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Found 12 papers, 1 papers with code
Towards Efficient and Accurate CT Segmentation via Edge-Preserving Probabilistic Downsampling
Downsampling images and labels, often necessitated by limited resources or to expedite network training, leads to the loss of small objects and thin boundaries.
High-Quality Face Caricature via Style Translation
We perform an incremental facial exaggeration from the real image to the caricature faces using the encoder and generator's latent space.
Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation
We developed a model that is similar in spirit to the well-established encoder-decoder and residual convolution neural networks.
Brain MRI Segmentation using Rule-Based Hybrid Approach
Performance of these classifiers is investigated over different images of brain MRI and the variation in the performance of these classifiers is observed for different brain tissues.
Illumination Invariant Foreground Object Segmentation using ForeGANs
To address this problem, our presented GAN model is trained on background image samples with dynamic changes, after that for testing the GAN model has to generate the same background sample as test sample with similar conditions via back-propagation technique.
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends
In order to overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms.
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies.
Unsupervised RGBD Video Object Segmentation Using GANs
To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy.
Unsupervised Deep Context Prediction for Background Foreground Separation
Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations.
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise.
Comparative Study of ECO and CFNet Trackers in Noisy Environment
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades.
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.
