scholarly journals FAST STEREO IMAGE DEPTH ESTIMATION ALGORITHM BASED ON CHANGE DETECTION METHODS IN CAMERAS FIELD OF VIEW

2018 ◽  
pp. 94-98
Author(s):  
A. V. Khamukhin ◽  
V. V. Kuzmina
Keyword(s):  
Computational Complexity ◽  
Fast Algorithm ◽  
Moving Objects ◽  
Depth Estimation ◽  
Estimation Algorithm ◽  
Video Stream ◽  
Field Of View ◽  
Stereo Image ◽  
Detection Methods ◽  
Image Depth

Stereo image depth estimation algorithms have not been widely used in CCTV systems yet, since either their accuracy is low or their computational complexity is high, which prevents implementation of these algorithms due to economic limitations on the cost of equipment used in security systems. In this paper, a new fast algorithm is proposed for reconstructing the depth of stereo images, which is used for reliable event identification in the CCTV systems cameras field of view. This real-time video stream processing method is based on the use of a fast algorithm for detecting changes in the cameras scene in the combination with SGBM depth estimation algorithm, which processes only image areas containing scene changes. The proposed method of significant computational complexity reduction of the depth estimation algorithm makes possible to acquire information about the distance from the cameras to moving objects in the field of view and use this information as an additional feature, that helps to reduce the operational cost and to improve the reliability of CCTV systems.

scholarly journals Anytime Stereo Image Depth Estimation on Mobile Devices

2019 ◽  
Author(s):  
Yan Wang ◽  
Zihang Lai ◽  
Gao Huang ◽  
Brian H. Wang ◽  
Laurens van der Maaten ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Depth Estimation ◽  
Stereo Image ◽  
Image Depth

scholarly journals DL-based segmentation of endoscopic scenes for mitral valve repair

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Matthias Ivantsits ◽  
Lennart Tautz ◽  
Simon Sündermann ◽  
Isaac Wamala ◽  
Jörg Kempfert ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Minimally Invasive ◽  
Mitral Valve Repair ◽  
Video Stream ◽  
Field Of View ◽  
Valve Repair ◽  
Distance Measurements ◽  
Anatomical Structures ◽  
Endoscopic Video ◽  
Camera Position

AbstractMinimally invasive surgery is increasingly utilized for mitral valve repair and replacement. The intervention is performed with an endoscopic field of view on the arrested heart. Extracting the necessary information from the live endoscopic video stream is challenging due to the moving camera position, the high variability of defects, and occlusion of structures by instruments. During such minimally invasive interventions there is no time to segment regions of interest manually. We propose a real-time-capable deep-learning-based approach to detect and segment the relevant anatomical structures and instruments. For the universal deployment of the proposed solution, we evaluate them on pixel accuracy as well as distance measurements of the detected contours. The U-Net, Google’s DeepLab v3, and the Obelisk-Net models are cross-validated, with DeepLab showing superior results in pixel accuracy and distance measurements.

Nearby Phase Search Algorithm with Reduced Complexity for Higher Order QAM Formats

2013 ◽  
Vol 760-762 ◽  
pp. 1869-1873
Author(s):  
Li Min Xia ◽  
Xian Zhou ◽  
Dong Yan ◽  
Na Na Zhang ◽  
Xiao Yun Wu
Keyword(s):  
Computational Complexity ◽  
Bit Error Rate ◽  
High Speed ◽  
Optical Transmission ◽  
Search Algorithm ◽  
Estimation Algorithm ◽  
Coherent Receivers ◽  
Multi Level ◽  
Reduced Complexity ◽  
Laser Line Width

This paper proposes a nearby phase search (NPS) algorithm based on BPS estimation algorithm in optical coherent receivers. And its suitable for arbitrary multi-level modulation. Making use of the continuity of phase change, the proposed NPS algorithm is applied to process nearby symbols by taking the pre-estimation phase of each symbol block as reference point. Compared to the traditional blind phase search (BPS) algorithm and its improved two-stage BPS algorithm, the performance of the proposed NPS algorithm is greatly improved in ultra-high speed coherent optical transmission system. By the simulation, the effectiveness and feasibility of the proposed algorithm are demonstrated in 28GBaud 16-QAM and 64-QAM system. Its shown that the computational complexity of the NPS algorithm greatly reduces in the guarantee of laser line width tolerance and bit error rate.

scholarly journals The Synthesis of Unpaired Underwater Images for Monocular Underwater Depth Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qi Zhao ◽  
Ziqiang Zheng ◽  
Huimin Zeng ◽  
Zhibin Yu ◽  
Haiyong Zheng ◽  
...  
Keyword(s):  
Structural Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Image Synthesis ◽  
Map Estimation ◽  
Estimation Model ◽  
Depth Prediction ◽  
Underwater Image ◽  
Underwater Vision ◽  
Image Depth

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.

scholarly journals DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES

2020 ◽  
Vol V-2-2020 ◽  
pp. 451-458
Author(s):  
L. Madhuanand ◽  
F. Nex ◽  
M. Y. Yang
Keyword(s):  
Deep Learning ◽  
Ground Level ◽  
Depth Estimation ◽  
Aerial Images ◽  
Aerial Image ◽  
Depth Information ◽  
Single Image ◽  
Monocular Depth ◽  
Uav Images ◽  
Image Depth

Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.

scholarly journals A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 980 ◽  
Author(s):  
Hui Feng ◽  
Xiaoqing Zhao ◽  
Zhengquan Li ◽  
Song Xing
Keyword(s):  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Base Station ◽  
Damping Factor ◽  
Mimo Detection ◽  
Discrete Estimation

In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

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