Volume-1 Issue-1, December 2020 – Indian Journal of Image Processing and Recognition (IJIPR)

A1015021121

IJIPR Journal — Wed, 11 Aug 2021 04:22:28 +0000

The Indian Journal of Image Processing and Recognition (IJIPR) has ISSN 2582-8037 (online), open-access, peer-reviewed, periodical, bi-monthly international journal. This journal is published by Lattice Science Publication (LSP) in February, April, June, August, October and December. The aim of the journal is to publish high quality peer–reviewed original articles in the area of Image Processing and Recognition that covers Active Vision and Robotic Systems, Architecture of Imaging and Vision Systems, Autonomous Vehicles, Character and Handwritten Text Recognition, Chemical and Spectral Sensitization, Chemistry of Photosensitive Materials, Coating Technologies, Coding and Transmission, Cognitive Aspects of Image Understanding, Color Imaging, Communication of Visual Data, Data Fusion from Multiple Sensor Inputs, Display and Printing, Document Image Understanding, Generation and Display, Holography, Image Analysis and Interpretation, Image Capturing, Image Databases, Image Generation, Image Manipulation, Image Permanence, Image Processing, Image Processing Applications, Image Processing: Coding Analysis and Recognition, Image Representation, Image Sensing, Imaging Systems and Image Scanning, Implementation and Architectures, Latent Image, Materials for Electro-Photography, Monitoring and Surveillance, Network Architecture for Real-Time Video Transport, New Visual Services Over ATM/Packet Network, Non-Impact Printing Technologies, Object Modeling and Knowledge Acquisition, Photoconductors, Photographic Emulsions, Photopolymers, Prepress and Printing Technologies, Processing and Analysis, Protocols for Packet Video, Remote Image Sensing, Retrieval and Multimedia, Scene Modeling, Signal-Processing Aspects of Image Processing, Storage and Transmission, Video Coding Algorithms and Technologies for ATM/P. #Active Vision and Robotic Systems #Architecture of Imaging and Vision Systems #Autonomous Vehicles #Character and Handwritten Text Recognition #Chemical and Spectral Sensitization #Chemistry of Photosensitive Materials #Coating Technologies #Coding and Transmission #Cognitive Aspects of Image Understanding #Color Imaging #Communication of Visual Data #Data Fusion from Multiple Sensor Inputs #Display and Printing #Document Image Understanding #Generation and Display #Holography #Image Analysis and Interpretation #Image Capturing #Image Databases #Image Generation #Image Manipulation #Image Permanence #Image Processing #Image Processing Applications #Image Processing: Coding Analysis and Recognition #Image Representation #Image Sensing #Imaging Systems and Image Scanning #Implementation and Architectures #Latent Image #Materials for Electro-Photography #Monitoring and Surveillance #Network Architecture for Real-Time Video Transport #New Visual Services Over ATM/Packet Network #Non-Impact Printing Technologies #Object Modeling and Knowledge Acquisition #Photoconductors #Photographic Emulsions #Photopolymers #Prepress and Printing Technologies #Processing and Analysis #Protocols for Packet Video #Remote Image Sensing #Retrieval and Multimedia #Scene Modeling #Signal-Processing Aspects of Image Processing #Storage and Transmission #Video Coding Algorithms and Technologies for ATM/P #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Microsoft Mathematics as a Teaching and Learning Tool for Mathematics
V. Naganjaneyulu¹, Shaik Mohammed Ali², Mohd Irshad Ali³
¹V. Naganjaneyulu, Lords Institute of Engineering and Technology, Telangana, India.

²Shaik Mohammed Ali, Lords Institute of Engineering and Technology, Telangana, India.

³Mohd Irshad Ali, Lords Institute of Engineering and Technology, Telangana, India.

