Grayscale-inversion and rotation invariant image description with sorted LBP features

Han, Yuanjing; Song, Tianyou; Feng, Jie; Xie, Yurui

doi:10.1016/j.image.2021.116491

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…The experiment started by converting the original image into a grayscale image using the thresholding process, as shown in Fig. 2 (a) [36]. The Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE) values for 3 images were generated from the results of the computational process [37].…”

Section: Resultsmentioning

confidence: 99%

Internet of Things for Underwater Shrimp Image Detection Using Blob Detector

Setiawan¹,

Hadiyanto²,

Widodo³

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Measuring biomass content is an important stage in harvesting shrimp as it will determine the harvest time. Manual detection has caused shrimp stress and eventually caused shrimp death; therefore, a new shrimp biomass determination is required. This research aims to design an IoT technique-based biomass measurement, using underwater shrimp video with fog and cloud computing processes to easily detect shrimp underwater, irrespective of the complex noise. The method consists of several steps: image processing using grayscale, thresholding, contour edge detection, labeling, and blob detection. The results revealed that the highest SSIM value in the thresholding process was 0.18, while the lowest MSE was 91.35. In addition, in the contour edge detection process, the highest PSNR value was 3.6, and the lowest MSE was 2.06. The blob detection process produces a maximum key performance of 566, 411, and 387 in the Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), and Determinants of Hessian (DoH) methods, respectively. The Quality of Service (QoS) obtained throughput, loss, and delay values of 832.25, 0%, and 7.25 ms, respectively, in the data acquisition and computation processes, with the three parameters at a very good level. In conclusion, the IoT model is very suitable for underwater shrimp detection because it is a non-invasive method, contains high key performance blob detection, and has a very good QoS level and high-speed computation process.

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Section: Resultsmentioning

confidence: 99%

Internet of Things for Underwater Shrimp Image Detection Using Blob Detector

Setiawan¹,

Hadiyanto²,

Widodo³

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The generation of rotation-invariant LBP variations is straightforward (Han et al 2021;Zhao et al 2011). The proposed model includes logarithmic functionalization of the conventional LBP technique from Eqs.…”

Section: Feature Extraction Using Lt-lbpmentioning

confidence: 99%

LT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray Images

P.,

2024

Journal of Computer Science

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The novel logarithmic transformation of Local Binary Pattern (LT-LBP) for texture feature extraction is used with Deep Learning (DL) models. The Computational complexity in the DL models asymptotically increases with the data sesssst and the depth of the layers in Convolutional Neural Networks (CNN). Pre-processing the images before training the DL model holds a strong impact on the operation speed and throughput. The proposed work encompasses LT-LBP for spatial texture feature extraction and is combined with the DL models. LT-LBP reduces the computation while extracting the texture information from the medical images. The computational cost for the model has been reduced in this study. The experiments were done with various DL models such as inception V3, Dense Net, Mobile Net, Efficient Net (B0-B7) VGG16, and RESNET50, and the results were analyzed. The features were extracted using LT-LBP and given as input to the DL models for classification. The retrospective study was done with 118 COVID-19 X-ray images collected from Chengalpattu Medical College Hospital, Chennai, and the remaining 8,224 images were taken from Kaggle. The combination of LT-LBP with RESNET50 shows better performance when compared to other models with an accuracy of 87%. The Area Under the Curve (AUC) for the proposed model is 88, 83 and 86% for COVID, non-COVID, and pneumonia classifications.

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“…Digital images are sent electronically which are transferred to physical storage media in the form of photos and videos [34]. Grayscale image is an image with a gray level of pixels on the image object [35]. Grayscale image is the result of processing from RGB image, with the same red green blue value [36].…”

Section: Figure 1 Research Flowchartmentioning

confidence: 99%

Distance Estimation Between Camera and Shrimp Underwater Using Euclidian Distance and Triangles Similarity Algorithm

Setiawan¹,

Hadiyanto²,

Widodo³

2022

ISI

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Camera is the main tool for monitoring shrimp underwater with a noninvasive method. Distance of the shrimp underwater with the varying camera causes the monitoring of the estimated shrimp size to be less accurate. This study provides a new solution to detect the distance of shrimp underwater with a camera using the Euclidian distance and triangle similarity algorithm. The problem raised in this study is how to measure the distance of a shrimp underwater with a camera. The method used has several stages, including using the grayscale image method, image thresholding, edge detection, detection of Region of Interest (ROI), determining the position of shrimp coordinates, calculating the length of shrimp coordinates, camera calibration using the triangle similarity algorithm, calculating the estimated distance of shrimp with the camera. The study results obtained a focal length value of 1298.58, the value of the distance between the shrimp and the camera (D') for 5 positions of shrimp underwater from 50 cm – 19.86 cm, and the RE value from 0% - 0.13%. In conclusion, this method can be used to measure the estimated distance between of moving shrimp and a camera with a low error rate.

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Grayscale-inversion and rotation invariant image description with sorted LBP features

Cited by 10 publications

References 43 publications

Internet of Things for Underwater Shrimp Image Detection Using Blob Detector

Internet of Things for Underwater Shrimp Image Detection Using Blob Detector

LT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray Images

Distance Estimation Between Camera and Shrimp Underwater Using Euclidian Distance and Triangles Similarity Algorithm

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