Titlebook: Image Analysis and Recognition; 14th International C Fakhri Karray,Aurélio Campilho,Farida Cheriet Conference proceedings 2017 The Editor(s

harpsichord

folliculitis

Jonathan Mojoo,Keiichi Kurosawa,Takio Kuritaituations.Provides analyses on children living in street sitThis book provides new insights on the lives of children in street situations by providing analyses from a qualitative perspective on the sociology of childhood. It proposes some insightful perspectives on the current discussion about the r

Expediency

Blood-Clot

End-to-End Deep Learning for Driver Distraction Recognitiontrained convolutional neural networks VGG-19 are extracted. Despite the variation in illumination conditions, camera position, driver’s ethnicity, and genders in our dataset, our best fine-tuned model, VGG-19 has achieved the highest test accuracy of 95% and an average accuracy of 80% per class. The

Dysplasia

Acclaim

Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofingting more complex, and counter-measure approaches are necessary. Following the current progress with convolutional neural networks (CNN) in classification tasks, we present an approach based on transfer learning using a pre-trained CNN model using only static features to recognize photo, video or ma

增长

黑豹

Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cance be a powerful prognostic tool for cancer detection; however, these radiomics-driven methods currently rely on hand-crafted sets of quantitative imaging-based features, which can limit their ability to fully characterize unique prostate cancer tumour traits. We present a novel . framework via a mixt

nonchalance

Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Predictionprovide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of . for lung cancer prediction using CT imaging data. In this study, we rea

有罪

Left Ventricle Wall Detection from Ultrasound Images Using Shape and Appearance Informationts are normally obtained from manual segmentation in ultrasound images, which depends on operator experience. Supporting this process through automatic segmentation methods is very challenging due to low resolution, missing information, noise, and blurring on these images. In this work, we propose a