Journal of Innovative Image Processing - October 2019
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54
(FIVE YEARS 54)

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10
(FIVE YEARS 10)

Published By Inventive Research Organization

2582-4252
Updated Friday, 09 July 2021

2021 ◽  
Vol 3 (2) ◽  
pp. 131-143
Author(s):  
Vijayakumar T.

Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves the accuracy of biometric system. In many developed countries, palm print features are employed to provide the most accurate identification of an actual individual as fast as possible. The proposed system can be very suitable for the person who dislikes answering many questions for security authentication. Moreover, the proposed system can also be used to minimize the extra questionnaire by achieving a highest accuracy than other existing multimodal biometric systems. Finally, the results are computed and tabulated in this research article.


2021 ◽  
Vol 3 (2) ◽  
pp. 144-156
Author(s):  
Senthil T. Kumar

The rapidly emerging virtual reality (VR) and augmented reality (AR) technologies have greatly improved the digital shopping experience and retail selling environment. In terms of practical applications and academic research, fragmentation in VR and AR contributes to the technology's multidisciplinary roots in terms of applications. In this paper, the retail applications and research works that make use of VR and AR technology are compared and analyzed. The implementation, consumer acceptance, applications, issues and other related terms are compared. This study establishes a foundation for future work in the retail applications field.


2021 ◽  
Vol 3 (2) ◽  
pp. 118-130
Author(s):  
Dhaya R

For implementing change detection approaches in image processing domain, spectral limitations in remotely sensed images are remaining as an unresolved challenge. Recently, many algorithms have been developed to detect spectral, spatial, and temporal constraints to detect digital change from the synthetic aperture radar (SAR) images. The unsupervised method is used to detect the appropriate changes in the digital images, which are taken between two different consecutive periods at the same scene. Many of the algorithms are identifying the changes in the image by utilizing a similarity index-based approach. Therefore, it fails to detect the original changes in the images due to the recurring spectral effects. This necessitated the need to initiate more research for suppressing the spectral effects in the SAR images. This research article strongly believes that the unsupervised learning approach can solve the spectral issues to correct in the appropriate scene. The convolutional neural network has been implemented here to extract the image features and classification, which will be done through a SVM classifier to detect the changes in the remote sensing images. This fusion type algorithm provides better accuracy to detect the relevant changes between different temporal images. In the feature extraction, the semantic segmentation procedure will be performed to extract the flattened image features. Due to this procedure, the spectral problem in the image will be subsided successfully. The CNN is generating feature map information and trained by various spectral images in the dataset. The proposed hybrid technique has developed an unsupervised method to segment, train, and classify the given input images by using a pre-trained semantic segmentation approach. It demonstrates a high level of accuracy in identifying the changes in images.


2021 ◽  
Vol 3 (2) ◽  
pp. 100-117
Author(s):  
Milan Tripathi

With the rapid urbanization and people moving from rural areas to urban time has become a very huge commodity. As a result of this change in people's lifestyles, there is a growing need for speed and efficiency. In the supermarket industry, item identification and billing are generally done manually, which takes a lot of time and effort. The lack of a bar code on the fruit products slows down the processing time. Before beginning the billing process, the seller may need to weigh the items in order to update the barcode, or the biller may need to input the item's name manually. This doubles the effort and also consumes a significant amount of time. As a result, several convolutional neural network-based classifiers are proposed to identify the fruits by visualizing via the camera for establishing a quick billing procedure in order to overcome this difficulty. The best model among the suggested models is capable of classifying pictures with start-of-art accuracy, which is superior than that of previously published studies.


