• Title/Summary/Keyword: image segmentation technique

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Region-Growing Segmentation Algorithm for Rossless Image Compression to High-Resolution Medical Image (영역 성장 분할 기법을 이용한 무손실 영상 압축)

  • 박정선;김길중;전계록
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.1
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    • pp.33-40
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    • 2002
  • In this paper, we proposed a lossless compression algorithm of medical images which is essential technique in picture archive and communication system. Mammographic image and magnetic resonance image in among medical images used in this study, proposed a region growing segmentation algorithm for compression of these images. A proposed algorithm was partition by three sub region which error image, discontinuity index map, high order bit data from original image. And generated discontinuity index image data and error image which apply to a region growing algorithm are compressed using JBIG(Joint Bi-level Image experts Group) algorithm that is international hi-level image compression standard and proper image compression technique of gray code digital Images. The proposed lossless compression method resulted in, on the average, lossless compression to about 73.14% with a database of high-resolution digital mammography images. In comparison with direct coding by JBIG, JPEG, and Lempel-Ziv coding methods, the proposed method performed better by 3.7%, 7.9% and 23.6% on the database used.

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Segmented Video Coding Using Variable Block-Size Segmentation by Motion Vectors (움직임벡터에 의한 가변블럭영역화를 이용한 영역기반 동영상 부호화)

  • 이기헌;김준식;박래홍;이상욱;최종수
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.4
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    • pp.62-76
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    • 1994
  • In this paper, a segmentation-based coding technique as applied to video sequences is proposed. A proposed method separates an image into contour and texture parts, then the visually-sensitive contour part is represented by chain codes and the visually-insensitive texture part is reconstructed by a representative motion vector of a region and mean of the segmented frame difference. It uses a change detector to find moving areas and adopts variable blocks to represent different motions correctly. For better quality of reconstructed images, the displaced frame difference between the original image and the motion compensated image reconstructed by the representative motion vector is segmented. Computer simulation with several video sequences shows that the proposed method gives better performance than the conventional ones in terms of the peak signal to noise ratio(PSNR) and compression ration.

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A study on the flame recognition technique of an oxygen blown converter (전로 화염 인식에 관한 연구)

  • 류창우;채홍국;은종호
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.1473-1475
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    • 1996
  • In this paper, we propose the method to find the active region of flame which is produced within the gap between an oxygen blown converter and a skirt. For real-time image processing, basic region segmentation algorithms such as thresholding and XORing are used to segment the active region of flame. The result of this processing may be useful to clear the relationship between hood pressure and flame.

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Neighboring Elemental Image Exemplar Based Inpainting for Computational Integral Imaging Reconstruction with Partial Occlusion

  • Ko, Bumseok;Lee, Byung-Gook;Lee, Sukho
    • Journal of the Optical Society of Korea
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    • v.19 no.4
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    • pp.390-396
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    • 2015
  • We propose a partial occlusion removal method for computational integral imaging reconstruction (CIIR) based on the usage of the exemplar based inpainting technique. The proposed method is an improved version of the original linear inpainting based CIIR (LI-CIIR), which uses the inpainting technique to fill in the data missing region. The LI-CIIR shows good results for images which contain objects with smooth surfaces. However, if the object has a textured surface, the result of the LI-CIIR deteriorates, since the linear inpainting cannot recover the textured data in the data missing region well. In this work, we utilize the exemplar based inpainting to fill in the textured data in the data missing region. We call the proposed method the neighboring elemental image exemplar based inpainting (NEI-exemplar inpainting) method, since it uses sources from neighboring elemental images to fill in the data missing region. Furthermore, we also propose an automatic occluding region extraction method based on the use of the mutual constraint using depth estimation (MC-DE) and the level set based bimodal segmentation. Experimental results show the validity of the proposed system.

