• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.643-661
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• 2018

This paper proposes a novel algorithm for lane detection based on inverse perspective transformation and Kalman filter. A simple inverse perspective transformation method is presented to remove perspective effects and generate a top-view image. This method does not need to obtain the internal and external parameters of the camera. The Gaussian kernel function is used to convolute the image to highlight the lane lines, and then an iterative threshold method is used to segment the image. A searching method is applied in the top-view image obtained from the inverse perspective transformation to determine the lane points and their positions. Combining with feature voting mechanism, the detected lane points are fitted as a straight line. Kalman filter is then applied to optimize and track the lane lines and improve the detection robustness. The experimental results show that the proposed method works well in various road conditions and meet the real-time requirements.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.96-111
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• 2022

With the rapid development of the Fourth Industrial Revolution and digital transformation, scientific and technological innovation measures are being devised to overcome Korea's low-growth, high-cost structure. Accordingly, by examining the R&D investment evaluation system of R&D PIE (R&D Platform for Investment and Evaluation), which has been promoted by the Korean government in response to the Fourth Industrial Revolution, from the perspective of R&D transformation, this study aims to explore a new path for a sustainable national science and technology innovation system following digital transformation. In particular, from the perspective of R&D PIE, a MLP (Multi-level Perspective), which had been conducted as an abstract theoretical study, was attempted with specific cases and analysis for each of the three layers: niche, landscape, and regime. In conclusion, R&D PIE was intended to elevate the abstract R&D investment evaluation system to a platform that leads innovation in the digital space of the Fourth Industrial Revolution. In addition, it was confirmed that the R&D PIE could be replaced or enhanced as a platform for innovation in response to the Fourth Industrial Revolution, thereby providing an alternative to job creation and an escape from economic crisis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3981-4004
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• 2019

This paper proposes a novel method for locating objects in real space from a single remote image and measuring actual distances between them by automatic detection and perspective transformation. The dimensions of the real space are known in advance. First, the corner points of the interested region are detected from an image using deep learning. Then, based on the corner points, the region of interest (ROI) is extracted and made proportional to real space by applying warp-perspective transformation. Finally, the objects are detected and mapped to the real-world location. Removing distortion from the image using camera calibration improves the accuracy in most of the cases. The deep learning framework Darknet is used for detection, and necessary modifications are made to integrate perspective transformation, camera calibration, un-distortion, etc. Experiments are performed with two types of cameras, one with barrel and the other with pincushion distortions. The results show that the difference between calculated distances and measured on real space with measurement tapes are very small; approximately 1 cm on an average. Furthermore, automatic corner detection allows the system to be used with any type of camera that has a fixed pose or in motion; using more points significantly enhances the accuracy of real-world mapping even without camera calibration. Perspective transformation also increases the object detection efficiency by making unified sizes of all objects.

• Fourth Industrial Review
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• v.2 no.1
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• pp.33-43
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• 2022

Purpose - With the rapid development of information technology, the digital economy has become an important grip of the new development pattern, and the digital transformation of enterprises is a major trend. As SMEs are the mainstay of the economy and employment, it is important to study the digital transformation of SMEs to promote the development of the digital economy. Research design, data, and methodology - Based on a resource-based view, this report analyses the current situation and issues of digital transformation of SMEs from the perspective of digital orientation as well as the breadth and depth of technological capabilities of enterprises, using data from a survey of 200 manufacturing enterprises in China. Result - The results show that SMEs generally have a digital transformation mindset, but a weak digital foundation. In order to execute a digital strategy, companies need digital transformation capabilities. These behavioral capabilities are guided by the strategic direction of the business, and companies with strong capabilities are always trying to tap into the full digital potential. Conclusion - In order to effectively promote the digital transformation of SMEs, we suggest that SMEs pay attention to digital capability building, make good use of digital platforms and network resources, and lay a solid foundation for digital transformation; meanwhile, we suggest that government departments play a guiding and supporting role to build a cross-industry and cross-sector digital synergy system to help promote the development of SMEs' digital transformation. This paper presents some suggestions from both the internal and external environment of the enterprise, with the expectation of contributing to the digital transformation practices of SMEs.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.235-238
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• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.

• International Journal of Contents
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• v.6 no.4
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• pp.1-7
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• 2010

Natural images often contain useful information about the scene such as text or company logos placed on a rectangular shaped plane. The 2D images captured from such objects by a camera are often distorted, because of the effects of the perspective projection camera model. This distortion makes the acquisition of the text information difficult. In this study, we detect the rectangular object on which the text is written, then the image is restored by removing the perspective distortion. The Hough transform is used to detect the boundary lines of the rectangular object and a bilinear transformation is applied to restore the original image.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.257-265
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• 2021

Although researches on digital transformation and competitive strategies are active, but research or alternatives considering fitness between them are still insufficient. Therefore, this study aims to identify the relationship between the types of digital transformation project and the innovation of competitive strategies, and to suggest strategic implications and alternatives for how to utilize digital transformation when establishing a competitive strategy. To this end, this study first derived 14 types of digital transformation in 4 areas through literature research. Then, the relevance of digital transformation and competitive strategy identified through previous studies and case analysis was identified, an effective link between competition strategy and digital transformation, and optimization of fitness between competition strategy and internal resources were presented. This study contributed in that it attempted to advance research on competitive strategies in an academic perspective by approaching them from the perspective of strategic fitness about digital transformation. In addition, in the industrial aspect, it contributed to the fact that it supported the systematic implementation of the competitive strategic response of companies that had to deal with the situation where they wanted to promote digital transformation wisely from the viewpoint of fitness.

• The Mathematical Education
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• v.47 no.4
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• pp.437-445
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• 2008

On the basis of the meaning and general process of geometric proof through transformation concept and understanding the geometric properties of linear transformation, this study showed that the centroid of geometrical figure and certain properties of a parabola and an ellipse in school mathematics can be explained as a conservative properties through linear transformation. From an educational perspective, this is a good example of showing the process of how several existing individual knowledge can be reorganized by a mathematical concept. Considering the fact that mathematical usefulness of linear transformation can be revealed through an invariable and conservation concept, further discussion is necessary on whether the linear transformation map included in the former curriculum have missed its point.