scholarly journals A Hardware Accelerator for Real Time Sliding Window Based Pedestrian Detection on High Resolution Images

2016 ◽  
pp. 46-66 ◽  
Author(s):  
Asim Khan ◽  
Muhammad Umar Karim Khan ◽  
Muhammad Bilal ◽  
Chong-Min Kyung
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Hardware Accelerator ◽  
High Resolution Images

scholarly journals FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images

2013 ◽  
Author(s):  
Michael Hahnle ◽  
Frerk Saxen ◽  
Matthias Hisung ◽  
Ulrich Brunsmann ◽  
Konrad Doll
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Pedestrian Detection ◽  
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 High-Resolution Image Inpainting Based on Multi-Scale Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1370 ◽  
Author(s):  
Tingzhu Sun ◽  
Weidong Fang ◽  
Wei Chen ◽  
Yanxin Yao ◽  
Fangming Bi ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Pedestrian Detection ◽  
Image Inpainting ◽  
Resolution Image ◽  
Multi Scale ◽  
High Resolution Image ◽  
Adversarial Network ◽  
Generation Network ◽  
High Resolution Images

Although image inpainting based on the generated adversarial network (GAN) has made great breakthroughs in accuracy and speed in recent years, they can only process low-resolution images because of memory limitations and difficulty in training. For high-resolution images, the inpainted regions become blurred and the unpleasant boundaries become visible. Based on the current advanced image generation network, we proposed a novel high-resolution image inpainting method based on multi-scale neural network. This method is a two-stage network including content reconstruction and texture detail restoration. After holding the visually believable fuzzy texture, we further restore the finer details to produce a smoother, clearer, and more coherent inpainting result. Then we propose a special application scene of image inpainting, that is, to delete the redundant pedestrians in the image and ensure the reality of background restoration. It involves pedestrian detection, identifying redundant pedestrians and filling in them with the seemingly correct content. To improve the accuracy of image inpainting in the application scene, we proposed a new mask dataset, which collected the characters in COCO dataset as a mask. Finally, we evaluated our method on COCO and VOC dataset. the experimental results show that our method can produce clearer and more coherent inpainting results, especially for high-resolution images, and the proposed mask dataset can produce better inpainting results in the special application scene.

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.

Horizontal Well Placement Benefits of High Resolution Images in Real Time, Case Study from UAE

2018 ◽  
Author(s):  
A. B. Abdelkarim ◽  
S. AL-Hassani ◽  
S. Ahmed ◽  
H. Abiujmeih ◽  
A. Serry ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Horizontal Well ◽  
Well Placement ◽  
High Resolution Images
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