2021 ◽  
Vol 3 (2) ◽  
pp. 85-99
Author(s):  
Edriss Eisa Babikir Adam ◽  
Sathesh A

In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansion of the neural network method, the performance of digital image processing based crack identification has recently diminished. Many single classifier approaches are used to detect the cracks with high accuracy. The classifiers are not concentrating on random fluctuation in the training dataset and also it reflects in the final output as an over-fitting phenomenon. Though this model contains many parameters to justify the training data, it fails in the residual variation. These residual variations are frequent in UAV recorded photos as well as many camera images. To reduce this challenge, a noise reduction technique is utilized along with an SVM classifier to reduce classification error. The proposed technique is more resourceful by performing classification via SVM approach, and further the feature extraction and network training has been implemented by using the CNN method. The captured digital images are processed by incorporating the bending test through reinforced concrete beams. Moreover, the proposed method is determining the widths of the crack by employing binary conversion in the captured images. The proposed model outperforms conservative techniques, single type classifiers, and image segmentation type process methods in terms of accuracy. The obtained results have proved that, the proposed hybrid method is more accurate and suitable for crack detection in concrete bridges especially in the unmanned environment.


2021 ◽  
Vol 3 (2) ◽  
pp. 75-84
Author(s):  
Smitha T. V. ◽  
Madhura S ◽  
Sindhu R ◽  
Brundha R

In this paper our aim is to provide a survey of mesh generation techniques for some Engineering applications. Mesh generation is a very important requirement to solve any problem by very popular numerical method known as Finite element method (FEM). It has several applications in various fields. One such technique is Automated generation of finite element meshes for aircraft conceptual design. It’s an approach for automated generation of fully connected finite element meshes for all internal structural components, given wing body, geometry model, controlled by a few conceptual level structural layout parameters. Another application where it is used is in the study of biomolecules to generate volumetric mesh of a biomolecule of any size and shape based on its atomic structure. These methods are proved to be a faster method due to the usage of computing techniques. Mesh generator is also used for creating finite element surface and volumetric mesh from 3D binary and gray scale medical images. Some of the applications include volumetric images, surface mesh extraction, surface mesh repairing and many more. It is of great importance in understanding the human brain which is a complex subject. Though 3D visualization is a useful tool available, yet it is inadequate due to its challenging computational problem. This paper also includes the survey on latest tools used for these applications which overcomes many problems associated with the conventional approaches.


2021 ◽  
Vol 3 (1) ◽  
pp. 66-74
Author(s):  
Ranganathan G

In the near future, deep learning algorithms will be incorporated in several applications for assisting the human beings. The deep learning algorithms have the tendency to allow a computer to work on its assumption. Most of the deep learning algorithms mimic the human brain’s neuron connection to leverage an artificial intelligence to the computer system. This helps to improve the operational speed and accuracy on several critical tasks. This paper projects the blocks, which are required for the incorporation of deep learning based algorithm. Also, the paper attempts to deeply analyze the necessity of the preprocessing step over several deep learning based applications.


2021 ◽  
Vol 3 (1) ◽  
pp. 52-65
Author(s):  
Thomas Amanuel ◽  
Amanuel Ghirmay ◽  
Huruy Ghebremeskel ◽  
Robel Ghebrehiwet ◽  
Weldekidan Bahlibi

This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.


2021 ◽  
Vol 3 (1) ◽  
pp. 36-51
Author(s):  
Samuel Manoharan J

Cloud computing models have emerged to be a key player in the field of information processing in the recent decade. Almost all the services related to data processing and storage from firms work on a cloud platform providing the requested services to the consumers at any point of time and location. Security is an essential concern in cloud models as they primarily deal with data. Since multitude of user’s access cloud by way of storing confidential information in the virtual storage platform or accessing vital data from archives, security and privacy is of prime concern. This has been taken as the motivation of this research work. An effective Chaotic based Biometric authentication scheme for user interaction layer of cloud is proposed and implemented in this research paper. The proposed method uses fingerprint as the biometric trait and varies from conventional methods by utilizing a N-stage Arnold Transform to securely verify the claim of the so-called legitimate user. The experimentations have been compared with existing benchmark methods and superior performances observed in terms of detections, false detection accuracy etc.


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