GAN-Based Steganography with the Concatenation of Multiple Feature Maps

2020 ◽  
pp. 3-17
Author(s):  
Haibin Wu ◽  
Fengyong Li ◽  
Xinpeng Zhang ◽  
Kui Wu
Keyword(s):  
Feature Maps ◽  
Multiple Feature

scholarly journals ON-ROAD INFORMATION EXTRACTION FROM LIDAR DATA VIA MULTIPLE FEATURE MAPS

2020 ◽  
Vol V-1-2020 ◽  
pp. 207-213
Author(s):  
H. Wu ◽  
Z. Xie ◽  
C. Wen ◽  
C. Wang ◽  
J. Li
Keyword(s):  
Information Extraction ◽  
Elevation Gradient ◽  
Semantic Segmentation ◽  
Lidar Data ◽  
Feature Maps ◽  
Road Users ◽  
Multiple Feature ◽  
Lower Accuracy ◽  
Vertical Density ◽  
New Framework

Abstract. On-road information, including road boundaries, road markings, and road cracks, provides significant guidance or warning to all road users. Recently, the on-road information extraction from LiDAR data have been widely studied. However, for the LiDAR data with lower accuracy and higher noise, some detailed information, such as road boundary, is difficult to be extracted correctly. Furthermore, most of previous studies lack an exploration of efficiently extracting multiple on-road information from a single framework. In this paper, we propose a new framework that can simultaneously extract multiple on-road information from high accuracy LiDAR data and can also more robustly extract detailed road boundaries from low accuracy LiDAR data. First, we propose a Curb-Aware Ground Filter to extract ground points with rich curb structure features. Second, we transform the vertical density, elevation gradient and intensity features of the ground points into multiple feature maps and extract multiple on-road information from the feature maps by employing a semantic segmentation network. Experimental results on three datasets with different data accuracy demonstrate that our method outperforms other recent competitive methods.

Alignment of Coexisting Cortical Maps in a Motor Control Model

1996 ◽  
Vol 8 (4) ◽  
pp. 731-755 ◽  
Author(s):  
Yinong Chen ◽  
James A. Reggia
Keyword(s):  
Primary Motor Cortex ◽  
Temporal Correlation ◽  
Three Dimensions ◽  
Feature Maps ◽  
Temporal Correlations ◽  
Antagonist Muscles ◽  
Multiple Feature ◽  
Primary Sensorimotor Cortex ◽  
Cortical Regions ◽  
Spatial Locations

How do multiple feature maps that coexist in the same region of cerebral cortex align with each other? We hypothesize that such alignment is governed by temporal correlations: features in one map that are temporally correlated with those in another come to occupy the same spatial locations in cortex over time. To examine the feasibility of this hypothesis and to establish some of its detailed implications, we studied a multilayered, closed-loop computational model of primary sensorimotor cortex. A simulated arm moving in three dimensions formed the external environment for the model cortical regions. Coexisting proprioceptive and motor maps formed and generally aligned in a fashion consistent with the temporal correlation hypothesis. For example, in simulated proprioceptive sensory cortex the map of elements responding strongly to stretch of a particular muscle matched the map of tension sensitivity in antagonist muscles. In simulated primary motor cortex the map of elements responding strongly to increased tension in specific muscles matched the map of output elements for the same muscles. These computational results suggest specific experimental measurements that can support or refute the temporal correlation hypothesis for map alignments.

Infrared ship target segmentation through integration of multiple feature maps

2016 ◽  
Vol 48-49 ◽  
pp. 14-25 ◽  
Author(s):  
Zhaoying Liu ◽  
Xiangzhi Bai ◽  
Changming Sun ◽  
Fugen Zhou ◽  
Yujian Li
Keyword(s):  
Feature Maps ◽  
Target Segmentation ◽  
Multiple Feature

scholarly journals Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps

2021 ◽  
Vol 11 (21) ◽  
pp. 10216
Author(s):  
Hyungsuk Kim ◽  
Juyoung Park ◽  
Hakjoon Lee ◽  
Geuntae Im ◽  
Jongsoo Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Screening Method ◽  
Network Models ◽  
Multiple Time ◽  
Feature Maps ◽  
Multiple Feature ◽  
Phase Images ◽  
The Common

