• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1083-1094
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• 2020

Recognizing plant species based on leaf images is challenging because of the large inter-class variation and inter-class similarities among different plant species. The effective extraction of leaf descriptors constitutes the most important problem in plant leaf recognition. In this paper, a multi-scale angular description method is proposed for fast and accurate leaf recognition and retrieval tasks. The proposed method uses a novel scale-generation rule to develop an angular description of leaf contours. It is parameter-free and can capture leaf features from coarse to fine at multiple scales. A fast Fourier transform is used to make the descriptor compact and is effective in matching samples. Both support vector machine and k-nearest neighbors are used to classify leaves. Leaf recognition and retrieval experiments were conducted on three challenging datasets, namely Swedish leaf, Flavia leaf, and ImageCLEF2012 leaf. The results are evaluated with the widely used standard metrics and compared with several state-of-the-art methods. The results and comparisons show that the proposed method not only requires a low computational time, but also achieves good recognition and retrieval accuracies on challenging datasets.

• Annual Conference of KIPS
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• 2017.11a
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• pp.781-784
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• 2017

Face recognition systems are characterized by low invasiveness of acquisition, and increasingly better reliability. Such systems may not be applied effectively, when the images are in low resolution (LR) as in the case that photos are taken from long distances, typically public surveillance. In theory, the high resolution (HR) image reconstructed from an LR face image, applying a super resolution (SR) method, can be used for face recognition. However, existing face SR algorithms may not give satisfactory results in face recognition. This article investigates the very low resolution face recognition problem and introduces a partial differential equation (PDE)-based SR method for a face recognition system of convolutional neural network (CNN).

• Journal of Preventive Medicine and Public Health
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• v.25 no.3 s.39
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• pp.269-286
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• 1992

The purpose of this study is to investigate the present state of worker's recognition and attitude about hazard materials and occupational disease in his workplace. In general worker's view of hazard materials and occupational disease that sis recognition and attitude is related to worker's health behavior for preventing occupational disease and improving his health status. The study subject is composed of workers in the asbestos related industry for example brake lining manufacturing industry, asbestos fiber manufacturing industry, and asbestos slate manufacturing industry. The result of the study are follows : 1. The most of workers in the asbestos related industry have taken health education and safety education, and the more than half of workers recognized the usefulness of preventive device, and ventilatory device in workplace. 2. About 70% of workers have always taken the preventive device. 3. About 80% of workers have recognized occupational disease in the asbestos related industry, and about 64% of workers have recognized that hls workplace have harmful effect on his health. 4. Recognition about the usefulness of ventilatory device in work place has not related with any variables. But recognition about the usefulness of repiratory protector has related with recognition of hazard materials in his workplace, for example asbestos. 5. Attitude about severity and susceptability of occupational disease in the asbestos related industry have related with knowledge about hazard materials and occupational disease.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

• Dementia and Neurocognitive Disorders
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• v.22 no.4
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• pp.158-168
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• 2023

Background and Purpose: Facial emotion recognition deficits impact the daily life, particularly of Alzheimer's disease patients. We aimed to assess these deficits in the following three groups: subjective cognitive decline (SCD), mild cognitive impairment (MCI), and mild Alzheimer's dementia (AD). Additionally, we explored the associations between facial emotion recognition and cognitive performance. Methods: We used the Korean version of the Florida Facial Affect Battery (K-FAB) in 72 SCD, 76 MCI, and 76 mild AD subjects. The comparison was conducted using the analysis of covariance (ANCOVA), with adjustments being made for age and sex. The Mini-Mental State Examination (MMSE) was utilized to gauge the overall cognitive status, while the Seoul Neuropsychological Screening Battery (SNSB) was employed to evaluate the performance in the following five cognitive domains: attention, language, visuospatial abilities, memory, and frontal executive functions. Results: The ANCOVA results showed significant differences in K-FAB subtests 3, 4, and 5 (p=0.001, p=0.003, and p=0.004, respectively), especially for anger and fearful emotions. Recognition of 'anger' in the FAB subtest 5 declined from SCD to MCI to mild AD. Correlations were observed with age and education, and after controlling for these factors, MMSE and frontal executive function were associated with FAB tests, particularly in the FAB subtest 5 (r=0.507, p<0.001 and r=-0.288, p=0.026, respectively). Conclusions: Emotion recognition deficits worsened from SCD to MCI to mild AD, especially for negative emotions. Complex tasks, such as matching, selection, and naming, showed greater deficits, with a connection to cognitive impairment, especially frontal executive dysfunction.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.603-606
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• 1998

In a large number of words and the continued speech recognition system using a phoneme as teh recognition unit, it is necessary to segment processing. In this paper, a normalized AMDF new method. The suggested parameter represents a degree of sharpness at valley point. This method can detect the speech segment between the steady state and transient region to the continued speech without a prior information of speech signal.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.293-297
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• 1992

For the computerized monitoring and diagnosis of the undesirable chip chatter which are major obstacles to FMS, a pattern recognition system based on multi-layer perception neural network is developed and the performance of the system is experimentally evaluated. Experimental results show that recognition of the two class state of normal or abnormal cutting gives satisfactory results with success rate of 81`91%. Therefore, the proposed system has possibility for use in monitoring and diagnosis of automatic manufacturing system

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.217-218
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• 2007

Face recognition, recognizing the human faces, is one of the most important techniques for making intelligent robot that provide commendable services to human. In this paper, we make a comparative study of Original PCA, 2DPCA, 2DPCA based algorithms and LDA in robot environment. Database is obtained through the robot's camera in a laboratory what is made like home environment for experiment.. We consider distance state what can be generated in home environment for database.

• Convergence Security Journal
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• v.8 no.4
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• pp.135-143
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• 2008

Currently, The HMI system is being used on the network is limited in the ability. In this paper, an Industrial HMI applied the transformed state splitting algorithm. this study suggests by applying a transformed the Successive state splitting algorithm, for the modeling in the questions of the expected data. So, you can save time and reliable and precise as high as 98.15 percent repregented recognition rate. HMI system applied to the voice of industrial equipment the man can not act directly in the industry environment was able to drive devices. Optimize the performance of the engine was the voice of HMI system.