Improving pedestrian segmentation using region proposal-based CNN semantic segmentation

Model-Based Pedestrian Segmentation

The Problem: Many video-surveillance applications involve video data of humans in scenes. To accurately describe the location, action, and appearance of humans under video surveillance, we need to segment the people from the scene. We propose to use a model-based approach to segment humans from the scene. We are in particular interested in pedestrians from video data. A good segmentation will a...

Improving Text Segmentation Using Latent Semantic Analysis

Choi, Wiemer-Hastings and Moore (2001) proposed to use Latent Semantic Analysis to extract semantic knowledge from corpora in order to improve the accuracy of a text segmentation algorithm. By comparing the accuracy of the very same algorithm depending on whether or not it takes into account complementary semantic knowledge, they were able to show the benefit derived from such knowledge. In the...

Prostate segmentation and lesions classification in CT images using Mask R-CNN

Purpose: Non-cancerous prostate lesions such as prostate calcification, prostate enlargement, and prostate inflammation cause too many problems for men’s health. This research proposes a novel approach, a combination of image processing techniques and deep learning methods for classification and segmentation of the prostate in CT-scan images by considering the experienced physicians’ reports. ...

Automatic MRI Prostate Segmentation based CNN-ASM

CNN-based Segmentation of Medical Imaging Data

Convolutional neural networks have been applied to a wide variety of computer vision tasks. Recent advances in semantic segmentation have enabled their application to medical image segmentation. While most CNNs use two-dimensional kernels, recent CNN-based publications on medical image segmentation featured three-dimensional kernels, allowing full access to the three-dimensional structure of me...