WITHDRAWN: Feature extraction of underwater images using principle component analysis with image registration

Islabudeen, M.; Vigneshwaran, P.; Madhuri, G Sindhu; Kumar, Brijesh; Ragaventhiran, J.; Sharmila, G.

doi:10.1016/j.matpr.2021.03.341

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The future work of this is to work on gaining the consistency of images. [23]Yang, M., Hu, K., Du, Y., Wei, Z., Sheng, Z., & Hu, J explained a conditional GAN architecture is used for image enhancement by multi scale generator and dual discriminator. The multi-scale generator is used extraction and the dual discriminator improves the quality of the images and the loss function is used in optimization of the process.…”

Section: Literature Surveymentioning

confidence: 99%

Image Enhancement of Underwater Images using Deep Learning Techniques

Sravani¹,

Sai²,

Noomika³

et al. 2023

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

The quality of underwater images is very important for research purpose and ocean exploration. The market for high-quality underwater images is expanding exponentially. Despite this, there are a number of issues including blurriness, dim lighting, poor contrast and color fidelity. Light attenuation, absorption and scattering lower the quality. As a result, it is essential to enhance underwater images, and different image processing techniques are used for enhancement .The deep learning methodology used in this study improves image quality and produce the best outcomes when compared to conventional image enhancing techniques. The architecture used is GAN with the combination of Image processing algorithms as a preprocessing step for implementing deep learning models .The algorithms include Histogram Equalization and Dark Channel Prior. These methods normalizes the contrast of images and removes the hazing or dark parts of the images. The dataset used in this study is EUVP (Paired) which contains about 2186 real-world underwater images. Three parameters, UICM, MSE and PSNR are used to assess quality of enhanced images. The optimum technique for improving the images is determined by comparing the performance of the models after the dataset has been trained through them.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Image Enhancement of Underwater Images using Deep Learning Techniques

Sravani¹,

Sai²,

Noomika³

et al. 2023

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

show abstract

“…PCA is a process of data mining and data dimension reduction. After receiving adequate data information, if all the data are calculated directly, the whole analysis process calculation will be too large, and the accuracy of the calculation will decrease (Islabudeen et al, 2021). PCA converts indicators to independent comprehensive index, and reduces the number of indicators.…”

Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

Inspection and classification of wheat quality using image processing

Zhu

Sun²,

Cai

et al. 2023

Qual. Assur. Saf. Crops Foods

View full text Add to dashboard Cite

Wheat plays an important role in our daily life and industrial production. Several computer vision approaches have been proposed for classifying wheat quality, but there were some methods focusing on the problem of cohesive wheats while image processing. In this paper, we designed a single kernel guide groove to separate the cohesive wheats, which could simplify the algorithm of image processing and improve the accuracy rate of classification. For the method followed while recording the data, the image information must be converted into digital information, and the results are provided using appropriate image processing algorithms. Image preprocessing steps such as binarization, image enhancement, image segmentation, and morphological processing were used to reduce noise. For image segmentation, we proposed the following new segmentation methods: (1) extracting wheat region by converting image to H channel and (2) watershed algorithm based on Euclidean distance transformation. For the classification model, 22 features of 7 different qualities of wheat were inputted in the Back Propagation (BP) neural network and Support Vector Machine (SVM) model, and the overall correct classification rates were determined to be 91% and 97% for SVM and BP neural network, respectively. The BP neural network was more suitable for wheat appearance quality detection.

show abstract

“…has a substantial impact on a wide range of monitoring areas. To connect with other networking sensors, the wireless device runs on rechargeable batteries [4]. The focus of this research is to track and monitor animals' function and health.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Diagnose the Animal Health Continuous Monitoring Using IoT Based Sensory Data

Kandepan¹,

Venkatesan²

2022

I2M

View full text Add to dashboard Cite

Monitoring the health of humans in everyday life is quite challenging. Smart wearables have been increasingly important in recent technologies for monitoring and indicating our current health status, which is utilised to appropriately diagnose our health. Monitoring animals' health raises several concerns. We must wait for veterinary specialists to assess and diagnose whether the animals are suffering only in known regions. The result is delayed treatment and degradation of animal health. Therefore, primary health diagnosis is needed. However, mounting equipment was required to identify these features. This research leads to the recognition of the behaviour of animals through this technique, which facilitates the assessment of the health of animals. The use of Wireless Sensor Networks (WSN) and IoT features an Animals Smart Healthcare Monitoring (ASHM) system for this research to track animal behavior with high precision using sensors and to detect animals' health with great precision. We proposed designing a system that would track the movement and health of an animal. This can be achieved by developing a system with animal smart health monitoring system. The sensor can be mounted on the animal body to get the desired physiological parameters like temperature, heart rate, respiratory rate, ECG, and Blood pressure. This health surveillance system method has reduced the mean latency by 75% and provides results with an output accuracy of 98%.

show abstract

WITHDRAWN: Feature extraction of underwater images using principle component analysis with image registration

Cited by 5 publications

References 10 publications

Image Enhancement of Underwater Images using Deep Learning Techniques

Image Enhancement of Underwater Images using Deep Learning Techniques

Inspection and classification of wheat quality using image processing

A Novel Approach to Diagnose the Animal Health Continuous Monitoring Using IoT Based Sensory Data

Contact Info

Product

Resources

About