Search Results for author:
Found 10 papers, 2 papers with code
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
We investigated uncertainty sampling, annotation redundancy restriction, and initial dataset selection techniques.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
Structure-Aware NeRF without Posed Camera via Epipolar Constraint
This two-stage strategy is not convenient to use and degrades the performance because the error in the pose extraction can propagate to the view synthesis.
Estimation of 3D Human Pose Using Prior Knowledge
Estimating three-dimensional human poses from the positions of two-dimensional joints has shown promising results. However, using two-dimensional joint coordinates as input loses more information than image-based approaches and results in ambiguity. In order to overcome this problem, we combine bone length and camera parameters with two-dimensional joint coordinates for input. This combination is more discriminative than the two-dimensional joint coordinates in that it can improve the accuracy of the model's prediction depth and alleviate the ambiguity that comes from projecting three-dimensional coordinates into two-dimensional space.
A Deep Retinal Image Quality Assessment Network with Salient Structure Priors
Dual-branch SalStructIQA contains two CNN branches and one is guided by large-size salient structures while the other is guided by tiny-size salient structures.
A Dark and Bright Channel Prior Guided Deep Network for Retinal Image Quality Assessment
Retinal image quality assessment is an essential task in the diagnosis of retinal diseases.
A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet.
DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation
In this paper, we propose a novel segmentation approach, named Cartesian-polar dual-domain network (DDNet), which for the first time considers the complementary of the Cartesian domain and the polar domain.
Feature selection via simultaneous sparse approximation for person specific face verification
There is an increasing use of some imperceivable and redundant local features for face recognition.
Feature Selection via Sparse Approximation for Face Recognition
Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades.
