Journal of Innovative Image Processing - October 2019
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TOTAL DOCUMENTS

63
(FIVE YEARS 63)

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10
(FIVE YEARS 10)

Published By Inventive Research Organization

2582-4252
Updated Tuesday, 21 September 2021

2021 ◽  
Vol 3 (3) ◽  
pp. 269-283
Author(s):  
R. Kanthavel

To solve the challenges in traffic object identification, fuzzification, and simplification in a real traffic environment, it is highly required to develop an automatic detection and classification technique for roads, automobiles, and pedestrians with multiple traffic objects inside the same framework. The proposed method has been evaluated on a database with complicated poses, motions, backgrounds, and lighting conditions for an urban scenario where pedestrians are not obstructed. The suggested CNN classifier has an FPR of less than that of the SVM classifier. Confirming the significance of automatically optimized features, the SVM classifier's accuracy is equal to that of the CNN. The proposed framework is integrated with the additional adaptive segmentation method to identify pedestrians more precisely than the conventional techniques. Additionally, the proposed lightweight feature mapping leads to faster calculation times and it has also been verified and tabulated in the results and discussion section.


2021 ◽  
Vol 3 (3) ◽  
pp. 255-268
Author(s):  
Yasir Babiker Hamdan ◽  
A. Sathesh

Voting is now governed by regulations that specify how a person's choices may be communicated and their desires can be realized. This study proposes an electronic voting machine (EVM) as an alternative for traditional voting methods, which may include the manual utilization of only microcontroller-based circuits. With the identified fingerprint liveness, the proposed technique will make voting considerably easier, more effective, and less likely to result in fraud. The suggested model will support and advance the trustworthiness of all votes and it will also assist in streamlining the counting and verification process. It is difficult to demonstrate that an advanced voting system has been properly designed since several critical criteria must be satisfied. Poll results should be kept private in the database in order to preserve the data. The voting process must also show the votes obtained by the respective candidates. The proposed authenticated voting machine can be applied to the local area elections in order to speed up the process and make the election process more transparent. To maintain its theoretical strength, the proposed research idea needs further study. The model employs radio frequency and fingerprint recognition to maintain the protection.


2021 ◽  
Vol 3 (3) ◽  
pp. 240-254
Author(s):  
Subarna Shakya

Face recognition at a distance (FRAD) is one of the most difficult types of face recognition applications, particularly at a distance. Due to the poor resolution of facial image, it is difficult to identify faces from a distance. Recently, while recording individuals, the camera view is broad and just a small portion of a person's face is visible in the image. To ensure that the facial image has a low resolution, which deteriorates both face detection and identification engines, the facial image is constantly at low resolution. As an immediate solution, employing a high-definition camera is considered as a simple and practical approach to improve the reliability of algorithm and perform well on low-resolution facial images. While facial detection will be somewhat decreased, a picture with higher quality will result in a slower face detection rate. The proposed work aims to recognize faces with good accuracy even at a distance. The eye localization works for the face and eye location in the face of a human being with varied sizes at multiple distances. This process is used to detect the face quickly with a comparatively high accuracy. The Gaussian derivative filter is used to reduce the feature size in the storage element, which improves the speed of the recognition ratio. Besides, the proposed work includes benchmark datasets to evaluate the recognition process. As a result, the proposed system has achieved a 93.24% average accuracy of face recognition.


2021 ◽  
Vol 3 (3) ◽  
pp. 223-239
Author(s):  
S. Sairamkumar

In agriculture, crop yield estimation is critical. Remote sensing is being used in farming systems to increase yield efficiency and lower operating costs. Remote sensing-based strategies, on the other hand, necessitate extensive processing, necessitating the use of machine learning models for crop yield prediction. Descriptive analytics is a form of analytics that is used to accurately estimate crop yields. This paper discusses the most recent research on machine learning-based strategies for efficient crop yield prediction. In general, the training model's accuracy should be higher, and the error rate should be low. As a result, significant effort is being put forward to propose a machine learning technique that will provide high precision in crop yield prediction.


