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.
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.
The proposed method is a promising baseline method for joint image generation and compression using generative adversarial networks.
In this work, we present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation.
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 #6 on JPEG Artifact Correction on LIVE1 (Quality 10 Color)
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.
In this paper, we show that a generalized Gaussian mixture model (GGMM) captures the underlying distribution of patches better than a GMM.
Image restoration methods aim to recover the underlying clean image from corrupted observations.
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.
We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem.
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers.
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.
Thus, we propose hand segmentation method for hand-object interaction using only a depth map.
In our system, we track hand articulations by minimizing discrepancy between depth map from sensor and computer-generated hand model.
We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects.
This letter presents a novel approach to extract reliable dense and long-range motion trajectories of articulated human in a video sequence.
Experimental results show that the proposed method produces high quality dense depth estimates, and is robust to noisy measurements.