Search Results - (( based segmentation using algorithm ) OR ( framework segmentation using algorithm ))
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1
Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…In the preprocessing stage, the On-Line Noise Filtering Algorithm for Trajectory Segmentation Based on Minimum Description Length concept (ONF_TRS) is proposed to achieve trajectory segmentation and remove noise points in real time with low computational cost. …”
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2
Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image
Published 2024“…The adaptability of the algorithm is demonstrated through a 3D surface reconstruction using a final disparity map. …”
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3
A feature-based approach for segmenting faces
Published 2004“…In this paper, we propose a feature-based algorithm for segmenting faces that is sufficiently generic and is also easily extensible to cope with more demanding variations of the imaging conditions. …”
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4
Stereo matching algorithm using census transform and segment tree for depth estimation
Published 2023“…The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. …”
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5
A framework for white blood cell segmentation in microscopic blood images using digital image processing
Published 2009“…The segmentation is conducted using a proposed segmentation framework that consists of an integration of several digital image processing algorithms. …”
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6
Embedded car plate image recognition system
Published 2008“…Captured license plate is segmented into individual character by using histogram-based approach. …”
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Learning Object -
7
Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring
Published 2016“…The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. …”
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8
Image segmentation method for boundary detection of breast thermography using random walkers
Published 2013“…The performance of the proposed method was evaluated by a board of three professional radiologists and the final decision was based on the majority agreement. The segmentation was based on constant parameters among all images used in the study; these standard segmentation parameters achieved acceptable results in most cases. …”
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9
2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images
Published 2025“…This study introduces a novel method based on Two-tier Semantic Segmentation (2TSS) framework explicitly aimed at enhancing hotspot detection in thermal images of PV modules. …”
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10
Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images
Published 2016“…Methods: The developed framework comprises of five computational steps to segment the ATFL ligament region. …”
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11
Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images
Published 2016“…Methods: The developed framework comprises of five computational steps to segment the ATFL ligament region. …”
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12
Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The segmentation iris is transformed to rectangular shape using the Rubber Sheet method. …”
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13
A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation
Published 2021“…The proposed method has been validated using four commonly used video entity segmentation datasets: SegTrack V2, DAVIS 2016, CdNet 2014, and the Youtube-Object dataset. …”
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14
A detailed description on unsupervised heterogeneous anomaly based intrusion detection framework
Published 2019“…To accomplish this, a current segment (clustering) has been used and a few new parts (filtering, aggregating and feature analysis) have been presented. …”
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15
Development of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learning
Published 2023“…The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. …”
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16
Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
Published 2022“…A fuzzy-based Naive Bayes algorithm has been used in this case. …”
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17
Deep learning semantic segmentation for water level estimation using surveillance camera
Published 2021“…This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. …”
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18
Deep learning-based item classification for retail automation
Published 2025“…Real-time processing was achieved through the integration of object detection algorithms like YOLO and image segmentation techniques. …”
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Final Year Project / Dissertation / Thesis -
19
Fast shot boundary detection based on separable moments and support vector machine
Published 2021“…Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. …”
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20
Optimising acoustic features for source mobile device identification using spectral analysis techniques / Mehdi Jahanirad
Published 2016“…Both models optimize acoustic features for source mobile device identification based on near-silent segments. The proposed feature sets along with selected feature extraction methods from the literature are analyzed and compared by using supervised learning techniques (i.e. support vector machines, nearest-neighbor, naïve Bayesian, neural network, logistic regression, and ensemble trees classifier), as well as unsupervised learning techniques (i.e. probabilistic-based and nearest-neighbor-based algorithms). …”
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