IS&T | Library

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

140 5

IMAGE SEGMENTATION
CONVOLUTIONAL NEURAL NETWORK
FLUORESCENCE MICROSCOPY
INTENSITY INHOMOGENEITY
TUBULE SEGMENTATION

Soonam Lee, Chichen Fu, Paul Salama, Kenneth W. Dunn, Edward J. Delp

Pages 199-1 - 199-8, January 2018, © Society for Imaging Science and Technology 2018

DOI

10.2352/ISSN.2470-1173.2018.15.COIMG-199

Volume 30

Issue 15

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods.

Digital Library: EI

Published Online: January 2018

Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN)

54 13

CONVOLUTIONAL NEURAL NETWORK
POLYNOMIAL
POOLING
CONVOLUTION
NEURAL NETWORK
MNIST
CIFAR
SVHN

Patrick Martell, Vijayan Asari

Pages 338-1 - 338-6, January 2018, © Society for Imaging Science and Technology 2018

DOI

10.2352/ISSN.2470-1173.2018.10.IMAWM-338

Volume 30

Issue 10

Convolutional neural networks (CNNs) tend to look at methods to improve performance by adding more input data, making modifications on existing data, or changing the design of the network to better suit the variables. The goal of this work is to supplement the small number of existing methods that do not use one of the previously mentioned techniques. This research aims to show that with a standard CNN, classification accuracy rates have the potential to be improved without changes to the data or major network design modifications, such as adding convolution or pooling layers. A new layer is proposed that will be inserted in a similar location as the non-linearity functions in standard CNNs. This new layer creates a localized connectivity for each perceptron to a polynomial of N^th degree. This can be performed in both the convolutional portion and the fully connected portion of the network. The proposed polynomial layer is added with the idea that the higher dimensionality enables a better description of the input space, which leads to a higher classification rate. Two different datasets, MNIST and CIFAR10 are utilized for classification, each containing 10 distinct classes and having similar training set sizes, 60,000 and 50,000 images, respectively. These datasets differ in that the images are 28 × 28 grayscale and 32 × 32 RGB, respectively. It is shown that the added polynomial layers enable the chosen CNN design to have a higher rate of accuracy on the MNIST dataset. This effect was only similar at a lower learning rate with the CIFAR10 dataset.

Digital Library: EI

Published Online: January 2018

Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks

82 13

FLASHOVER PREDICTION
CONVOLUTIONAL NEURAL NETWORK
OBJECT RECOGNITION AND SEGMENTATION
COMPUTER VISION

Kyongsik Yun, Jessi Bustos, Thomas Lu

Pages 127-1 - 127-4, January 2018, © Society for Imaging Science and Technology 2018

DOI

10.2352/ISSN.2470-1173.2018.09.SRV-127

Volume 30

Issue 9

A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: firefighters. Firefighters' safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover ("angel fingers") and can be quantified by color, size, and shape. Using a color video stream from a firefighter's body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred.

Digital Library: EI

Published Online: January 2018

Goal!! Event detection in sports video

408 47

CONVOLUTIONAL NEURAL NETWORK
EVENT DETECTION
SPORTS VIDEO
GOAL DETECTION
FOOTBALL

Grigorios Tsagkatakis, Mustafa Jaber, Panagiotis Tsakalides

Pages 15 - 20, January 2017, © Society for Imaging Science and Technology 2017

DOI

10.2352/ISSN.2470-1173.2017.16.CVAS-344

Volume 29

Issue 16

Understanding complex events from unstructured video, like scoring a goal in a football game, is an extremely challenging task due to the dynamics, complexity and variation of video sequences. In this work, we attack this problem exploiting the capabilities of the recently developed framework of deep learning. We consider independently encoding spatial and temporal information via convolutional neural networks and fusion of features via regularized Autoencoders. To demonstrate the capacities of the proposed scheme, a new dataset is compiled, compose of goal and no-goal sequences. Experimental results demonstrate that extremely high classification accuracy can be achieved, from a dramatically limited number of examples, by leveraging pre-trained models with fine-tuned fusion of spatio-temporal features.

