• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1121-1129
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• 2022

Object recognition is carried out using RGB images in various object recognition studies. However, RGB images in dark illumination environments or environments where target objects are occluded other objects cause poor object recognition performance. On the other hand, IR images provide strong object recognition performance in these environments because it detects infrared waves rather than visible illumination. In this paper, we propose an RGB-IR fusion model, high-frequency interchange network (HINet), which improves object recognition performance by combining only the strengths of RGB-IR image pairs. HINet connected two object detection models using a mutual high-frequency transfer (MHT) to interchange advantages between RGB-IR images. MHT converts each pair of RGB-IR images into a discrete cosine transform (DCT) spectrum domain to extract high-frequency information. The extracted high-frequency information is transmitted to each other's networks and utilized to improve object recognition performance. Experimental results show the superiority of the proposed network and present performance improvement of the multispectral object recognition task.

• Fashion & Textile Research Journal
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• v.7 no.2
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• pp.231-236
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• 2005

The purposes of this study were, a more systemic and desirable improvement method of retraining program was researched for cultivation of field beauticians through acknowledgement of the environmental change and phenomenon of beauty shops by time and through recognition of current retraining situation. 1) In terms of acknowledgement of the necessity for beautician retraining, the item on the necessity for beautician retraining showed beauticians' high requirement for retraining. 2) The main problem of reeducation was that reeducation curricular of each organization have not been programed. 3) Presence education was the main thing to be completed for reeducation program improvement. We also could know that the improvement and complement of work environment, the education extension besides technology education, and the rapid acquisition of information as improvement were recognized importantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2853-2873
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• 2013

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants' daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants' characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.107-114
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• 2010

A guitar, a piano, and a violin are popular musical instruments for User Created Contents(UCC). However the patterns of audio signal generated by a guitar and a piano are too similar to differentiate. The difference between two musical instruments can be found by analyzing the frequency variation per each band near signal peaks. The distribution of probability on the existence of signal peaks based on Cumulative Histogram were applied to musical instrument recognition. Experiments with statistical models of the frequency variation per each band near signal peaks showed the 14% improvement of musical instrument recognition.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.699-707
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• 2015

Although digit character recognition has got a significant improvement in recent years, it is still challenging to achieve satisfied result if the data contains an amount of distracting factors. This paper proposes a novel digit character recognition approach using a multi-layer hierarchical model, Hybrid Restricted Boltzmann Machines (HRBMs), which allows the learning architecture to be robust to background distracting factors. The insight behind the proposed model is that useful high-level features appear more frequently than distracting factors during learning, thus the high-level features can be decompose into hybrid hierarchical structures by using only small label information. In order to extract robust and compact features, a stochastic 0-1 layer is employed, which enables the model's hidden nodes to independently capture the useful character features during training. Experiments on the variations of Mixed National Institute of Standards and Technology (MNIST) dataset show that improvements of the multi-layer hierarchical model can be achieved by the proposed method. Finally, the paper shows the proposed technique which is used in a real-world application, where it is able to identify digit characters under various complex background images.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.385-391
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• 2020

As deep learning technology has been developed and applied to various fields, it is gradually changing from an existing single image based application to a video based application having a time base in order to recognize human behavior. However, unlike 2D CNN in a single image, 3D CNN in a video has a very high amount of computation and parameter increase due to the addition of a time axis, so improving accuracy in action recognition technology is more difficult than in a single image. To solve this problem, we investigate and analyze various techniques to improve performance in 3D CNN-based image recognition without additional training time and parameter increase. We propose a time base ensemble using the time axis that exists only in the videos and an ensemble in the input frame. We have achieved an accuracy improvement of up to 7.1% compared to the existing performance with a combination of techniques. It also revealed the trade-off relationship between computational and accuracy.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.29-38
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• 2015

Human action recognition has become an important research topic in computer vision area recently due to many applications in the real world, such as video surveillance, video retrieval, video analysis, and human-computer interaction. The goal of this paper is to evaluate descriptors which have recently been used in action recognition, namely Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF). This paper also proposes new descriptors to represent the change of points within each part of a human body, caused by actions named as Histogram of Changing Points (HCP) and so-called Average Speed (AS) which measures the average speed of actions. The descriptors are combined to build a strong descriptor to represent human actions by modeling the information about appearance, local motion, and changes on each part of the body, as well as motion speed. The effectiveness of these new descriptors is evaluated in the experiments on KTH and Hollywood datasets.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2600-2608
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• 1998

This paper proposes an algorithm for off line recognition of handwritten characters, especially effective for large-set characters such as Korean and Chinese characters. The algorithm is based on a minimum distance dlassification method which is simple and easy to implement but suffers from low recognition performance. Two strategies have been developed to improve its performance; one is multi-stage pre-classification and the other is candicate reordering. Effectiveness of the algorithm has been proven by and experimet with the samples of 574 classes in a handwritten Korean character catabase named PE02, where 86.0% of recognition accuracy and 15 characters per second of processing speed have been obtained.

• Convergence Security Journal
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• v.12 no.4
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• pp.41-47
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• 2012

For competitiveness of national B2C (Business to Customer) service industry, improvement of process and analysis focused on customer and change of service system are needed. In other words, a business and an organization should deduce and provide what kind of services customers want. Then, evaluate customers' satisfaction and improve the service quality. To achieve this goal, accurate feedbacks from customers play an important role; however, there are not quantitative and standard systems a lot in nation. Recently, the researches about ICT (Information and Communication Technology) that can recognize emotion of human being are on the increase. The facial expression recognition among them is known as most efficient and natural human interface. This research analyzes about more efficient facial expression recognition and suggests a customer satisfaction feedback system using that.

• Journal of information and communication convergence engineering
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• v.13 no.3
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• pp.145-151
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• 2015

We describe in this paper a hardware-based improvement scheme of a real-time automatic speech recognition (ASR) system with respect to speed by designing a parallel feature extraction algorithm on a Field-Programmable Gate Array (FPGA). A computationally intensive block in the algorithm is identified implemented in hardware logic on the FPGA. One such block is mel-frequency cepstrum coefficient (MFCC) algorithm used for feature extraction process. We demonstrate that the FPGA platform may perform efficient feature extraction computation in the speech recognition system as compared to the generalpurpose CPU including the ARM processor. The Xilinx Zynq-7000 System on Chip (SoC) platform is used for the MFCC implementation. From this implementation described in this paper, we confirmed that the FPGA platform is approximately 500× faster than a sequential CPU implementation and 60× faster than a sequential ARM implementation. We thus verified that a parallelized and optimized MFCC architecture on the FPGA platform may significantly improve the execution time of an ASR system, compared to the CPU and ARM platforms.