Manuscript received on 23 November 2020 | Revised Manuscript received on 07 December 2020 | Manuscript Accepted on 15 December 2020 | Manuscript published on 30 December 2020 | PP: 40-44 | Volume-1 Issue-1, December 2020 | Retrieval Number: A1003021121/2021©LSP

Open Access | Ethics and Policies | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This article depicts how the utilization of Microsoft Mathematics (MM) programming has encouraged for educators to build up their capacities in two viewpoints: 1) solving tough problem 2) developing students focused instructing course of actions. The specified models explain that for a portion of the intense issues can be introduced to the students, it is practically unworkable or extremely hard to physically make right drawings. To overcome this trouble, the utilization of MM programming has all the earmarks of being critical. Furthermore, the utilization of this product can energize students close enough for trouble fathoming just as present a straightforward and persuading way regarding approve the arrangement. Moreover, learners can develop precise visual portrayals to display genuine circumstances productively by utilizing changes in MM programming. This can spare time widely so students can give more consideration on the practical aspects and comprehension. Astounding instructing assignments that take advantage of MM programming can likewise effectively develop student’s mathematics understanding. Also an investigation is being done on engineering students after MM Training supports the statement of effectiveness of MM in teaching and learning process.

Keywords: Open Technology Integration, Mathematics Education, Microsoft Mathematics, Technology Application, Paired t-test.

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A1009021121

IJIPR Journal — Wed, 11 Aug 2021 04:08:26 +0000

Digital Image Classification of Mango and Coconut for Natham Taluk, Dindigul District using Sentinel-2a Optical Data
S. Parthipan
S. Parthipan, M.Tech, Geoinformatics, Department of Geography, University of Madras, Guindy, Chennai (Tamil Nadu), India.

Abstract: Remote sensing and GIS have been widely applied in agriculture. Several methods exist for mango classification of satellite data which can be utilized by the agricultural sector. This study focuses on using supervised classification approaches to classify mango and coconut plantations Natham taluk, Dindigul district Tamil Nadu. Sentinel 2A acquired on 3 rd February2018wasusedforimageclassification.Groundtruthdatacol lectionwasperformed through the taluk. The land use and land cover of the study area were distinguished into five classes viz., coconut, mango, cropland, settlements and waterbody. Supervised image classification technique such as Mahalanob is Distance, Maximum likelihood Classifier, Spectral angle mapper and Spectral correlation mapper methods were applied over the image. The accuracy measures, suchasproducer’ sacc uracy, user’ saccuracy, overall accuracy and kappa coefficient were estimated. The results showed that maximum likelihood supervised classifier had the highest overall accuracy of 51.4% while other supervised classifier such as Maha lanob is Distance (32.4%), Minimum Distance classifier (42.86%), Spectral Angle Mapper (42.85%), Spectral Angle Mapper (42.85%) and Spectral Correlation Mapper (34.53%) had lower accuracy. It is suggested to utilize multi-date data for classification for crop discrimination utilising the unique phenology of various crops for better accuracy.

Keywords: Mahalanobis, Coconut, Mango, Cropland, Settlements

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A1004021121

IJIPR Journal — Wed, 11 Aug 2021 04:00:03 +0000

Boundary Based Analysis of Image Fusion using Discrete Wavelet Transform
MonihaRangaraju¹, Dharani Krishnan², Balaji E³, Umesh K⁴
¹MonihaRangaraju, JSS Academy of Technical Education, Bangalore (Karnataka), India.

²Dharani Krishnan, JSS Academy of Technical Education, Bangalore (Karnataka), India.

³Balaji E, JSS Academy of Technical Education, Bangalore (Karnataka), India.

⁴Umesh K, JSS Academy of Technical Education, Bangalore (Karnataka), India.

Abstract: Image fusion is the process of conflating or combing two or more images into a single image in order to implicate the necessary information from the source images. There is plenty of technological advancement present in today’s medical imaging field. The main drawback is that each and every imaging modality has its own specialty and limitation. Thus, fusion is used to overcome the shortcoming of displaying vital information in multiple images. Fusion of CT and MRI images aids in extracting useful tidings present in both the hard and soft tissues of the human body. Image fusion of the two input images can be done using various methods such as max, min, mean, Left-Right (LR), Right-Left (RL), Up-Down (UD), Down-Up (DU). The majorly used fusion techniques in this paper include LR, RL, UD and DU fusion.

