• Journal of IKEEE
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• v.18 no.3
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• pp.398-402
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• 2014

In this paper, we propose a parallel processing method of pedestrian detection algorithm based on ROI-CENTRIST. There is a difficulty in the real-time processing of pedestrian detection in the embedded environment, using the conventional pedestrian detection method. This problem can be solved by a parallel processing method of applying the ROI to the conventional algorithm. The proposed parallel processing method of pedestrian detection using ROI-CENTRIST show the result of 5.2 frames per second, which is about 10% improvement over the conventional pedestrian detection method based on CENTRIST.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.229-232
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• 2003

In this paper, we present an efficient algorithm to detect microcalcifications ROI (Regions of Interest) in digital mammograms using Linear filters. To efficiently detect microcalcifications ROI, we used three sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using mean filter and linear filters.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.253-264
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• 2009

This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.203-208
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• 2019

Fast operation of a CNN based object detection is important in many application areas. It is an efficient approach to reduce the size of an input image. However, it is difficult to find an area that includes a target object with minimal computation. This paper proposes a ROI detection method that is fast and robust to noise. The proposed method is not affected by a flicker line noise that is a kind of aliasing between camera and LED light. Fast operation is achieved by using down-sampling efficiently. The accuracy of the proposed ROI detection method is 92.5% and the operation time for a frame with a resolution of 640 × 360 is 0.388msec.

• Journal of IKEEE
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• v.16 no.4
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• pp.349-355
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• 2012

Information technology improves convenience, safety, and performance of automobiles. Recently, a lot of algorithms are studied to provide safety and environment information for driving, and lane detection algorithm is one of them. In this paper, we propose a lane detection algorithm that reduces the amount of calculation by reducing region of interest (ROI) after preprocessing. The proposed algorithm reduces the area of ROI a lot by determining the candidate regions near lane boundaries as V-ROI so that the amount of calculation is reduced. In addition, the amount of calculation can be maintained almost the same regardless of the resolutions of the input images by compressing the images since the lane detection algorithm does not require high resolution. The proposed algorithm is implemented using C++ and OpenCV library and is verified to work at 30 fps for realtime operation.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.113-122
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• 2013

In this paper, we propose the method that automatically corrects the composition of a picture stylishly as well as reliably by cropping pictures based on the Rule of Thirds. The region of interest (ROI) is extracted from a picture by applying the Saliency Map and the Image Segmentation technology, the composition of the photo is amended based on this area to satisfy the Rule of Thirds. In addition, since the face region of the person is added to ROI by the Face Detection technique and the composition is amended by the various scenario according to ROI, the little more natural picture is acquired. The experimental result shows that the photo of the corrected composition was naturally amended compared with the original photo.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.41-53
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• 2006

In this paper, an adaptive ROI(region of interest) detection system for spatialtemporal features is proposed. It utilizes spatiotemporal features for the purpose of detecting ROI. It is assumed that motion representing temporal visual conspicuity between adjacent frames takes higher priority over spatial visual conspicuity. Because objects or regions in motion usually draw stronger attention than others in motion pictures. In case of still images visual features that constitute topographic feature maps are used as spatial features. Comparative experiments with a human subjective evaluation show that correct detection rate of visual attention region is improved by exploiting both spatial and temporal features compared to the case of exploiting either feature.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.116-125
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• 2014

Road conditions can provide important information for driving safety in driving assistance systems. The input images usually include unnecessary information and they need to be analyzed only in a region of interest (ROI) to reduce the amount of computation. In this paper, a vision-based road ROI determination algorithm is proposed to detect the road region using the positional information of a vanishing point and line segments. The line segments are detected using Canny's edge detection and Hough transform. The vanishing point is traced by a Kalman filter to reduce the false detection due to noises. The road ROI can be determined automatically and adaptively in every frame after initialization. The proposed method is implemented using C++ and the OpenCV library, and the road ROIs are obtained from various video images of black boxes. The results show that the proposed algorithm is robust.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.222-224
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• 2009

This paper presents an algorithm that extracts road lane's specific information by using HSI color information and performance enhancement of lane detection base on vision processing of drive assist. As a preprocessing for high speed lane detection, the optimal extraction of region of interest for lane boundary(ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled and it also increases reliabilities by deleting edges those are misrecognized. Road lane is extracted with simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since noise can be removed by using saturation and brightness of HSI color model. Also it searches for the road lane's color information and extracts characteristics. The real road experimental results are presented to evaluate the effectiveness of the proposed method.