Placing objects in context via inpainting for out-of-distribution segmentation by De Jorge, P, Volpi, R, Dokania, PK, Torr, PHS, Rogez, G

“…When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. …”

Using multiple segmentations to discover objects and their extent in image collections by Russell, BC, Efros, AA, Sivic, J, Freeman, WT, Zisserman, A

“…Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. …”

Segmentation of magnetic resonance images for assessing neonatal brain maturation by Wang, S

“…We also investigate atlas-based whole brain segmentation that generates the binary mask for the region of interest. …”

DiffuSeg: domain-driven diffusion for medical image segmentation by Zhang, L, Wu, F, Bronik, K, Papiez, BW

“…In recent years, the deployment of supervised machine learning techniques for segmentation tasks has significantly increased. Nonetheless, the annotation process for extensive datasets remains costly, labor-intensive, and error-prone. …”

A new hybrid algorithm for retinal vessels segmentation on fundus images by Dharmawan, Dhimas Arief, Li, Di, Ng, Boon Poh, Rahardja, Susanto

“…Automatic retinal vessels segmentation is an important task in medical applications. …”
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Deep learning approach to cervical segmentation from routine CT images by Duan, Yiming

“…Recently, techniques for 3D medical image segmentation have become increas ingly sophisticated. …”
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Audio segmentation for speech/non-speech detection using wavelet transform by Swe Zin Kalayar Khine.

“…In this project, the noisy audio signal is segmented into speech signal and non-speech signal (that is noise or silence). …”
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Multi-scale space smoothing and segmentation of range data for robot navigation by Tang, Fan

“…This algorithm smoothes range data and segments it at the same time by translating a model based mask over the data. …”
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Novel level set based statistical frameworks for segmentation of MR images by Mahshid Farzinfar

“…In summary, two automated frameworks for segmentation of medical images are proposed. They are the joint curve evolution and the EM-joint shape based algorithms. …”
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Medical image segmentation using random forest and convolutional neural networks by Duan, You

“…This dissertation found that Convolutional Neural Network (CNN) works well in the field of image segmentation. Random Forest (RF) can also obtain good results when processing image segmentation, but it will be affected by the image noise.…”
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Texture aware image segmentation using graph cuts and active contours by Zhou, Hailing, Zheng, Jianmin, Wei, Lei

“…The problem of segmenting a foreground object out from its complex background is of great interest in image processing and computer vision. …”
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Advancements in glioma segmentation: comparing the U-Net and DeconvNet models by Mohammed, AA, Gonzales, RA

“…In this study, we address the challenge of accurately segmenting gliomas from magnetic resonance imaging (MRI) scans, an essential task for treatment planning and prognosis in glioma patients. …”

Anatomically plausible segmentations: explicitly preserving topology through prior deformations by Wyburd, MK, Dinsdale, NK, Jenkinson, M, Namburete, AIL

“…Since the rise of deep learning, new medical segmentation methods have rapidly been proposed with extremely promising results, often reporting marginal improvements on the previous state-of-the-art (SOTA) method. …”

Measurement of brain structures based on statistical and geometrical 3D segmentation by ángel, M, Ballester, G, Zisserman, A, Brady, M

“…In this paper we present a novel method for three-dimensional segmentation and measurement of volumetric data based on the combination of statistical and geometrical information. …”

Highly polarized light emission by isotropic quantum dots integrated with magnetically aligned segmented nanowires by Jun, Shinae, Erdem, Talha, Guzelturk, Burak, Perkgöz, Nihan Kosku, Uran, Can, Jang, Eunjoo, Demir, Hilmi Volkan

“…In this work, we demonstrate a proof-of-concept system for generating highly polarized light from colloidal quantum dots (QDs) coupled with magnetically aligned segmented Au/Ni/Au nanowires (NWs). Optical characterizations reveal that the optimized QD-NW coupled structures emit highly polarized light with an s-to p-polarization (s/p) contrast as high as 15:1 corresponding to a degree of polarization of 0.88. …”
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Interfacial fracture behavior of double-ceramic-layer thermal barrier coating system with segmented structure by Li, Biao, Fan, Xueling, Wang, Tiejun, Zhou, Kun

“…Segmented double-ceramic-layer thermal barrier coating system (DCL-TBCs) is promising for application in next-generation turbines. …”
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Dual attention deep learning network for automatic steel surface defect segmentation by Pan, Yue, Zhang, Limao

“…Experimental results show that, compared with other deep learning models, DAN-DeepLabv3+ based on the Xception backbone exhibits the best segmentation performance under the mean intersection over union (IoU) of 89.95% and the frequency-weighted IoU of 97.34%. …”
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CRCNet: few-shot segmentation with cross-reference and region–global conditional networks by Liu, Weide, Zhang, Chi, Lin, Guosheng, Liu, Fayao

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Hybrid UNet transformer architecture for ischemic stoke segmentation with MRI and CT datasets by Soh, Wei Kwek, Rajapakse, Jagath Chandana

“…A hybrid UNet and Transformer (HUT) network is introduced to combine the merits of the UNet and Transformer architectures, improving brain lesion segmentation from MRI and CT scans. The HUT overcomes the limitations of conventional approaches by utilizing two parallel stages: one based on UNet and the other on Transformers. …”
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Automated detection and segmentation of vertebral bodies in volumetric abdominal magnetic resonance images by Lin, Dehui.

“…Detection and segmentation of the column of vertebral bodies are intermediate steps required to identify the bone marrow which is considered as the wrongly labeled visceral adipose tissue in the assessment of abdominal obesity. …”
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