Keywords: Image fusion, CT, MRI,DWT, dmey and coif, LR, RL, UD, DU Fusion.

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A1007021121

IJIPR Journal — Wed, 11 Aug 2021 03:50:35 +0000

Method of Determining Housing Density From Images Captured Using Unmanned Aerial Vehicle (UAV)
Van B. Patiluna¹, Krystle Mae M. Labio², Marlowe Edgar C. Burce³
¹Van B. Patiluna, Dept. of Computer Engineering, University of San Carlos, Cebu, Philippines.

²Krystle Mae M. Labio, Dept. of Computer Engineering, University of San Carlos, Cebu, Philippines.

³Dr. Marlowe Edgar C. Burce, Dept. of Computer Engineering, University of San Carlos, Cebu, Philippines.

Abstract: Urban density is a study in urban planning which determines the number of structures in a certain land area. The data can be interpreted in a socio-economic context such as population, income level, health risks, etc. Therefore, monitoring urban areas particularly the growth in residential areas is an important step to control the expansion and minimizing risks of epidemics, natural and man-made disasters. It also provides data to the city planners to monitor and control the urban development and provide adequate services to the populace in these areas. Remote sensing has been utilised to determine urban density by using combination of satellite images, multispectral data or combination of both. These data sources are expensive and most of the time, outdated. Therefore, an alternative platform shall be used to capture aerial images. The platform proposed are unmanned aerial vehicles (UAV). The goal of this study is to detect the density of a certain urban area using only images captured from the UAV specifically an octocopter. Since there is only one data source, an adapted workflow utilizing object-based classification method implemented in eCognition software by Definiens Imaging GmbH which is based on Fractal Net Evolution Approach (FNEA). Object-based classification is used because of higher accuracy than per-pixel classification given that only the RGB images are the available data source. The density is calculated through the ratio of structures and the land area which will be evaluated and results will be assessed in the following index range: low density, average density and high density.

Keywords: UAV, octocopter, urban density, object-based classification.

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A1001021121

IJIPR Journal — Wed, 11 Aug 2021 03:39:49 +0000

Adopted Image Matching Techniques for Aiding Indoor Navigation
Mohamed Ramadan¹, Mohamed El-Tokhey², Ayman Ragab³, Tamer Fathy⁴, Ahmed Ragheb⁵
¹Mohamed Ramadn, Assistant Lecturer, Public Works Department, Ain Shams University/ Faculty of Engineering/ Cairo, Egypt.

²Mohamed El-Tokhey, Professors, Public Works Department, Ain Shams University/ Faculty of Engineering/ Cairo, Egypt.

³Ayman Ragab, Professors, Public Works Department, Ain Shams University/ Faculty of Engineering/ Cairo, Egypt.

⁴Tamer Fathy, Professors, Public Works Department, Ain Shams University/ Faculty of Engineering/ Cairo, Egypt.

⁵Ahmed Ragheb, Associate Professor, Public Works Department, Ain Shams University/ Faculty of Engineering/ Cairo, Egypt.

Abstract: Fast and accurate image matching is a very important task with various applications in computer vision and robotics. In this research, we compare the performance of all available feature detection techniques (HARRIS, GFTT, SIFT, SURF, STAR, FAST, ORB, MSER, Dense, and Simple Blob), feature description techniques (SIFT, SURF, BRIEF, and ORB), and image matching techniques (Brute Force, BruteForce-L1, Brute Force-Hamming, Brute Force-Hamming LUT, and Flann Based) against different kinds of geometric distortions and deformations such as scaling, rotation, fish-eye distortion, and shearing. To perform this task, we manually apply different types of transformations on original images and compute the matching evaluation parameters such as the number of key points in images, the processing time, and the matching accuracies for each algorithm and we will show that which algorithm is the best more robust against these distortions.

Keywords: Feature Detector, Feature Descriptor, Image Matching, Indoor Navigation, Open CV.

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