Ultrasound (US) imaging is widely utilized as a diagnostic screening method, and deep learning has recently drawn attention for the analysis of US images for the pathological status of tissues. While low image quality and poor reproducibility are the common obstacles in US analysis, the small size of the dataset is a new limitation for deep learning due to lack of generalization. In this work, a convolutional neural network (CNN) using multiple feature maps, such as entropy and phase images, as well as a B-mode image, was proposed to classify breast US images. Although B-mode images contain both anatomical and textual information, traditional CNNs experience difficulties in abstracting features automatically, especially with small datasets. For the proposed CNN framework, two distinct feature maps were obtained from a B-mode image and utilized as new inputs for training the CNN. These feature maps can also be made from the evaluation data and applied to the CNN separately for the final classification decision. The experimental results with 780 breast US images in three categories of benign, malignant, and normal, showed that the proposed CNN framework using multiple feature maps exhibited better performances than the traditional CNN with B-mode only for most deep network models.

scholarly journals Contactless System with Mask and Temperature Detection

2021 ◽  
pp. 10-18
Author(s):  
Prof. Sheetal Mahadik ◽  
Namrata J. Ravat ◽  
Kunal Y. Singh ◽  
Suvita K. Yadav
Keyword(s):  
Large Scale ◽  
Face Mask ◽  
Temperature Sensing ◽  
Feature Maps ◽  
Temperature Detection ◽  
Learning Techniques ◽  
Multiple Feature ◽  
Public Areas ◽  
Contactless System ◽  
High Level

Coronavirus disease in 2019 has affected the world very badly on a large scale. One of the important protection methods is to wear masks in public areas. Also, while using public services it is important to wear a mask correctly if you want to use their services. However, there is very few researches on face mask detection based on image analysis. In this paper, we propose Face Mask, which is a high-accuracy and efficient face mask detector. The proposed system is a one-stage detector, which consists of a pyramid network to fuse high-level semantic information with multiple feature maps, and a module to focus on detecting face masks. In addition, we also propose a novel cross-class object removal algorithm that will reject predictions with low confidences and the high intersection of the union. Besides, we also focus on the possibilities of implementing Face Mask with a light-weighted neural network MobileNet for embedded or mobile devices. In this paper, we introduce an affordable solution aiming to increase COVID-19 indoor safety, covering relevant aspects: 1) contactless temperature sensing 2) mask detection. Contactless temperature sensing subsystem relies on Arduino Uno using an infrared sensor or thermal camera, while mask detection is performed by leveraging computer vision techniques and Deep Learning Techniques.

scholarly journals Progressive Feature Polishing Network for Salient Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12128-12135 ◽  
Author(s):  
Bo Wang ◽  
Quan Chen ◽  
Min Zhou ◽  
Zhiqiang Zhang ◽  
Xiaogang Jin ◽  
...  
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Hierarchical Structures ◽  
Salient Object Detection ◽  
Salient Object ◽  
Post Processing ◽  
Feature Maps ◽  
Multiple Feature ◽  
Benchmark Datasets ◽  
Multi Level

Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly incorporating all higher level context information. Moreover, it can keep the dimensions and hierarchical structures of the feature maps, which makes it flexible to be integrated with any CNN-based models. Empirical experiments show that our results are monotonically getting better with increasing number of FPMs. Without bells and whistles, PFPN outperforms the state-of-the-art methods significantly on five benchmark datasets under various evaluation metrics. Our code is available at: https://github.com/chenquan-cq/PFPN.

scholarly journals A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images

2018 ◽  
Vol 25 (9) ◽  
pp. 1136-1145 ◽  
Author(s):  
Qiang Zheng ◽  
Steven Warner ◽  
Gregory Tasian ◽  
Yong Fan
Keyword(s):  
Graph Cuts ◽  
Ultrasound Images ◽  
Feature Maps ◽  
Dynamic Graph ◽  
Multiple Feature ◽  
2D Ultrasound

Modeling student knowledge with self-organizing feature maps.

1993 ◽  
Author(s):  
Steven A. Harp ◽  
Tariq Samad ◽  
Michael Villano
Keyword(s):  
Feature Maps ◽  
Student Knowledge ◽  
Self Organizing
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