Results 71 to 80 of about 4,342 (209)

Model‐Agnostic Influential Outlier Metric

Stat, Volume 15, Issue 3, September 2026.
ABSTRACT The influential outlier metric (IOM) provides model‐agnostic influential outlier detection. We define influence of an observation using a combination of SHapley Additive exPlanation (SHAP) values and the residual. Both are transformed using normalizing flows, changing their respective measures to Gaussian distributions.
Colin C. Jones, David A. Campbell
wiley   +1 more source

Superpixels Generation of RGB-D Images Based on Geodesic Distance [PDF]

, 2015
A novel algorithm for generating superpixels of RGB-D images is presented in this paper. A regular triangular mesh is constructed by the depth and a local geometric features sensitive initialization method is proposed for initializing seeds by a density ...
Pan, Xiao, Zhou, Yuanfeng, Liu, Shuwei, Zhang, Caiming   +3 more
core   +1 more source

Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images

IEEE Access, 2020
Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is of great importance for better tumor diagnosis, growth rate prediction and radiotherapy planning. But this task is extremely challenging due to intrinsically heterogeneous tumor appearance,
Tamjid Imtiaz, Shahriar Rifat, Shaikh Anowarul Fattah, Khan A. Wahid   +3 more
doaj   +1 more source

The ORCA‐TWIN qCMOS Experiment I. Science Case and Commissioning at Calar Alto Observatory

Astronomische Nachrichten, Volume 347, Issue 7, August 2026.
ABSTRACT We describe a pilot study to explore a new generation of fast and low noise CMOS image sensors for time domain astronomy, using two remote telescopes with a baseline of 1635 km. The experiment involves direct imaging with novel qCMOS image sensor technology that combines fast readout with sub‐electron readout noise.
Martin M. Roth, Paško Roje, Stella Vješnica, Stefan Cikota, Alex J. Brown, Mike Kretlow, Marco Azzaro, Santiago Reinhart, Jesús Aceituno, Harry Dawson, Carsten Denker, Torsten Enßlin, Stephan Geier, Thomas Granzer, Thomas Kupfer, Gianluca Li Causi, Samaya Nissanke, José Luis Ortiz, Fernando Pedichini, Katja Poppenhäger, Axel Schwope   +20 more
wiley   +1 more source

SEEDS: superpixels extracted via energy-driven sampling

, 2012
Superpixel algorithms aim to over-segment the image by grouping pixels that belong to the same object. Many state-of-the-art superpixel algorithms rely on minimizing objective functions to enforce color homogeneity.
Van den Bergh, Michael, Boix, Xavier, Michael Van den Bergh, Benjamin de Capitani, Van Gool, Luc, Luc Van Gool, Xavier Boix, Roig, Gemma, Gemma Roig   +8 more
core   +1 more source

Superpixels Optimized by Color and Shape [PDF]

, 2018
Image over-segmentation is formalized as the approximation problem when a large image is segmented into a small number of connected superpixels with best fitting colors. The approximation quality is measured by the energy whose main term is the sum of squared color deviations over all pixels and a regularizer encourages round shapes.
Vitaliy Kurlin, Donald Harvey
openaire   +2 more sources

An Open Source Automated Tumor Infiltrating Lymphocytes Algorithm for Prognosis in Primary Small Bowel Adenocarcinoma Using Routine Hematoxylin and Eosin Stained Sections

Cancer Medicine, Volume 15, Issue 8, August 2026.
ABSTRACT Objective While tumor‐infiltrating lymphocytes (TILs) are prognostic in various cancers, their role in small bowel adenocarcinoma (SBA) is unexplored. This study evaluates the prognostic significance of digitally quantified TILs in SBA. Methods Using digital pathology (QuPath) on hematoxylin and eosin (H&E)‐stained slides from 62 SBA cases, we
Minying Deng, Xin Liu, Yuanlong Luo, Ayizimugu Abuduwaili, Qi Song, Rongkui Luo, Wen Huang, Lei Xu, Xiaolei Zhang, Lin Sun, Jieakesu Su, Chen Xu, Yingyong Hou   +12 more
wiley   +1 more source

Superpixel Estimation for Hyperspectral Imagery [PDF]

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014
In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and ...
Pegah Massoudifar, Anand Rangarajan 0001, Paul D. Gader   +2 more
openaire   +2 more sources

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

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

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, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source