scholarly journals STEREO MATCHING ALGORITHM BASED ON ILLUMINATION CONTROL TO IMPROVE THE ACCURACY

2016 ◽  
Vol 35 (1) ◽  
pp. 39 ◽  
Author(s):  
Rostam Affendi Hamzah ◽  
Haidi Ibrahim ◽  
Anwar Hasni Abu Hassan
Keyword(s):  
Image Quality ◽  
Stereo Vision ◽  
Stereo Matching ◽  
State Of The Art ◽  
Experimental Results ◽  
New Method ◽  
Absolute Difference ◽  
Matching Algorithm ◽  
Disparity Maps

This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.

A New Method on Super Pixel Reducing Stereo Matching Time of Integrated Imaging

2020 ◽  
pp. 2154014
Author(s):  
Xue-Guang Wang ◽  
Ming Li ◽  
Lei Zhang ◽  
Hui Zhao ◽  
Thelma D. Palaoag
Keyword(s):  
3D Reconstruction ◽  
Conventional Method ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Technical Difficulty ◽  
New Method ◽  
Matching Algorithm ◽  
The Core ◽  
Pixel Matching ◽  
Novel Method

Stereo vision and 3D reconstruction technologies are increasingly concerned in many fields. Stereo matching algorithm is the core of stereo vision and also a technical difficulty. A novel method based on super pixels is mentioned in this paper to reduce the calculating amount and the time. Stereo images from University of Tsukuba are used to test our method. The proposed method spends only 1% of the time spent by the conventional method. Through a two-step super-pixel matching optimization, it takes 6.72 s to match a picture, which is 12.96% of the pre-optimization.

Research on Stereo Matching Algorithm in Intelligent Vehicle Application

2014 ◽  
Vol 678 ◽  
pp. 35-38 ◽  
Author(s):  
Peng He ◽  
Feng Gao
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Driver Assistance System ◽  
Experimental Results ◽  
Assistance System ◽  
Intelligent Vehicle ◽  
Driver Assistance ◽  
Matching Algorithm ◽  
Environment Perception ◽  
Vehicle Technologies

Perception of environment in front of driving vehicle is a core investigation theme of intelligent vehicle technologies aiming to increase safety, convenience and efficiency of driving. Using stereo vision for environment perception is a hot technology. This paper developed an algorithm for stereo matching in intelligent vehicle application. The experimental results indicate that this algorithm is effective. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.

scholarly journals Application of Positional Entropy to Fast Shannon Entropy Estimation for Samples of Digital Signals

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1173
Author(s):  
Marcin Cholewa ◽  
Bartłomiej Płaczek
Keyword(s):  
Shannon Entropy ◽  
State Of The Art ◽  
Large Data ◽  
Experimental Results ◽  
New Method ◽  
Computational Experiments ◽  
Digital Signals ◽  
Theoretical Concepts ◽  
Entropy Estimation

This paper introduces a new method of estimating Shannon entropy. The proposed method can be successfully used for large data samples and enables fast computations to rank the data samples according to their Shannon entropy. Original definitions of positional entropy and integer entropy are discussed in details to explain the theoretical concepts that underpin the proposed approach. Relations between positional entropy, integer entropy and Shannon entropy were demonstrated through computational experiments. The usefulness of the introduced method was experimentally verified for various data samples of different type and size. The experimental results clearly show that the proposed approach can be successfully used for fast entropy estimation. The analysis was also focused on quality of the entropy estimation. Several possible implementations of the proposed method were discussed. The presented algorithms were compared with the existing solutions. It was demonstrated that the algorithms presented in this paper estimate the Shannon entropy faster and more accurately than the state-of-the-art algorithms.

