scholarly journals ICNet for Real-Time Semantic Segmentation on High-Resolution Images

2018 ◽  
pp. 418-434 ◽  
Author(s):  
Hengshuang Zhao ◽  
Xiaojuan Qi ◽  
Xiaoyong Shen ◽  
Jianping Shi ◽  
Jiaya Jia
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Semantic Segmentation ◽  
High Resolution Images

Faster-PPN: Towards Real-Time Semantic Segmentation with Dual Mutual Learning for Ultra-High Resolution Images

2021 ◽  
Author(s):  
Bicheng Dai ◽  
Kaisheng Wu ◽  
Tong Wu ◽  
Kai Li ◽  
Yanyun Qu ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Semantic Segmentation ◽  
Mutual Learning ◽  
High Resolution Images

Electrical Probing and Surface Imaging of Deep Sub-Micron Integrated Circuits

1999 ◽  
Author(s):  
Kenneth Krieg ◽  
Richard Qi ◽  
Douglas Thomson ◽  
Greg Bridges
Keyword(s):  
High Resolution ◽  
Integrated Circuits ◽  
Real Time ◽  
High Speed ◽  
Time Signal ◽  
Surface Imaging ◽  
Atomic Force ◽  
Submicron Structures ◽  
High Resolution Images ◽  
Capacitive Loading

Abstract A contact probing system for surface imaging and real-time signal measurement of deep sub-micron integrated circuits is discussed. The probe fits on a standard probe-station and utilizes a conductive atomic force microscope tip to rapidly measure the surface topography and acquire real-time highfrequency signals from features as small as 0.18 micron. The micromachined probe structure minimizes parasitic coupling and the probe achieves a bandwidth greater than 3 GHz, with a capacitive loading of less than 120 fF. High-resolution images of submicron structures and waveforms acquired from high-speed devices are presented.

Real-time, parallel segmentation of high-resolution images on multi-core platforms

2014 ◽  
Vol 13 (4) ◽  
pp. 685-702 ◽  
Author(s):  
Dana Forsthoefel Fitzgerald ◽  
D. Scott Wills ◽  
Linda M. Wills
Keyword(s):  
High Resolution ◽  
Real Time ◽  
High Resolution Images

scholarly journals Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

2017 ◽  
Vol 9 (5) ◽  
pp. 500 ◽  
Author(s):  
Mi Zhang ◽  
Xiangyun Hu ◽  
Like Zhao ◽  
Ye Lv ◽  
Min Luo ◽  
...  
Keyword(s):  
High Resolution ◽  
Semantic Segmentation ◽  
Manifold Ranking ◽  
Multi Scale ◽  
High Resolution Images

scholarly journals Real-Time Moving Object Detection in High-Resolution Video Sensing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3591 ◽  
Author(s):  
Haidi Zhu ◽  
Haoran Wei ◽  
Baoqing Li ◽  
Xiaobing Yuan ◽  
Nasser Kehtarnavaz
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Computationally Efficient ◽  
Real Time Processing ◽  
Time Processing ◽  
Video Frames ◽  
High Resolution Images

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

scholarly journals RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1985
Author(s):  
Qi Wang ◽  
Meihan Wu ◽  
Fei Yu ◽  
Chen Feng ◽  
Kaige Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Semantic Segmentation ◽  
Processing Unit ◽  
Real Time Processing ◽  
Side Scan Sonar ◽  
Network Training ◽  
Sonar Sensor ◽  
Sonar Images

Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

Semantic segmentation of high-resolution images

2017 ◽  
Vol 60 (12) ◽  
Author(s):  
Juhong Wang ◽  
Bin Liu ◽  
Kun Xu
Keyword(s):  
High Resolution ◽  
Semantic Segmentation ◽  
High Resolution Images
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