Scott E. Umbaugh

Binarization of color document images via luminance and saturation color features

Dermatologist-level classification of skin cancer with deep neural networks

A Neural Network-Based Novelty Detector for Image Sequence Analysis

What dermatologists should know about digital imaging

A survey on deep learning in medical image analysis

Image Analysis and Computer Vision: 1995

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

An intelligent system for livestock disease surveillance

Detecting and tracking vehicles in traffic by unmanned aerial vehicles

Survey over image thresholding techniques and quantitative performance evaluation

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

Medical image analysis: progress over two decades and the challenges ahead

Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

A review of novelty detection

Telemedicine Technology and Clinical Applications

Digital image processing and analysis : human and computer vision applications with CVIPtools

Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom

Thermal Imaging of Normal and Cranial Cruciate Ligament‐Deficient Stifles in Dogs

Automatic color segmentation of images with application to detection of variegated coloring in skin tumors

An automatic color segmentation algorithm with application to identification of skin tumor borders

Skin lesion classification using relative color features

Detection of atypical texture features in early malignant melanoma

Unsupervised color image segmentation: with application to skin tumor borders

Computer Vision and Image Processing: A Practical Approach Using CVIPTools

Automatic color segmentation algorithms-with application to skin tumor feature identification