• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.67-73
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• 2020

In this paper, we present a CNN-based gesture recognition approach which reduces the memory burden of input data. Most of the neural network-based gesture recognition methods have used a sequence of frame images as input data, which cause a memory burden problem. We use a motion history image in order to define a meaningful gesture. The motion history image is a grayscale image into which the temporal motion information is collapsed by synthesizing silhouette images of a user during the period of one meaningful gesture. In this paper, we first summarize the previous traditional approaches and neural network-based approaches for gesture recognition. Then we explain the data preprocessing procedure for making the motion history image and the neural network architecture with three convolution layers for recognizing the meaningful gestures. In the experiments, we trained five types of gestures, namely those for charging power, shooting left, shooting right, kicking left, and kicking right. The accuracy of gesture recognition was measured by adjusting the number of filters in each layer in the proposed network. We use a grayscale image with 240 × 320 resolution which defines one meaningful gesture and achieved a gesture recognition accuracy of 98.24%.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.86-96
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• 2020

Based on the use cases of image recognition technology, this study looked at how artificial intelligence plays a role in image recognition technology. Through image recognition technology, satellite images can be analyzed with artificial intelligence to reveal the calculation of oil storage tanks in certain countries. And image recognition technology makes it possible for searching images or products similar to images taken or downloaded by users, as well as arranging fruit yields, or detecting plant diseases. Based on deep learning and neural network algorithms, we can recognize people's age, gender, and mood, confirming that image recognition technology is being applied in various industries. In this study, we can look at the use cases of domestic and overseas image recognition technology, as well as see which methods are being applied to the industry. In addition, through this study, the direction of future research was presented, focusing on various successful cases in which image recognition technology was implemented and applied in various industries. At the conclusion, it can be considered that the direction in which domestic image recognition technology should move forward in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.1-12
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• 2016

Digital imaging technology has developed into a state-of-the-art IT convergence, composite industry beyond the limits of the multimedia industry, especially in the field of smart object recognition, face - Application developed various techniques have been actively studied in conjunction with the phone. Recently, face recognition technology through the object recognition technology and evolved into intelligent video detection recognition technology, image recognition technology object detection recognition process applies to skills through is applied to the IP camera, the image object recognition technology with face recognition and active research have. In this paper, we first propose the necessary technical elements of the human factor technology trends and look at the human object recognition based spFACS (Smile Progress Facial Action Coding System) for detecting smiles study plan of the image recognition technology recognizes objects. Study scheme 1). ASM algorithm. By suggesting ways to effectively evaluate psychological research skills through the image object 2). By applying the result via the face recognition object to the tooth area it is detected in accordance with the recognized facial expression recognition of a person demonstrated the effect of extracting the feature points.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.53-62
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• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.69-73
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• 2017

In this paper, we developed a real-time image recognition algorithm based on machine learning and tested the performance of the algorithm. The real-time image recognition algorithm recognizes the input image in real-time based on the machine-learned image data. In order to test the performance of the real-time image recognition algorithm, we applied the real-time image recognition algorithm to the autonomous vehicle and showed the performance of the real-time image recognition algorithm through the application of the autonomous vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1140-1145
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• 2003

Image normalization is one of the important areas in pattern recognition. Also, log-polar images are useful in the sense that their image data size is reduced dramatically comparing with conventional images and it is possible to develop faster pattern recognition algorithms. Especially, the log-polar image is very similar with the structure of human eyes. However, there are almost no researches on pattern recognition using the log-polar images while a number of researches on visual tracking have been executed. We propose an image normalization technique of log-polar images using momentums applicable for affine-invariant pattern recognition. We handle basic distortions of an image including translation, rotation, scaling, and skew of a log-polar image. The algorithm is experimented in a PC-based real-time vision system successfully.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.99-112
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• 2015

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.1-10
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• 2022

China is a big country in animal fur industry. The total production and consumption of fur are increasing year by year. However, the recognition of fur in the fur production process still mainly relies on the visual identification of skilled workers, and the stability and consistency of products cannot be guaranteed. In response to this problem, this paper proposes a feature fusion-based animal fur recognition network on the basis of typical convolutional neural network structure, relying on rapidly developing deep learning techniques. This network superimposes texture feature - the most prominent feature of fur image - into the channel dimension of input image. The output feature map of the first layer convolution is inverted to obtain the inverted feature map and concat it into the original output feature map, then Leaky ReLU is used for activation, which makes full use of the texture information of fur image and the inverted feature information. Experimental results show that the algorithm improves the recognition accuracy by 9.08% on Fur_Recognition dataset and 6.41% on CIFAR-10 dataset. The algorithm in this paper can change the current situation that fur recognition relies on manual visual method to classify, and can lay foundation for improving the efficiency of fur production technology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.887-890
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• 2012

Image recognition is one of the most important research directions of pattern recognition. Image based road automatic identification technology is widely used in current society, the intelligence has become the trend of the times. This paper studied the image segmentation algorithm theory and its application in road signs recognition system. With the help of image processing technique, respectively, on road signs automatic recognition algorithm of three main parts, namely, image segmentation, character segmentation, image and character recognition, made a systematic study and algorithm. The experimental results show that: the image segmentation algorithm to establish road signs recognition model, can make effective use of smart phone system and application.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.