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RISS 인기검색어
- 검색키워드
- 저자 : Satya Prakash Yadav (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Fusion of Medical Images using a Wavelet Methodology: A Survey
Satya Prakash Yadav,Sachin Yadav 대한전자공학회 2019 IEIE Transactions on Smart Processing & Computing Vol.8 No.4
Image compression or fusion is the concept of identifying in-depth parameters of disease variables, and requires output images that preserve all the viable and prominent information that is gathered from source images without any further introduction of artifacts or unnecessary distortions. Measurement of images for prospective evaluation and image fusion depends on various performance measures, such as structure similarity index, standard deviation, edge detection, correlation coefficient and high pass correlation, average gradient, root-mean-square error, peak signal-to-noise ratio, entropy, etc. This review discusses various medical image fusion modalities focused on Principal Component Analysis, Independent Component Analysis, and wavelet transform. An introduction to the usefulness of such modalities is presented, suggesting safe hybrid modality combinations that could greatly enhance the image fusion process. Novel trends in medical image fusion techniques to achieve a perfectly desired, quality image, the future prospects of an ideal technique for medical imaging, and recognition of diseases are covered.
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Vision-based Detection, Tracking, and Classification of Vehicles
Satya Prakash Yadav 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.6
Motion capturing involves the identification of objects in motion and plotting their motion by analyzing them. When considered in regard to a video sequence, motion capturing can be defined as a procedure of unmasking objects in motion in sequences of frames using exhaustive and proficient digital image processing techniques. Methodology: To ingress need contrasting time for trial and their execution shows difference in terms of pace and memory requisites. Results: The result obtained shows the performance of the algorithms and models in the given conditions. It also reflects the best suited environment for the techniques. The work was accomplished by testing the algorithms for numerous sequences of input video. The identification rate analysis gives the identification rate of foreground pixels for various colors divergent from the background model. Conclusions: The objective of computer vision is to imitate human vision utilizing advanced digital images through three principle handling parts that are executed consistently, i.e., acquisition of pictures, picture processing, and picture investigation and comprehension. It has gained a lot of attention from researchers in an enormous field. The basic purpose of this study was to prospecting tracking and computer-aided object detection techniques. For this purpose, a number of methods were observed, studied critically, and used. Originality: In the study, an improved frame differencing method for tracking an object is briefly reviewed, and an improved version of the algorithm was also implemented using MATLAB. The proposed algorithm was tested over distinct video sequences, and it was observed that the objects in motion were identified with minimal error rate when compared to a traditional frame differencing method. Limitations: One of the major challenges in the process of tracking objects in motion is to design algorithms or techniques for tracking the objects present in disrupted or random videos, like videos attained from broadcast news networks or home videos. These videos contain noise, and some of them may be unstructured, compressed, and denominationally having edited pieces obtained in different ways by moving cameras.
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Speech Recognition using Machine Learning
Vineet Vashisht,Aditya Kumar Pandey,Satya Prakash Yadav 대한전자공학회 2021 IEIE Transactions on Smart Processing & Computing Vol.10 No.3
Speech recognition is one of the fastest-growing engineering technologies. It has several applications in different areas, and provides many potential benefits. A lot of people are unable to communicate due to language barriers. We aim to reduce this barrier via our project, which was designed and developed to achieve systems in particular cases to provide significant help so people can share information by operating a computer using voice input. This project keeps that factor in mind, and an effort is made to ensure our project is able to recognize speech and convert input audio into text; it also enables a user to perform file operations like Save, Open, or Exit from voice-only input. We design a system that can recognize the human voice as well as audio clips, and translate between English and Hindi. The output is in text form, and we provide options to convert audio from one language to the other. Going forward, we expect to add functionality that provides dictionary meanings for Hindi and English words. Neural machine translation is the primary algorithm used in the industry to perform machine translation. Two recurrent neural networks used in tandem to construct an encoder-decoder structure are the architecture behind neural machine translation. This work on speech recognition starts with an introduction to the technology and the applications used in different sectors. Part of the report is based on software developments in speech recognition.
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