Overview of DEXTR - Object Segmentation Using Extreme Points
DEXTR, or Deep Extreme Cut, is a computer vision technique that allows the precise segmentation of an object in an image. This is accomplished by using the extreme points of an object, or the left-most, right-most, top, and bottom pixels, as guiding signals for the input to the network. The extreme points are annotated and used to create a heatmap with activations in those regions.
The heatmap is created by centering a 2D Gaussian ar
What Is HANet?
HANet stands for Height-driven Attention Network, which is an additional module designed to improve semantic segmentation in urban-scene images. HANet focuses on selecting informative features or classes based on the vertical position of the pixel to enhance the accuracy of semantic segmentation in urban-scene images.
Why Is HANet Important?
The pixel-wise class distributions in urban-scene images are significantly different from each other among segmented sections in the imag
PALED: An Effective Approach to Quantify Patchiness in Biomedical Images
Biomedical imaging techniques have transformed the way medical professionals diagnose and treat various diseases. From X-ray scans to magnetic resonance imaging (MRI) to computed tomography (CT), these techniques have become critical for understanding the internal structures of the human body, non-invasively. However, imaging data can be complex, and the interpretation of these images is challenging for clinicians and rese
Overview of SCNN_UNet_ConvLSTM
SCNN_UNet_ConvLSTM is an artificial intelligence technique that combines different deep learning models to make accurate predictions on image segmentation and video tracking tasks. This technique uses a combination of spatial CNN with UNet based Encoder-decoder and ConvLSTM to capture high-dimensional information from images and video streams.
What is SCNN_UNet_ConvLSTM?
SCNN_UNet_ConvLSTM is a deep learning technique that is used to solve various computer visi