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Found 25 papers, 10 papers with code
Accelerating Inference of Networks in the Frequency Domain
In particular, we propose a frequency inference chain that is dual to the network inference in the spatial domain.
Deep Clustering via Distribution Learning
Thus, in this work, we provide a theoretical analysis to guide the optimization of clustering via distribution learning.
RAST: Restorable Arbitrary Style Transfer
Embedding an artistic style can result in unintended changes to the image content.
RAST: Restorable arbitrary style transfer via multi-restoration
We control the content-style balance in stylized images by the accuracy of image restoration.
Medical Image Denosing via Explainable AI Feature Preserving Loss
Denoising algorithms play a crucial role in medical image processing and analysis.
Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks
Since high-frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.
Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module
On this foundation, we present a novel Differentiable Arithmetic Distribution Module (DADM), which is designed to extract the intrinsic probability distributions from images.
Bridging Distribution Learning and Image Clustering in High-dimensional Space
Based on the experimental results, we believe distribution learning can exploit the potential of GMM in image clustering within high-dimensional space.
Is Deep Learning Network Necessary for Image Generation?
In this paper, we investigate the possibility of image generation without using a deep learning network, motivated by validating the assumption that images follow a high-dimensional distribution.
Learning Distributions via Monte-Carlo Marginalization
One strong evidence of the benefit of our method is that the distributions learned by the proposed approach can generate better images even based on a pre-trained VAE's decoder.
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
In this paper, we propose a method called Learning Temporal Distribution and Spatial Correlation (LTS) that has the potential to be a general solution for universal moving object segmentation.
Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks
Since high frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.
SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space
We propose a method SPGNet for 3D human pose estimation that mixes multi-dimensional re-projection into supervised learning.
Ranked #58 on
3D Human Pose Estimation
on Human3.6M
Universal Background Subtraction based on Arithmetic Distribution Neural Network
Thus, the proposed approach is able to utilize the probability information of the histogram and achieve promising results with a very simple architecture compared to traditional convolutional neural networks.
Subjective and Objective Visual Quality Assessment of Textured 3D Meshes
Based on analysis of the results, we propose two new metrics for visual quality assessment of textured mesh, as optimized linear combinations of accurate geometry and texture quality measurements.
A note on 'A fully parallel 3D thinning algorithm and its applications'
A 3D thinning algorithm erodes a 3D binary image layer by layer to extract the skeletons.
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow
This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm.
A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants
We detect the ventricles as blobs using a fast linear Maximally Stable Extremal Regions algorithm.
Simplified Active Calibration
The estimated focal lengths are then treated as known parameters to obtain a linear set of equations to calculate the principal point.
Towards the identification of Parkinson's Disease using only T1 MR Images
Our proposed method has three main steps : 1) Preprocessing, 2) Fea- ture Extraction, and 3) Classification.
IVUS-Net: An Intravascular Ultrasound Segmentation Network
IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases.
A Simplified Active Calibration algorithm for Focal Length Estimation
By establishing a correspondence between only two images taken after slightly panning and tilting the camera and a reference image, our proposed Simplified Calibration Method is able to calculate the focal length of the camera.
Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selection
Secondly, we propose a region selection strategy to label two ERELs as lumen and media based on the stability of their textural information.
Highlighting objects of interest in an image by integrating saliency and depth
However, the depth information acquired from stereo can also be used along with saliency to highlight certain objects in a scene.
Entropy-difference based stereo error detection
As a remedy, we propose a novel error detection approach based solely on the input image and its depth map.
