30 research outputs found
Classification of sea ice types for the East part of Greenland waters using SENTINEL 1 data
Ships navigate in Greenland waters all year round. Cruises to Greenland due to tourism
and educational purposes have increased the last decade. Hence, it is essential for ships
that navigate through Sea Ice in winter to use reliable and accurate information on sea ice
conditions. An accurate classification of Sea Ice types is an ongoing problem. Many
classification algorithms depend only on the SAR image intensity for discriminating the
sea ice types. Different Sea Ice types exhibit similar backscatter signature which makes
the algorithm unable to correctly classify them.
In this study, two dual-polarization SENTINEL-1 images with a spatial resolution of 40 x
40m acquired over the East part of Greenland in February and May of 2016. Support
Vector Machine (SVM) classifier was used to perform the classification. In order to
improve the discrimination of ice types, texture analysis was performed using Grey Level
Co-occurrence Matrix (GLCM) algorithm. Ten GLCM texture features were calculated.
The analysis revealed that the most informative texture features for the sea ice
classification are Energy, mean, dissimilarity for HV polarization and Angular Second
Moment, variance and energy for HH polarization.
The classification results for the SAR images acquired during winter and spring period
were compared against the sea ice charts produced by DMI. A good agreement between
the classification results and validation data is shown. The results show that the overall
classification accuracy for both SAR images amount to 91%. The most hazardous for ships
navigation sea ice types (old ice and deformed first year ice) have been successfully
discriminated
Analogy-Forming Transformers for Few-Shot 3D Parsing
We present Analogical Networks, a model that encodes domain knowledge
explicitly, in a collection of structured labelled 3D scenes, in addition to
implicitly, as model parameters, and segments 3D object scenes with analogical
reasoning: instead of mapping a scene to part segments directly, our model
first retrieves related scenes from memory and their corresponding part
structures, and then predicts analogous part structures for the input scene,
via an end-to-end learnable modulation mechanism. By conditioning on more than
one retrieved memories, compositions of structures are predicted, that mix and
match parts across the retrieved memories. One-shot, few-shot or many-shot
learning are treated uniformly in Analogical Networks, by conditioning on the
appropriate set of memories, whether taken from a single, few or many memory
exemplars, and inferring analogous parses. We show Analogical Networks are
competitive with state-of-the-art 3D segmentation transformers in many-shot
settings, and outperform them, as well as existing paradigms of meta-learning
and few-shot learning, in few-shot settings. Analogical Networks successfully
segment instances of novel object categories simply by expanding their memory,
without any weight updates. Our code and models are publicly available in the
project webpage: http://analogicalnets.github.io/.Comment: ICLR 202
Methods for measuring fluoroscopic skin dose
This paper briefly reviews available technologies for measuring or estimating patient skin dose in the interventional fluoroscopic environment
Patient dose reduction during voiding cystourethrography
Voiding cystourethrography (VCUG) is a commonly performed examination in a pediatric uroradiology practice. This article contains suggestions on how the radiation dose to a child from VCUG can be made ‘as low as reasonably achievable–(ALARA). The pediatric radiologist should consider the appropriateness of the clinical indication before performing VCUG and utilize radiation exposure techniques and parameters during VCUG to reduce radiation exposure to a child. The medical physicist and fluoroscope manufacturer can also work together to optimize a pulsed-fluoroscopy unit and further reduce the radiation exposure. Laboratory and clinical research is necessary to investigate methods that reduce radiation exposures during VCUG, and current research is presented here