Fuzzy Evaluation of Kansei Attributes Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Wei Jiang ◽  
Kai Zhang ◽  
Wu Zhao ◽  
Xin Guo
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Quantitative Evaluation ◽  
Subjective Evaluation ◽  
Fuzzy Classification ◽  
Fuzzy Evaluation ◽  
Emotional Needs ◽  
Quantitative Results ◽  
Attribute Evaluation ◽  
Statistical Time

Abstract The emotional needs for products have increased significantly with the recent improvements in living standards. Attribute evaluation forms the core of Kansei engineering in emotion-oriented products, and is practically quite subjective in nature. Essentially, attribute evaluation is a fuzzy classification task, whose quantitative results change slightly with statistical time and statistical objects, making it difficult to accurately describe using standard mathematical models. In this paper, we propose a novel deep-learning-assisted fuzzy attribute-evaluation (DLFAE) method, which could generate quantitative evaluation results. In comparison to existing methods, the proposed method combines subjective evaluation with convolutional neural networks, which facilitates the generation of quantitative evaluation results. Additionally, this strategy has better transferability for different situations, increasing its versatility and applicability. This, in turn, reduces the computational burden of evaluation and improves operational efficiency.

scholarly journals Avaliação de dor em expressão facial neonatal por meio de redes neurais profundas

2020 ◽  
Author(s):  
◽  
L. F. Buzuti
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pain Assessment ◽  
Neural Models ◽  
External Variable ◽  
Neonatal Pain ◽  
Face Images ◽  
Average Accuracy ◽  
Late Intervention ◽  
Quantitative Results

Neonatal pain assessment might suffer variation among health professionals, leading to late intervention and flimsy treatment of pain in several occasions. Therefore, it is essential to develop computational tools of pain assessment, less subjective and susceptible to external variable influences. Deep learning models, especially Convolutional Neural Networks, have gained ground in the last decade, due to many successful applications in image analysis, object recognitions and human emotion recognitions. In this context, the general aim this dissertation was analyse quantitatively and qualitatively models of Convolutional Neural Networks in the task neonatal pain classification through a computacional framework based in face images of two distinct databases (an international, named COPE, and other national, named UNIFESP). How specific aims were implemented, evaluated and compared the performance of three existent models used in literature: Neonatal Convolutional Neural Network (N-CNN) and two type of ResNet50 models. The quantitative results showed the excellence of N-CNN to neonatal pain assessment automatic, with average accuracy of 87.2% and 78.7% for the databases COPE and UNIFESP, respectively. However, the quantitative analysis showed that all neural models evaluated, including N-CNN models, can learn artifacts from the imagens and not variation discriminating in faces, thus showed the necessity more studies to apply this models in clinical practice

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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