Synthetic Datasets for Rebar Instance Segmentation Using Mask R-CNN

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. ...

ME R-CNN: Multi-Expert R-CNN for Object Detection

Recent CNN-based object detection methods have drastically improved their performances but still use a single classifier as opposed to ”multiple experts” in categorizing objects. The main motivation of introducing multi-experts is twofold: i) to allow different experts to specialize in different fundamental object shape priors and ii) to better capture the appearance variations caused by differ...

R-CNN minus R

Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search. In this paper, we investigate the role of proposal generation in CNN-based detectors in orde...

Characterizing Multiple Instance Datasets

Compressing Terrain Datasets Using Segmentation

We propose a novel compression scheme to achieve lossy compression of elevation datasets. Our scheme does not use predictors in the traditional sense. Our predictors are based on planar dataset segments. We believe that this is a far better way of expressing context in an elevation dataset since it can capture continuities in different geometries and allows us to provide an error bound on the o...