• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.43-49
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• 2018

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.124-127
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• 1997

In this paper, we propose the new face detection and tracking angorithm in sequential images which have complex background. In order to apply face deteciton algorithm efficently, we convert the conventional RGB coordiantes into CIE coordonates and make the input images insensitive to luminace. And human face shapes and colors are learned using ueural network's backpropagation. For variable face size, we make mosaic size of input images vary and get the face location with various size through neural network. Besides, in sequential images, we suggest face motion tracking algorithm through image substraction processing and thresholding. At this time, for accurate face tracking, we use the face location of previous. image. Finally, we verify the real-time applicability of the proposed algorithm by the simple simulation.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.377-378
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• 2007

This paper proposes an system to generate rolled-equivalent fingerprints by mosaicking sequential images captured by an toothless device. To capture rolled-equivalent fingerprints, previous works use multiple cameras. However, the method in this paper captures sequential fingerprint images with a single camera and mosaic the images by estimating the transform between images through optical flow.

• Journal of the Korea Convergence Society
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• v.5 no.3
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• pp.41-44
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• 2014

Sequential object analysis are playing vital role in real time application in computer vision and object detections.Measuring the similarity in two images are very important issue any authentication activities with how best to compare two independent images. Identification of similarities of two or more sequential images is also the important in respect to moving of neighborhoods pixels. In our study we introduce the morphological and shared near neighborhoods concept which produces a sufficient results of comparing the two images with objects. Considering the each pixel compare with 8-connectivity pixels of second image. For consider the pixels we expect the noise removed images are to be considered, so we apply the morphological transformations such as opening, closing with erosion and dilations. RGB of pixel values are compared for the two sequential images if it is similar we include the pixels in the resultant image otherwise ignore the pixels. All un-similar pixels are identified and ignored which produces the similarity of two independent images. The results are produced from the images with objects and gray levels. It produces the expected results from our process.

• Spatial Information Research
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• v.18 no.3
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• pp.73-83
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• 2010

This study presents a method to segment an image, employing the statistics observed at each pixel location across sequential frame images. In the acquisition and analysis of spatial information, utilization of digital image processing technique has very important implications. Various image segmentation techniques have been presented to distinguish the area of digital images. In this study, based on the analysis of the spectroscopic characteristics of sequential frame images that had been previously researched, an image segmentation method was proposed by using the randomness occurring among a sequence of frame images for a same scene. First of all, we computed the mean and standard deviation values at each pixel and found reliable pixels to determine seed points using their standard deviation value. For segmenting an image into individual regions, we conducted region growing based on a T-test between reference and candidate sample sets. A comparative analysis was conducted to assure the performance of the proposed method with reference to a previous method. From a set of experimental results, it is confirmed that the proposed method using a sequence of frame images segments a scene better than a method using a single frame image.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.121-124
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• 2005

A field-sequential color projection system can display color images using panel. However, it suffers from a characteristic trichromatic separation known as "color breakup". The viewing of images exhibiting color breakup may cause visual fatigue and mental stress. In this study, the authors examine, from the standpoint of human factors, the objective and subjective symptoms that can result from the viewing of images with color breakup.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1341-1347
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• 2007

The displacement field (Optical flow) has been calculated by bottom-up approaches based on local processing. In contrast with them, in this paper, a top-down approach based on expanding in turn from the lowest order mode the whole motion in an image pair of sequential images is proposed. The intensity of medical images usually represents a quantity which is conserved during the motion. Hence sequential images are ideally related by a coordinate transformation. The displacement field can be determined from the generalized moments of the two images. The equations which transform arbitrary generalized moments from a source image to a target image are expressed as a function of the displacement field. The appareent displacement field is then computed iteratively by a projection method which utilizes the functional derivatives of the linearized moment equations. This method is demonstrated using a pair of sequential heart images. For comparative evaluation, we applied Horn and Schunck's method, a standard multigrid method, and our proposed algorithm to sequential image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.331-341
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• 2002

In order to extract 3-dimensional information from 2-D image, stereo images are prerequisite. Moreover, for the measurement of moving objects, the synchronized sequential stereo images have to be captured and image matching should be implemented for determining the location of moving objects. In this research, a simple method computing 3-dimensional coordinates from sequential images of moving objects was implemented. The sequential stereo images were captured by a video camera with a beam splitter. Once video images were digitalized by frame grabber, the interest points were extracted and matched in each stereo image, and the coordinates of center of them are calculated using weighted average method. Then, 3-dimensional coordinates of moving objects were computed by DLT algorithms.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.115-118
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• 2003

In this paper we implemented a field-sequential stereoscopic endoscope system that can generate stereoscopic images with different perspective depth using LCD stutter. Re stereoscopic image is generated from stereoscopic adapter that has LCD shutter. We have compared the stereoscopic depth of a field-sequential stereoscopic endoscope system with that of the conventional endoscope system. And the implemented system is verified by evaluation the field-sequential stereoscopic image on a Monitor. This system will be use to medical instruments in time.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.904-911
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• 2022

Occlusion is one of the most challenging problems for computer vision-based construction monitoring. Due to the intrinsic dynamics of construction scenes, vision-based technologies inevitably suffer from occlusions. Previous researchers have proposed the occlusion handling methods by leveraging the prior information from the sequential images. However, these methods cannot be employed for construction object detection in non-sequential images. As an alternative occlusion handling method, this study proposes a data augmentation-based framework that can enhance the detection performance under occlusions. The proposed approach is specially designed for rebar occlusions, the distinctive type of occlusions frequently happen during construction worker detection. In the proposed method, the artificial rebars are synthetically generated to emulate possible rebar occlusions in construction sites. In this regard, the proposed method enables the model to train a variety of occluded images, thereby improving the detection performance without requiring sequential information. The effectiveness of the proposed method is validated by showing that the proposed method outperforms the baseline model without augmentation. The outcomes demonstrate the great potential of the data augmentation techniques for occlusion handling that can be readily applied to typical object detectors without changing their model architecture.