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Found 9 papers, 3 papers with code
Deep Learning for Multi-Label Learning: A Comprehensive Survey
Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point.
CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision
These new local distortions are generated by considering the scene context of the images that guarantees a high level of photo-realism.
A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment
Video quality assessment is a challenging problem having a critical significance in the context of medical imaging.
Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment
Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination.
Kalman Filter Based Multiple Person Head Tracking
For multi-target tracking, target representation plays a crucial rule in performance.
Adaptive Context Encoding Module for Semantic Segmentation
The object sizes in images are diverse, therefore, capturing multiple scale context information is essential for semantic segmentation.
Generative Smoke Removal
In minimally invasive surgery, the use of tissue dissection tools causes smoke, which inevitably degrades the image quality.
Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?
For feature extraction, we use statistical features based on bivariate histogram distribution of gradient magnitude~(GM) and Laplacian of Gaussian~(LoG).
A Smoke Removal Method for Laparoscopic Images
In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces error for the image processing (used in image guided surgery), but also reduces the visibility of the surgeons.
