Leukocyte Segmentation via End-to-End Learning of Deep Convolutional Neural Networks

2019 ◽  
pp. 191-200 ◽  
Author(s):  
Yan Lu ◽  
Haoyi Fan ◽  
Zuoyong Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
End To End ◽  
Leukocyte Segmentation

scholarly journals Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

2016 ◽  
Author(s):  
Ying Zhang ◽  
Mohammad Pezeshki ◽  
Philémon Brakel ◽  
Saizheng Zhang ◽  
César Laurent ◽  
...  
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
End To End

scholarly journals A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 328 ◽  
Author(s):  
Khalil Khan ◽  
Muhammad Attique ◽  
Rehan Ullah Khan ◽  
Ikram Syed ◽  
Tae-Sun Chung
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks ◽  
Face Image ◽  
Deep Convolutional Neural Networks ◽  
Age And Gender ◽  
Face Image Analysis ◽  
Probability Maps ◽  
End To End ◽  
And Gender

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

Face Occlusion Detection Using Deep Convolutional Neural Networks

2016 ◽  
Vol 30 (09) ◽  
pp. 1660010 ◽  
Author(s):  
Yizhang Xia ◽  
Bailing Zhang ◽  
Frans Coenen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Point Of View ◽  
Upper Body ◽  
Occlusion Detection ◽  
Deep Convolutional Neural Networks ◽  
Head Detection ◽  
In The Wild ◽  
End To End ◽  
Image Pixels

With the rise of crimes associated with Automated Teller Machines (ATMs), security reinforcement by surveillance techniques has been a hot topic on the security agenda. As a result, cameras are frequently installed with ATMs, so as to capture the facial images of users. The main objective is to support follow-up criminal investigations in the event of an incident. However, in the case of miss-use, the user’s face is often occluded. Therefore, face occlusion detection has become very important to prevent crimes connected with ATM usage. Traditional approaches to solving the problem typically comprise a succession of steps: localization, segmentation, feature extraction and recognition. This paper proposes an end-to-end facial occlusion detection framework, which is robust and effective by combining region proposal algorithm and Convolutional Neural Networks (CNN). The framework utilizes a coarse-to-fine strategy, which consists of two CNNs. The first CNN detects the head element within an upper body image while the second distinguishes which facial part is occluded from the head image. In comparison with previous approaches, the usage of CNN is optimal from a system point of view as the design is based on the end-to-end principle and the model operates directly on image pixels. For evaluation purposes, a face occlusion database consisting of over 50[Formula: see text]000 images, with annotated facial parts, was used. Experimental results revealed that the proposed framework is very effective. Using the bespoke face occlusion dataset, Aleix and Robert (AR) face dataset and the Labeled Face in the Wild (LFW) database, we achieved over 85.61%, 97.58% and 100% accuracies for head detection when the Intersection over Union-section (IoU) is larger than 0.5, and 94.55%, 98.58% and 95.41% accuracies for occlusion discrimination, respectively.

Sign in / Sign up

Export Citation Format

Share Document