Results 71 to 80 of about 986,520 (234)
Explainable Deep Learning for Imaging‐Based Skin Lesion Diagnosis: A Systematic Literature Review
Concurrency and Computation: Practice and Experience, Volume 38, Issue 16, August 2026.ABSTRACT In the latest years, the use of Deep Learning (DL) in imaging‐based skin lesion diagnosis has become increasingly prevalent. The deep models have revolutionized the computer‐aided diagnosis systems in terms of performance. However, DL models are often criticized as black boxes due to their complex and opaque internal design of numerous ...
Rym Dakhli, Walid Barhoumi
wiley +1 more source
Mixture-Based Superpixel Segmentation and Classification of SAR Images
, 2016 We propose a mixture-based superpixel segmentation method for synthetic aperture radar (SAR) images. The method uses SAR image amplitudes and pixel coordinates as features.
Kayabol, Koray, Arisoy, Sertac
core +1 more source
Automatic Image Segmentation With Superpixels and Image-Level Labels
IEEE Access, 2019 Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing.
Xinlin Xie +4 more
doaj +1 more source
Advanced Intelligent Systems, Volume 8, Issue 7, July 2026.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Superpixel Transformers for Efficient Semantic Segmentation
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 Semantic segmentation, which aims to classify every pixel in an image, is a key task in machine perception, with many applications across robotics and autonomous driving. Due to the high dimensionality of this task, most existing approaches use local operations, such as convolutions, to generate per-pixel features.
Alex Zihao Zhu +6 more
openaire +2 more sources
Video‐Based Rainfall Opportunistic Sensing in Hydrology: A Lightweight Machine Learning Approach
Water Resources Research, Volume 62, Issue 7, July 2026.Abstract Video‐based rainfall measurement is a frontier topic in opportunistic sensing; however, rapid, accurate, and robust identification of rainfall‐related rain‐streak features from dynamic videos remains a key challenge, especially for monitoring devices with limited computational resources.
Yongcheng Jin +5 more
wiley +1 more source
Content-Sensitive Superpixel Generation with Boundary Adjustment
Applied Sciences, 2020 Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was
Dong Zhang +5 more
doaj +1 more source
SUPERPIXEL SEGMENTATION FOR POLSAR IMAGES WITH LOCAL ITERATIVE CLUSTERING AND HETEROGENEOUS STATISTICAL MODEL [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Superpixel segmentation has an advantage that can well preserve the target shape and details. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel segmentation method is proposed.
D. Xiang +5 more
doaj +1 more source
A deep learning‐enabled toolkit for the 3D segmentation of ventricular cardiomyocytes
The Journal of Physiology, Volume 604, Issue 13, Page 5561-5584, 1 July 2026.Abstract figure legend 3D cardiomyocyte segmentation enables comprehensive analyses of myocardial microstructure in health and disease; however, it is technically demanding. We present an open‐source toolkit for this task, which reduces challenges associated with sample preparation, image restoration, segmentation and proofreading.
Joachim Greiner +6 more
wiley +1 more source
Superpixel Segmentation Systems: Design and Analysis
, 2019 Image segmentation is a partitioning of an image into distinct groups of pixels (“regions”), each region preferably spatially connected, where each region ideally corresponds to an object or other semantically meaningful scene component.
Malladi, Sree Ramya Surya Prabha
core +4 more sources