Integration of optical flow and Multi-Path-Viterbi algorithm for stereo vision

2017 ◽  
Vol 15 (03) ◽  
pp. 1750022 ◽  
Author(s):  
Qiwei Xie ◽  
Qian Long ◽  
Seiichi Mita
Keyword(s):  
Optical Flow ◽  
Stereo Matching ◽  
Viterbi Algorithm ◽  
State Of The Art ◽  
Structural Similarity ◽  
Processing Method ◽  
Matching Algorithm ◽  
Disparity Maps ◽  
Generalized Variation ◽  
Robust Result

This paper proposes a novel stereo matching algorithm to solve environment sensing problems. It integrates a non-convex optical flow and Viterbi process. The non-convex optical flow employs a new adaptive weighted non-convex Total Generalized Variation (TGV) model, which can obtain sharp disparity maps. Structural similarity, total variation constraint, and a specific merging strategy are combined with the 4 bi-directional Viterbi process to improve the robustness. In the fusion of the optical flow and Viterbi process, a new occlusion processing method is incorporated in order to get more sharp disparity and more robust result. Extensive experiments are conducted to compare this algorithm with other state-of-the-art methods. Experimental results show the superiority of our algorithm.

scholarly journals Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Contour Detection ◽  
Experimental Results ◽  
Contour Method ◽  
Class Differences ◽  
Dilated Convolution ◽  
The Mean ◽  
Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

scholarly journals Stereo Imaging Using Hardwired Self-Organizing Object Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5833
Author(s):  
Ching-Han Chen ◽  
Guan-Wei Lan ◽  
Ching-Yi Chen ◽  
Yen-Hsiang Huang
Keyword(s):  
Neural Network ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Imaging System ◽  
Object Segmentation ◽  
Hierarchical Architecture ◽  
Absolute Difference ◽  
Stereo Imaging ◽  
Som Neural Network ◽  
Self Organizing

Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 480 resolution color images.

scholarly journals Multimodal Summarization with Guidance of Multimodal Reference

2020 ◽  
Vol 34 (05) ◽  
pp. 9749-9756
Author(s):  
Junnan Zhu ◽  
Yu Zhou ◽  
Jiajun Zhang ◽  
Haoran Li ◽  
Chengqing Zong ◽  
...  
Keyword(s):  
Objective Function ◽  
Evaluation Method ◽  
Reference Data ◽  
State Of The Art ◽  
Semantic Space ◽  
Experimental Results ◽  
Model Output ◽  
Proposed Model ◽  
Evaluation Metric

Multimodal summarization with multimodal output (MSMO) is to generate a multimodal summary for a multimodal news report, which has been proven to effectively improve users' satisfaction. The existing MSMO methods are trained by the target of text modality, leading to the modality-bias problem that ignores the quality of model-selected image during training. To alleviate this problem, we propose a multimodal objective function with the guidance of multimodal reference to use the loss from the summary generation and the image selection. Due to the lack of multimodal reference data, we present two strategies, i.e., ROUGE-ranking and Order-ranking, to construct the multimodal reference by extending the text reference. Meanwhile, to better evaluate multimodal outputs, we propose a novel evaluation metric based on joint multimodal representation, projecting the model output and multimodal reference into a joint semantic space during evaluation. Experimental results have shown that our proposed model achieves the new state-of-the-art on both automatic and manual evaluation metrics. Besides, our proposed evaluation method can effectively improve the correlation with human judgments.

scholarly journals DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6457
Author(s):  
Hayat Ullah ◽  
Muhammad Irfan ◽  
Kyungjin Han ◽  
Jong Weon Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Reference Image ◽  
Primary Concern ◽  
Panoramic Images ◽  
Stitching Errors

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

Video Matching by One-Dimensional PSNR Profile

2013 ◽  
Vol 479-480 ◽  
pp. 174-178
Author(s):  
Shi Wei Lo
Keyword(s):  
Image Quality ◽  
Video Sequence ◽  
Experimental Results ◽  
Video Sequences ◽  
Motion Feature ◽  
One Dimensional ◽  
Matching Process ◽  
Video Matching

This paper addresses a compact framework to matching video sequences through a PSNR-based profile. This simplify video profile is suitable to matching process when apply in disordered undersea videos. As opposed to using color and motion feature across the video sequence, we use the image quality of successive frames to be a feature of videos. We employ the PSNR quality feature to be a video profile rather than the complex contend-based analysis. The experimental results show that the proposed approach permits accurate of matching video. The performance is satisfactory on determine correct video from undersea dataset.

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