Image Segmentation of Multi-shaped Overlapping Objects

Kumar, Abhinav; Chauhan, Jaideep Singh; Sarkar, Debasis

doi:10.5220/0006628404100418

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…In fact, no generalized approach exists that is effective for a wide range of image datasets. In many previous approaches that resolve the overlapping object problem in biomedical or industrial imagery [6]- [11], it must be assumed that the objects can be approximately fitted as an elliptical or circular shape. Therefore, these approaches will probably fail when the objects in the image have multiple shapes or their shapes are arbitrary.…”

Section: Introductionmentioning

confidence: 99%

Detection Algorithm for Overlapping LEDs in Vehicular Visible Light Communication System

2019

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In a vehicular visible light communication system, the visible light signals are obtained from a series of image frames showing LEDs as a transmitter which are captured by a high-speed camera as a receiver. However, when two LEDs overlap in the captured image, some serious problems related to data transmission arise, such as high data loss and bit-error-rate. To resolve these problems, in this paper, a method comprising three main steps for separating the overlapping LEDs is proposed. First, according to LED luminance, the edges of the LEDs are detected by applying an improved Canny edge detector algorithm. Then these edges are used to extract all the contours of the two overlapping LEDs. Finally, a derivative of the generalized Hough transform algorithm is utilized to distinguish each LED from the overlapping region according to the obtained LED contours. The performance of the proposed algorithm under different parametric conditions in real situations is analyzed according to the results of experiments conducted in an outdoor environment.

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Section: Introductionmentioning

confidence: 99%

Detection Algorithm for Overlapping LEDs in Vehicular Visible Light Communication System

2019

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Auto diagnostic system for detecting solitary and juxtapleural pulmonary nodules in computed tomography images using machine learning

Karrar

Mabrouk

Wahed

et al. 2022

Neural Comput & Applic

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Lung cancer is one of the most serious cancers in the world with the minimum survival rate after the diagnosis as it appears in Computed Tomography scans. Lung nodules may be isolated from (solitary) or attached to (juxtapleural) other structures such as blood vessels or the pleura. Diagnosis of lung nodules according to their location increases the survival rate as it achieves diagnostic and therapeutic quality assurance. In this paper, a Computer Aided Diagnosis (CADx) system is proposed to classify solitary nodules and juxtapleural nodules inside the lungs. Two main auto-diagnostic schemes of supervised learning for lung nodules classification are achieved. In the first scheme, (bounding box + Maximum intensity projection) and (Thresholding + K-means clustering) segmentation approaches are proposed then first- and second-order features are extracted. Fisher score ranking is also used in the first scheme as a feature selection method. The higher five, ten, and fifteen ranks of the feature set are selected. In the first scheme, Support Vector Machine (SVM) classifier is used. In the second scheme, the same segmentation approaches are used with Deep Convolutional neural networks (DCNN) which is a successful tool for deep learning classification. Because of the limited data sample and imbalanced data, tenfold cross-validation and random oversampling are used for the two schemes. For diagnosis of the solitary nodule, the first scheme with SVM achieved the highest accuracy and sensitivity 91.4% and 89.3%, respectively, with radial basis function and applying the (Thresholding + Kmeans clustering) segmentation approach and the higher 15 ranks of the feature set. In the second scheme, DCNN achieved the highest accuracy and sensitivity 96% and 95%, respectively, to detect the solitary nodule when applying the bounding box and maximum intensity projection segmentation approach. Receiver operating characteristic curve is used to evaluate the classifier’s performance. The max. AUC = 90.3% is achieved with DCNN classifier for detecting solitary nodules. This CAD system acts as a second opinion for the radiologist to help in the early diagnosis of lung cancer. The accuracy, sensitivity, and specificity of scheme I (SVM) and scheme II (DCNN) showed promising results in comparison to other published studies.

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Non-destructive robotic sorting of cracked pistachio using deep learning

Karadağ

Kılıç

2023

Postharvest Biology and Technology

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Image Segmentation of Multi-shaped Overlapping Objects

Cited by 6 publications

References 15 publications

Detection Algorithm for Overlapping LEDs in Vehicular Visible Light Communication System

Detection Algorithm for Overlapping LEDs in Vehicular Visible Light Communication System

Auto diagnostic system for detecting solitary and juxtapleural pulmonary nodules in computed tomography images using machine learning

Non-destructive robotic sorting of cracked pistachio using deep learning

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