ICTACT Journal on Image and Video Processing

R. Gayathri ¹

Affiliations
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN

Abstract

Image can be distorted by various ways including sensor inadequacy, transmission error, different noise factors and motion blurring. For controlling and maintaining the visual quality level of the image to be very high, it is very important to improve the image acquisition, image storage and image transmission, etc. Achieving high Peak Signal to Noise Ratio (PSNR) is essential goal of image restoration. This involves removing noises present in the image. Non-Local Means algorithm combined with Laplacian of Gaussian filter finds better results and produces good PSNR against impulse noise as well as Gaussian noise. Generally the effect of noise can be reduced using smooth filters for better results. Here, Laplacian of Gaussian (LoG) filter is applied for categorizing the edge and noisy pixels. Before that it is mandatory to obtain local smoothing of pixels. Finally the system performance is improved by averaging the non-local parameters. This is applicable to medical images also for removing impulse noise as well as Gaussian noise. The algorithm has been tested with MRI images and CT images efficiently. Better results are obtained in comparison with the previous methods with respect to better visual quality, PSNR and SSIM.

Keywords

Non-Local Means Filtering, Image Denoising, Impulse Detector, Impulse Noise, LoG Filter.

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C. Kiran Kumar ¹, R. Gayathri ², S. Thirukumaran ³, P. T. Kalaivaani ⁴

Affiliations
1 Department of Data Science, Codecraft Technologies, Bangalore, Karnataka, IN
2 Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
3 Department of Data Science, School of Science, Jain University, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN

Abstract

The use of ML methods with the objective of selecting wheat varieties that have a higher level of rust resistance encoded in their genomes is referred to as rust selection. In addition to that, the categorization of wheat illnesses by means of machine learning It has been attempted to classify wheat diseases by making use of a wide variety of machine learning techniques. In this paper, we develop an enhanced deep learning model to classify the disease present in the wheat plant. The study uses an improved convolutional neural network to classify the plant disease using a series of layers. The simulation is conducted in terms of the accuracy, precision, recall and f-measure. The results show that the proposed method achieves higher rate of accuracy than its predecessor.

Keywords

ML, Wheat Varieties, Rust Resistance, Disease.

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C. Kiran Kumar ¹, S. Chandra Sekaran ², R. Gayathri ³, S. Ramasamy ⁴

Affiliations
1 Data Science, Codecraft Technologies, Bangalore, IN
2 Department of Computer Science and Engineering, PSV College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
4 Department of Computer Science and Engineering, Hindusthan Institute of Technology, IN

Abstract

The only criteria that are used to evaluate the various neural network-based object identification models that are currently in use are the inference times and accuracy levels. The issue is that in order to put these new classes and situations to use in smart cities, we need to train on them in real time. We were not successful in locating any research or comparisons that were centered on the length of time necessary to train these models. As a direct consequence of this, the initial reaction times of these object identification models will consistently be quite slow (maybe in days). As a consequence of this, we believe that models that put an emphasis on the speed of training rather than accuracy alone are in significant demand. Users are able to gather photos for use in training in the present by utilizing concept names in online data collection toolkits; however, these images are iconic and do not have bounding boundaries. Under these conditions, the implementation of semi-supervised or unsupervised models in a variety of smart city applications might be able to contribute to an improvement in the precision of data derived from IoMT. In this study, we categorize the video clips into their appropriate classes using an improved ensemble classification model.

Keywords

CNN, Ensemble, Video Stream, IoT.

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R. Gayathri ¹, T. Gobinath ², A. Muthumari ³, R.S.V. Rama Swathi ⁴

Affiliations
1 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
2 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, University College of Engineering, IN
4 Department of Master of Business Administration, Koneru Lakshmaiah Education Foundation, IN

Abstract

In the era of rapid technological advancements, the demand for efficient and accurate identification and retrieval of information from multimedia images has seen a substantial increase. To meet this growing demand, artificial intelligence (AI)-based technologies, particularly feature extraction techniques, have gained significant popularity. Feature extraction involves the extraction of salient features from multimedia images, such as edges, lines, curves, textures, and colors, with the aim of representing the data in a more suitable format for analysis. This paper presents an enhanced AI-based feature extraction technique for multimedia image retrieval. The proposed method introduces a novel approach that combines the power of deep learning and evolutionary algorithms in a neuro-symbolic computation framework. Specifically, the renowned VGG16 deep learning algorithm is employed as the initial feature extractor. VGG16 is a state-of-the-art deep convolutional neural network that has demonstrated exceptional performance in various computer vision tasks, including image classification and feature extraction. The primary idea behind this approach is to leverage the capabilities of AI to extract the most discriminative features from the source images using VGG16. These features are then further refined using evolutionary algorithms, which employ a search and optimization process inspired by natural evolution. By iteratively improving the extracted features through the evolutionary algorithms, the method aims to enhance the discriminative power and representational quality of the extracted features. To evaluate the performance of the proposed approach, extensive experiments were conducted. The results demonstrate that the method achieves superior performance in terms of precision, recall, and F-measure when compared to conventional feature extraction techniques. Furthermore, a comprehensive comparison with state-of-the-art AI-based feature extraction techniques further highlights the potential and effectiveness of the proposed approach in multimedia image retrieval applications.

Keywords

Information Retrieval, Feature Extraction, Multimedia, Images.

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