Geometric conditions for injectivity of 3D Bézier volumes

The critical component of HCI is face recognition technology. Emotional computing heavily relies on the identification of facial emotions. Applications for emotion-driven face animation and dynamic assessment are numerous (FER). Universities have started to support real-world face expression recognition research as a result. Short video clips are continually uploaded and shared online, building up a library of videos on various topics. The enormous amount of movie data appeals to system engineers and researchers of autonomous emotion mining and sentiment analysis. The main idea is that categorizing things may be done by looking at how individuals feel about specific issues. People might choose to have a basic or complex facial appearance. People worldwide continually express their feelings through their faces, whether they are happy, sad, or uncertain. An online user can visually express themselves through a video’s editing, music, and subtitles. Additionally, before the video data can be used, noise in the data must frequently be eliminated. Automatically figuring out how someone feels in a video is a challenging task that will only get harder over time. Therefore, this paper aims to show how facial recognition video analysis can be used to show how sentiment analysis can help with business growth and essential decision-making. To determine how people are affected by reviewers’ writing, we use a technique for deciding emotions in this analysis. The feelings in movies are assessed using machine learning algorithms to categorize them. A lightweight machine learning algorithm is proposed to help in Aspect-oriented emotion classification for movie reviews. Moreover, to analyze real and published datasets, experimental results are compared with different Machine Learning algorithms, i.e., Naive Bayes, Support Vector Machine, Random Forest, and CNN. The proposed approach obtained 84.72 accuracy and 79.24 sensitivity. Furthermore, the method has a specificity of 90.64 and a precision of 90.2. Thus, the proposed method significantly increases the accuracy and sensitivity of the emotion detection system from facial feature recognition. Our proposed algorithm has shown contribution to detect datasets of different emotions with symmetric characteristics and symmetrically-designed facial image recognition tasks.

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Improved algorithms for determining the injectivity of 2D and 3D rational Bézier curves

Zhao

Wang

et al. 2022

era

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<abstract><p>Bézier curves and surfaces are important to computer-aided design applications. This paper presents algorithms for checking the injectivity of 2D and 3D Bézier curves. An injective Bézier curve or surface is one that has no self-intersections. The proposed algorithms use recently proposed sufficient and necessary conditions under which Bézier curves are guaranteed to be non-self-intersecting. As well as a rigorous derivation of the proposed algorithms, we present a series of examples and derive the complexity and computation times of the proposed algorithms. We find that the complexity our algorithms is approximately $ O(m) $, representing an improvement over previous injectivity-checking algorithms.</p></abstract>

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Geometric conditions for injectivity of 3D Bézier volumes

Cited by 3 publications

References 25 publications

Computational design of planet regolith sampler based on Bayesian optimization

Computational design of planet regolith sampler based on Bayesian optimization

Movie Reviews Classification through Facial Image Recognition and Emotion Detection Using Machine Learning Methods

Improved algorithms for determining the injectivity of 2D and 3D rational Bézier curves

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