AI-based Automatic Spine CT Image Segmentation and Haptic Rendering for Spinal Needle Insertion Simulator (척추 바늘 삽입술 시뮬레이터 개발을 위한 인공지능 기반 척추 CT 이미지 자동분할 및 햅틱 렌더링)

  • Park, Ikjong;Kim, Keehoon;Choi, Gun;Chung, Wan Kyun
    • The Journal of Korea Robotics Society
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    • v.15 no.4
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    • pp.316-322
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    • 2020
  • Endoscopic spine surgery is an advanced surgical technique for spinal surgery since it minimizes skin incision, muscle damage, and blood loss compared to open surgery. It requires, however, accurate positioning of an endoscope to avoid spinal nerves and to locate the endoscope near the target disk. Before the insertion of the endoscope, a guide needle is inserted to guide it. Also, the result of the surgery highly depends on the surgeons' experience and the patients' CT or MRI images. Thus, for the training, a number of haptic simulators for spinal needle insertion have been developed. But, still, it is difficult to be used in the medical field practically because previous studies require manual segmentation of vertebrae from CT images, and interaction force between the needle and soft tissue has not been considered carefully. This paper proposes AI-based automatic vertebrae CT-image segmentation and haptic rendering method using the proposed need-tissue interaction model. For the segmentation, U-net structure was implemented and the accuracy was 93% in pixel and 88% in IoU. The needle-tissue interaction model including puncture force and friction force was implemented for haptic rendering in the proposed spinal needle insertion simulator.

A label-free high precision automated crack detection method based on unsupervised generative attentional networks and swin-crackformer

  • Shiqiao Meng;Lezhi Gu;Ying Zhou;Abouzar Jafari
    • Smart Structures and Systems
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    • v.33 no.6
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    • pp.449-463
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    • 2024
  • Automated crack detection is crucial for structural health monitoring and post-earthquake rapid damage detection. However, realizing high precision automatic crack detection in the absence of corresponding manual labeling presents a formidable challenge. This paper presents a novel crack segmentation transfer learning method and a novel crack segmentation model called Swin-CrackFormer. The proposed method facilitates efficient crack image style transfer through a meticulously designed data preprocessing technique, followed by the utilization of a GAN model for image style transfer. Moreover, the proposed Swin-CrackFormer combines the advantages of Transformer and convolution operations to achieve effective local and global feature extraction. To verify the effectiveness of the proposed method, this study validates the proposed method on three unlabeled crack datasets and evaluates the Swin-CrackFormer model on the METU dataset. Experimental results demonstrate that the crack transfer learning method significantly improves the crack segmentation performance on unlabeled crack datasets. Moreover, the Swin-CrackFormer model achieved the best detection result on the METU dataset, surpassing existing crack segmentation models.

Target segmentation in non-homogeneous infrared images using a PCA plane and an adaptive Gaussian kernel

  • Kim, Yong Min;Park, Ki Tae;Moon, Young Shik
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.6
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    • pp.2302-2316
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    • 2015
  • We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

Lab Color Space based Rice Yield Prediction using Low Altitude UAV Field Image

  • Reza, Md Nasim;Na, Inseop;Baek, Sunwook;Lee, In;Lee, Kyeonghwan
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 2017.04a
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    • pp.42-42
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    • 2017
  • Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

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Development of the forest type classification technique for the mixed forest with coniferous and broad-leaved species using the high resolution satellite data

  • Sasakawa, Hiroshi;Tsuyuki, Satoshi
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.467-469
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    • 2003
  • This research aimed to develop forest type classification technique for the mixed forest with coniferous and broad-leaved species using the high resolution satellite data. QuickBird data was used as satellite data. The method of this research was to extract satellite data for every single tree crown using image segmentation technique, then to evaluate the accuracy of classification by changing grouping criteria such as tree species, families, coniferous or broad-leaved species, and timber prices. As a result, the classification of tree species and families level was inaccurate, on the other hand, coniferous or broad-leaved species and timber price level was high accurate.

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Image-based Soft Drink Type Classification and Dietary Assessment System Using Deep Convolutional Neural Network with Transfer Learning

  • Rubaiya Hafiz;Mohammad Reduanul Haque;Aniruddha Rakshit;Amina khatun;Mohammad Shorif Uddin
    • International Journal of Computer Science & Network Security
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    • v.24 no.2
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    • pp.158-168
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    • 2024
  • There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.