Comparative Study of Gradient Domain Based Image Blending Approaches

Tanuj, Maturi; Virigineni, Aishwarya; Mani, Apoorva; Subramani, R

doi:10.1109/icses52305.2021.9633858

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…Furthermore, P1 and P2 are input pictures [28]. Alpha blending for a wide range of α has been performed using MATLAB [29,30]. The architecture of accurate and inaccurate Full Adder cells is presented in Figure 7.…”

Section: Application Of Presented Full Addermentioning

confidence: 99%

A Novel High-Speed and Low-PDP Approximate Full Adder Cell for Image Blending

et al. 2023

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This paper presents a new and high-performance inaccurate Full Adder Cell utilizing the Carbon Nanotube Field Effect Transistor (CNFET) technology. Comprehensive simulations are performed at the transistor and application levels to justify the performance of our design. Simulations performed using the HSPICE tool confirm the significant improvement in the performance of the proposed circuit delay, power-delay product (PDP) and energy-delay product (EDP) compared to competitor designs. Additionally, via a MATLAB tool, the image blending (alpha blending) application uses inaccurate Full Adder cells. Software simulations confirm the suitable quality of the final images according to the image quality evaluation criteria.

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Section: Application Of Presented Full Addermentioning

confidence: 99%

A Novel High-Speed and Low-PDP Approximate Full Adder Cell for Image Blending

et al. 2023

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Stress Level Detection in Continuous Speech Using CNNs and a Hybrid Attention Layer

Subramani,

Suresh,

Donald

et al. 2023

Lecture Notes in Networks and Systems

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Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm

Chavali

Kandavalli

Sugash

et al. 2022

2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

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Grammar Detection, also referred to as Parts of Speech Tagging of raw text, is considered an underlying building block of the various Natural Language Processing pipelines like named entity recognition, question answering, and sentiment analysis. In short, forgiven a sentence, Parts of Speech tagging is the task of specifying and tagging each word of a sentence with nouns, verbs, adjectives, adverbs, and more. Sentiment Analysis may well be a procedure accustomed to determining if a given sentence's emotional tone is neutral, positive or negative. To assign polarity scores to the thesis or entities within phrase, in-text analysis and analytics, machine learning (ML) and natural language processing (NLP), approaches are incorporated. This Sentiment Analysis using POS tagger helps us urge a summary of the broader public over a specific topic. For this, we are using the Viterbi algorithm, Hidden Markov Model (HMM), Constraint-based Viterbi algorithm for POS tagging. By comparing the accuracies, we select the foremost accurate model's result for Sentiment Analysis for determining the character of the sentence.

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Comparative Study of Gradient Domain Based Image Blending Approaches

Cited by 7 publications

References 9 publications

A Novel High-Speed and Low-PDP Approximate Full Adder Cell for Image Blending

A Novel High-Speed and Low-PDP Approximate Full Adder Cell for Image Blending

Stress Level Detection in Continuous Speech Using CNNs and a Hybrid Attention Layer

Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm

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