, Lin C Y, Zhao Y. A post processing algorithm for upsampling depth image based on boundary correction. Soil Science Society of America Journal, 2015(3).270.
[19] Suzuki T, Ikehara M. (2014), Reversible Symmetric Nonexpansive Convolution: An Effective Image Boundary Processing for, -Channel Lifting-Based Linear-Phase Filter Banks. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 23(6). 2744-9.
[20] Disheng Zheng,Kai Liang. (2020). Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption Technique for Wireless Sensor Networks. Journal of Journal of Intelligent Systems and Internet of Things, 1 ( 2 ), 80-92.
[21] M. Sumithra and Dr. S. Malathi, “A Novel Distributed Matching Global and Local Fuzzy Clustering (DMGLFC) FOR 3D Brain Image Segmentation for Tumor Detection”, IETE Journal of Research, doi.org/10.1080/03772063.2022.2027284, 2021
[22] B.Buvanswari and T.Kalpalatha Reddy, “A Review of EEG Based Human Facial Expression Recognition Systems in Cognitive Sciences” International Conference on Enenrgy, Communication,Data analytics and SoftComputing(ICECDS),CFP17M55-PRJ:978-1-5386-1886-8”,August 2017.
[23] M. Sumithra and Dr. S. Malathi, ” Modified Global Flower Pollination Algorithm-based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging”, International Journal of Imaging Systems and Technology, Vol. 31, Issue No.1, pp. 223-235, 2021
[24] K. Sridharan , and Dr. M. Chitra "SBPE: A paradigm Approach for proficient Information Retrieval , Jokull Journal" , Vol 63, No. 7;Jul 2013
[25] M. Sumithra and Dr. S. Malathi, “3D Densealex NET Model with Back Propagation for Brain Tumor Segmentation”, International Journal OfCurent Research and Review, Vol. 13, Issue 12, 2021.
[26] B.Buvaneswari and Dr.T. Kalpalatha Reddy,“EEG signal classification using soft computing techniques for brain disease diagnosis”,Journal of International Pharmaceutical Research ,ISSN : 1674-0440,Vol.46,No.1,Pp.525-528,2019.
[27] K. Sridharan , and Dr. M. Chitra "Web Based Agent And Assertion Passive Grading For Information Retervial", ARPN Journal of Engineering and Applied Sciences, VOL. 10, NO. 16, September 2015 pp:7043-7048
[28] M. Sumithra and Dr. S. Malathi, “Segmentation Of Different Modalitites Using Fuzzy K-Means And Wavelet ROI”, International Journal Of Scientific & Technology Research, Vol. 8, Issue 11, pp. 996-1002, November 2019.
[29] M. Sumithra and S. Malathi, “ A Survey of Brain Tumor Segmentation Methods with Different Image Modalitites”, International Journal of Computer Science Trends and Technology (IJCST) – Vol. 5 Issue 2, Mar – Apr 2017
[30] B.Buvaneswari and Dr.T. Kalpalatha Reddy, “High Performance Hybrid Cognitive Framework for Bio-Facial Signal Fusion Processing for the Disease Diagnosis”, Measurement,ISSN: 0263-2241, Vol. 140, Pp.89-99,2019.
[31] Shitiz Gupta,Shubham Agnihotri,Deepasha Birla,Achin Jain,Thavavel Vaiyapuri,Puneet Singh Lamba, Image Caption Generation and Comprehensive Comparison of Image Encoders, Journal of Fusion: Practice and Applications, Vol. 4 , No. 2 , (2021) : 42-55 (Doi : https://doi.org/10.54216/FPA.040202).
[32] M. Sumithra and S. Malathi, “A survey on Medical Image Segmentation Methods with Different Modalitites”, International Journal of Engineering Research and Technology (IJERT) – Vol. 6 Issue 2, Mar 2018.
[33] B.Buvaneswari and Dr.T. KalpalathaReddy,“ELSA- A Novel Technique to Predict Parkinson's Disease in Bio-Facial”,International Journal of Advanced Trends in Computer Science and Engineering, ISSN 2278-3091,Vol.8,No.1,Pp. 12-17,2019
[34] K. Sridharan , and Dr. M. Chitra , Proficient Information Retrieval Using Trust Based Search On Expert And Knowledge Users Query Formulation System, Australian Journal of Basic and Applied Sciences, 9(23) July 2015, Pages: 755-765.
[35] Rabeb Touati,Dr. Imen Ferchichi,Dr. Imen Messaoudi,Dr.Afef Elloumi Oueslati,Dr. Zied Lachiri, Pre-Cursor microRNAs from Different Species classification based on features extracted from the image, Journal of Journal of Cybersecurity and Information Management, Vol. 3 , No. 1 , (2020) : 05-13 (Doi : https://doi.org/10.54216/JCIM.030101)
[36] Sumithra, M., Shruthi, S., Ram, S., Swathi, S., Deepika, T., "MRI image classification of brain tumor using deep neural network and deployment using web framework", Advances in Parallel Computing, 2021, 38, pp. 614–617.
[37] K. Sridharan , and Dr. M. Chitra "RSSE: A Paradigm for Proficient Information Retrieval using Semantic Web" , Life Science Journal 2013;10(7s), pp: 418-425
