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Found 21 papers, 6 papers with code
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
Although this approach utilizes information from all the phases and outperform single-phase segmentation networks, we demonstrate that their performance is not optimal and can be further improved by incorporating the learning from models trained on each single-phase individually.
Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging
Optical Coherence Tomography (OCT) is one of the most important retinal imaging technique.
A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection
Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient.
In Defense of Scene Graphs for Image Captioning
Several studies have noted that the naive use of scene graphs from a black-box scene graph generator harms image captioning performance and that scene graph-based captioning models have to incur the overhead of explicit use of image features to generate decent captions.
Toward Joint Image Generation and Compression using Generative Adversarial Networks
The proposed method is a promising baseline method for joint image generation and compression using generative adversarial networks.
Random Forest with Learned Representations for Semantic Segmentation
In this work, we present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation.
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction.
Ranked #7 on
JPEG Artifact Correction
on LIVE1 (Quality 20 Color)
Correction by Projection: Denoising Images with Generative Adversarial Networks
In this paper, we propose to denoise corrupted images by finding the nearest point on the GAN manifold, recovering latent vectors by minimizing distances in image space.
Image denoising with generalized Gaussian mixture model patch priors
In this paper, we show that a generalized Gaussian mixture model (GGMM) captures the underlying distribution of patches better than a GMM.
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\times$ speed-up
Image restoration methods aim to recover the underlying clean image from corrupted observations.
Depth Adaptive Deep Neural Network for Semantic Segmentation
To overcome this challenge, we develop a neural network which is able to adapt the receptive field not only for each layer but also for each neuron at the spatial location.
Relevance Subject Machine: A Novel Person Re-identification Framework
We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem.
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers.
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules.
Hand Segmentation for Hand-Object Interaction from Depth map
Thus, we propose hand segmentation method for hand-object interaction using only a depth map.
Adaptive Image Denoising by Mixture Adaptation
We propose an adaptive learning procedure to learn patch-based image priors for image denoising.
Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map
In our system, we track hand articulations by minimizing discrepancy between depth map from sensor and computer-generated hand model.
Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map
We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects.
Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution
This letter presents a novel approach to extract reliable dense and long-range motion trajectories of articulated human in a video sequence.
Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling
Experimental results show that the proposed method produces high quality dense depth estimates, and is robust to noisy measurements.
Adaptive Image Denoising by Targeted Databases
First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem.