Digital Library: EI

Published Online: January 2017

Robust Person Recognition using CNN

223 0

HEAD DETECTION
CONVOLUTIONAL NEURAL NETWORK
DEEP LEARNING

Ming Chen, Qian Lin, Jan P. Allebach, Fengqing Zhu

Pages 45 - 50, January 2017, © Society for Imaging Science and Technology 2017

DOI

10.2352/ISSN.2470-1173.2017.10.IMAWM-165

Volume 29

Issue 10

Person detection and recognition has many applications in autonomous driving, smart home and smart office applications. Knowledge about the presence of a person in the environment can be used in safety solutions such as collision avoidance, in energy conservation solutions such as turning lights and air-conditioning off when there is no person around, and in meeting and collaboration solutions such as locating a vacant room. In this paper, we present a solution that can reliably detect and recognize persons under different lighting conditions and pose based on head detection and recognition using deep learning. The system is proved to achieve good results on a challenging dataset.

Digital Library: EI

Published Online: January 2017

Pre-training via fitting deep neural network to rich-model features extraction procedure and its effect on deep learning for steganalysis

140 35

DEEP LEARNING
STEGANALYSIS
CONVOLUTIONAL NEURAL NETWORK
DCTR
MODEL FITTING

Jishen Zeng, Shunquan Tan, Bin Li, Jiwu Huang

DOI

10.2352/ISSN.2470-1173.2017.7.MWSF-324

Volume 29

Issue 7

Recent studies have shown that the steganalytic approaches based on deep learning frameworks cannot surpass their rich?model features based companions in peiformance. According to our analysis, one of the main causes of the unsatisfactory performance of deep learning frameworks is that training procedure tends to get stuck at local plateaus or even diverge when starting from a non-ideal initial state. In this paper we will try to investi?gate how to fit deep neural network to a rich-model features set. We regard it as a pre-training procedure and study its 4fect on deep learning for steganalysis. The state-of-the-art JPEG steganalytic features set DCTR is selected as the target and its features extraction procedure is divided into multiple sub-models. A deep learning framework with similar sub-networks is proposed. In the pre-training procedure we train theframeworkfrom bottom to up, fitting the output of each sub-network to the actual output of the corresponding sub-module of DCTR. The motivation behind the scenario is that we reinforce the proposed framework learn to fit the nonlinear mapping implicit in DCTR and expect when it is trainedfrom an initial state which represents an approximate so?lution of DCTR, we can get better peiformance compared to what DCTR has achieved.

Digital Library: EI

Published Online: January 2017

Histogram Layer, Moving Convolutional Neural Networks Towards Feature-Based Steganalysis

311 21

STEGANOGRAPHY
STEGANALYSIS
CONVOLUTIONAL NEURAL NETWORK

Vahid Sedighi, Jessica Fridrich

DOI

10.2352/ISSN.2470-1173.2017.7.MWSF-325

Volume 29

Issue 7

Feature-based steganalysis has been an integral tool for detecting the presence of steganography in communication channels for a long time. In this paper, we explore the possibility to utilize powerful optimization algorithms available in convolutional neural network packages to optimize the design of rich features. To this end, we implemented a new layer that simulates the formation of histograms from truncated and quantized noise residuals computed by convolution. Our goal is to show the potential to compactify and further optimize existing features, such as the projection spatial rich model (PSRM).

Digital Library: EI

Published Online: January 2017

Prune the Convolutional Neural Networks with Sparse Shrink

64 0

CONVOLUTIONAL NEURAL NETWORK
IMAGE CLASSIFICATION
SPARSE RECONSTRUCTION

Xin Li, Changsong Liu

DOI

10.2352/ISSN.2470-1173.2017.6.MOBMU-306

Volume 29

Issue 6