2021 ◽  
Vol 3 (3) ◽  
pp. 190-207
Author(s):  
S. K. B. Sangeetha

In recent years, deep-learning systems have made great progress, particularly in the disciplines of computer vision and pattern recognition. Deep-learning technology can be used to enable inference models to do real-time object detection and recognition. Using deep-learning-based designs, eye tracking systems could determine the position of eyes or pupils, regardless of whether visible-light or near-infrared image sensors were utilized. For growing electronic vehicle systems, such as driver monitoring systems and new touch screens, accurate and successful eye gaze estimates are critical. In demanding, unregulated, low-power situations, such systems must operate efficiently and at a reasonable cost. A thorough examination of the different deep learning approaches is required to take into consideration all of the limitations and opportunities of eye gaze tracking. The goal of this research is to learn more about the history of eye gaze tracking, as well as how deep learning contributed to computer vision-based tracking. Finally, this research presents a generalized system model for deep learning-driven eye gaze direction diagnostics, as well as a comparison of several approaches.


2021 ◽  
Vol 3 (3) ◽  
pp. 208-222
Author(s):  
B. Vivekanandam

In image/video analysis, crowds are actively researched, and their numbers are counted. In the last two decades, many crowd counting algorithms have been developed for a wide range of applications in crisis management systems, large-scale events, workplace safety, and other areas. The precision of neural network research for estimating points is outstanding in computer vision domain. However, the degree of uncertainty in the estimate is rarely indicated. Point estimate is beneficial for measuring uncertainty since it can improve the quality of decisions and predictions. The proposed framework integrates Light weight CNN (LW-CNN) for implementing crowd computing in any public place for delivering higher accuracy in counting. Further, the proposed framework has been trained through various scene analysis such as the full and partial vision of heads in counting. Based on the various scaling sets in the proposed neural network framework, it can easily categorize the partial vision of heads count and it is being counted accurately than other pre-trained neural network models. The proposed framework provides higher accuracy in estimating the headcounts in public places during COVID-19 by consuming less amount of time.


2021 ◽  
Vol 3 (3) ◽  
pp. 174-189
Author(s):  
P. Ebby Darney ◽  
I. Jeena Jacob

During the rainy season, many public outdoor crimes have been caught through video surveillance, and they do not have complete feature information to identify the image features. Rain streak removal techniques are ideal for indexing and obtaining additional information from such images. Furthermore, the rain substantially changes the intensity of images and videos, lowering the overall image quality of vision systems in outdoor recording situations. To be successful, the elimination of rain streaks in the film will require an advanced trial and error method. Different methods have been utilized to identify and eliminate the rainy effects by using the data on photon numbers, chromaticity, and probability of rain streaks present in digital images. This research work includes sparse coding process for removing rain streak by incorporating morphological component analyses (MCA) based algorithm. Based on the MCA algorithm, the coarse estimation becomes very simple to handle the rain streak or impulsive noisy images. The sparse decomposition of coarse is possible by estimating and eliminating all redundancies from the sources. This novel MCA approach is combined with sparsity coding process to provide better PSNR and less MSE results from the reconstructed images. This method is compared with of the existing research works on rain streak removal process. Besides, the obtained the results are illustrated and tabulated.


2021 ◽  
Vol 3 (3) ◽  
pp. 157-173
Author(s):  
S R Mugunthan

Due to unpredictability of climatic conditions across the world, early fire forecasting has become more challenging and critical for many oil and gas sectors. It is extremely hard for anyone to predict fires with any degree of certainty, especially in the gas or oil sectors. Until now, the models in use have not been adequate. However, this is critical in order to maintain workers and property safe. As a result, this research work investigates the different approaches available for fire hazard assessment and prediction in order to deal with fire dangers. Also, this research work presents the statistical machine learning methods to detect fire accidents in petroleum industries based on risk index models and risk assessment parameters by performing a statistical process. Moreover, this research work develops a statistical machine learning method to enhance the accuracy in predicting the fire occurrence. Finally, the proposed algorithm is measured by utilizing the performance metrics such as accuracy, proposed risk index, and sensitivity.


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.


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