• 제목/요약/키워드: Morphological operations

검색결과 108건 처리시간 0.024초

Noise-robust Hand Region Segmentation In RGB Color-based Real-time Image (RGB 색상 기반의 실시간 영상에서 잡음에 강인한 손영역 분할)

  • Yang, Hyuk Jin;Kim, Dong Hyun;Seo, Yeong Geon
    • Journal of Digital Contents Society
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    • 제18권8호
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    • pp.1603-1613
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    • 2017
  • This paper proposes a method for effectively segmenting the hand region using a widely popular RGB color-based webcam. This performs the empirical preprocessing method four times to remove the noise. First, we use Gaussian smoothing to remove the overall image noise. Next, the RGB image is converted into the HSV and the YCbCr color model, and global fixed binarization is performed based on the statistical value for each color model, and the noise is removed by the bitwise-OR operation. Then, RDP and flood fill algorithms are used to perform contour approximation and inner area fill operations to remove noise. Finally, ROI (hand region) is selected by eliminating noise through morphological operation and determining a threshold value proportional to the image size. This study focuses on the noise reduction and can be used as a base technology of gesture recognition application.

Lane Detection Algorithm using Morphology and Color Information (형태학과 색상 정보를 이용한 차선 인식 알고리즘)

  • Bae, Chan-Su;Lee, Jong-Hwa;Cho, Sang-Bock
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • 제48권6호
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    • pp.15-24
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    • 2011
  • As increase awareness of intelligent vehicle systems, many kinds of lane detection algorithm have been proposed. General boundary extraction method can bring good result in detection of lane on the road. But a shadow on the road, or other boundaries, such as horizontal lines can be detected. The method using morphological operations was used to extract information about Lane. By applying HSV color model for color information of lane, the candidate of the lane can be extracted. In this paper, the lane detection region was set by Hough transformation using the candidate of the lane. By extracting lane markings on the lane detection region, lane detection method can bring good result.

A Method to Recover 2D barcodes Contaminated with Dust (2D 바코드의 분진 오염 극복 방법)

  • Ha, Eunjae;Lee, Jaesung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • 제23권3호
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    • pp.276-281
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    • 2019
  • Food printers must use food ink cartridges approved by the Ministry of Food and Drug Safety (MFDS). A 2D bar code is used to read whether the ink cartridge is authentic. However, since the dye is diverged by heat pressure and printed, the barcode is contaminated. In this paper, we propose a pre-processing algorithm to solve the problem of barcode contamination by food coloring dust in a latte art printer. The algorithm is based on various morphological operations. We apply this algorithm before reading contaminated barcode images with a general QR code reader. It has been confirmed that, as compared with the existing QR code reader, the contamination rate that can be perceived is increased from 25% to 40% and even at a contamination rate of 45%, the recognition rate reaches 50%.

Structural Segmentation for 3-D Brain Image by Intensity Coherence Enhancement and Classification (명암도 응집성 강화 및 분류를 통한 3차원 뇌 영상 구조적 분할)

  • Kim, Min-Jeong;Lee, Joung-Min;Kim, Myoung-Hee
    • The KIPS Transactions:PartA
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    • 제13A권5호
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    • pp.465-472
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    • 2006
  • Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

Salient Object Extraction from Video Sequences using Contrast Map and Motion Information (대비 지도와 움직임 정보를 이용한 동영상으로부터 중요 객체 추출)

  • Kwak, Soo-Yeong;Ko, Byoung-Chul;Byun, Hye-Ran
    • Journal of KIISE:Software and Applications
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    • 제32권11호
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    • pp.1121-1135
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    • 2005
  • This paper proposes a moving object extraction method using the contrast map and salient points. In order to make the contrast map, we generate three-feature maps such as luminance map, color map and directional map and extract salient points from an image. By using these features, we can decide the Attention Window(AW) location easily The purpose of the AW is to remove the useless regions in the image such as background as well as to reduce the amount of image processing. To create the exact location and flexible size of the AW, we use motion feature instead of pre-assumptions or heuristic parameters. After determining of the AW, we find the difference of edge to inner area from the AW. Then, we can extract horizontal candidate region and vortical candidate region. After finding both horizontal and vertical candidates, intersection regions through logical AND operation are further processed by morphological operations. The proposed algorithm has been applied to many video sequences which have static background like surveillance type of video sequences. The moving object was quite well segmented with accurate boundaries.

Implementation of a walking-aid light with machine vision-based pedestrian signal detection (머신비전 기반 보행신호등 검출 기능을 갖는 보행등 구현)

  • Jihun Koo;Juseong Lee;Hongrae Cho;Ho-Myoung An
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • 제17권1호
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    • pp.31-37
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    • 2024
  • In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

Identifying Analog Gauge Needle Objects Based on Image Processing for a Remote Survey of Maritime Autonomous Surface Ships (자율운항선박의 원격검사를 위한 영상처리 기반의 아날로그 게이지 지시바늘 객체의 식별)

  • Hyun-Woo Lee;Jeong-Bin Yim
    • Journal of Navigation and Port Research
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    • 제47권6호
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    • pp.410-418
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    • 2023
  • Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.

Automatic Text Extraction from News Video using Morphology and Text Shape (형태학과 문자의 모양을 이용한 뉴스 비디오에서의 자동 문자 추출)

  • Jang, In-Young;Ko, Byoung-Chul;Kim, Kil-Cheon;Byun, Hye-Ran
    • Journal of KIISE:Computing Practices and Letters
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    • 제8권4호
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    • pp.479-488
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    • 2002
  • In recent years the amount of digital video used has risen dramatically to keep pace with the increasing use of the Internet and consequently an automated method is needed for indexing digital video databases. Textual information, both superimposed and embedded scene texts, appearing in a digital video can be a crucial clue for helping the video indexing. In this paper, a new method is presented to extract both superimposed and embedded scene texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, a color image is converted into a gray-level image and applies contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose+CloseOpen)/2 morphological operations, maintaining text components using (OpenClose+CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, my method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.