AIMC Topic: Image Processing, Computer-Assisted

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Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction.

IEEE transactions on pattern analysis and machine intelligence
A lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of deep convolutional neural networks (DCNNs). In the recent works, the texture features either correspond to components of a l...

Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis.

IEEE transactions on pattern analysis and machine intelligence
With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable structured inputs. This paper focuses on a recently emerged task, layou...

In-Field Automatic Identification of Pomegranates Using a Farmer Robot.

Sensors (Basel, Switzerland)
Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper pr...

Convolutional Ordinal Regression Forest for Image Ordinal Estimation.

IEEE transactions on neural networks and learning systems
Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression (OR) problem. Recent methods formulate an OR problem as a series of binary classification problems. Such methods cannot ensur...

Predictions on multi-class terminal ballistics datasets using conditional Generative Adversarial Networks.

Neural networks : the official journal of the International Neural Network Society
Ballistic impacts are a primary risk in both civil and military defence applications, where successfully predicting the dynamic response of a material or structure to impact crucial to the design of safe and fit-for-purpose protective structures. Thi...

Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework.

PloS one
The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. I...

Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.

Annals of nuclear medicine
OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over an hour after injection to obtain sufficient image quality can be challenging for patients with susp...

Deep Learning-Based Photoacoustic Imaging of Vascular Network Through Thick Porous Media.

IEEE transactions on medical imaging
Photoacoustic imaging is a promising approach used to realize in vivo transcranial cerebral vascular imaging. However, the strong attenuation and distortion of the photoacoustic wave caused by the thick porous skull greatly affect the imaging quality...

3D Segmentation Guided Style-Based Generative Adversarial Networks for PET Synthesis.

IEEE transactions on medical imaging
Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-...

Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks.

IEEE transactions on medical imaging
Encoding-decoding (ED) CNNs have demonstrated state-of-the-art performance for noise reduction over the past years. This has triggered the pursuit of better understanding the inner workings of such architectures, which has led to the theory of deep c...