2018 Digital Image Computing: Techniques and Applications (DICTA)最新文献_第8页

Histogram Alternation Based Digital Image Compression using Base-2 Coding 基于基-2编码的直方图交替数字图像压缩

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615830

Md.Atiqur Rahman, S. Islam, Jungpil Shin, Md. Rashedul Islam

{"title":"Histogram Alternation Based Digital Image Compression using Base-2 Coding","authors":"Md.Atiqur Rahman, S. Islam, Jungpil Shin, Md. Rashedul Islam","doi":"10.1109/DICTA.2018.8615830","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615830","url":null,"abstract":"The intention of data compression is to promote the storage and delivery of big images with excellent compression ratio and least distortion. Moreover, the number of internet user is growing day by day speedily. Therefore, the transfer of data is being another significant concern. The storage and the use of an uncompressed picture are very costly and time-consuming. There are many techniques such as Arithmetic coding, Run-length coding, Huffman coding, Shannon-Fano coding used to compress an image. Compression of a picture using the state-of-the-art techniques has a high impact. However, The compression ratio and transfer speed do not satisfy the current demand. This article proposes a new histogram alternation based lossy image compression using Base-2 coding. It increases the probabilities of an image by doing a little bit of change to its pixels level which helps to reduce code-word. This algorithm uses less storage space and works at high-speed to encode and decode an image. Average code length, compression ratio, mean square error and pick signal to noise ratio are used to estimate this method. The proposed method demonstrates better performance than the state-of-the-art techniques.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"19 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116099329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Learning Orientation-Estimation Convolutional Neural Network for Building Detection in Optical Remote Sensing Image 基于学习方向估计卷积神经网络的光学遥感图像建筑物检测

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615859

Yongliang Chen, W. Gong, Chaoyue Chen, Weihong Li

{"title":"Learning Orientation-Estimation Convolutional Neural Network for Building Detection in Optical Remote Sensing Image","authors":"Yongliang Chen, W. Gong, Chaoyue Chen, Weihong Li","doi":"10.1109/DICTA.2018.8615859","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615859","url":null,"abstract":"Benefiting from the great success of deep learning in computer vision, object detection with Convolutional Neural Network (CNN) based methods have drawn significant attentions. Various frameworks have been proposed which show awesome and robust performance for a large range of datasets. However, for building detection in remote sensing images, buildings always pose a diversity of orientation which makes it a challenge for the application of off-the-shelf methods to building detection in remote sensing images. In this work, we aim to integrate orientation regression into the popular axis-aligned bounding box to tackle this problem. To adapt the axis-aligned bounding boxes to arbitrarily orientated ones, we also develop an algorithm to estimate the Intersection Over Union (IOU) overlap between any two arbitrarily oriented boxes which is convenient to implement in Graphics Processing Unit (GPU) for fast computation. The proposed method utilizes CNN for both robust feature extraction and bounding box regression. We present our model in an end-to-end fashion making it easy to train. The model is formulated and trained to predict both orientation and location simultaneously obtaining tighter bounding box and hence, higher mean average precision (mAP). Experiments on remote sensing images of different scales shows a promising performance over the conventional one.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122493453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Feature-Level Fusion using Convolutional Neural Network for Multi-Language Synthetic Character Recognition in Natual Images 基于卷积神经网络的特征级融合自然图像多语言合成字符识别

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615845

Asghar Ali, M. Pickering

引用次数: 3

List of Meta Reviewers 元审稿人列表

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/dicta.2018.8615755

引用次数: 0

Disparity Guided Texture Inpainting for Light Field View Synthesis 视差引导纹理绘制光场视图合成

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615821

Yue Li, R. Mathew, D. Taubman

引用次数: 0

Road Trail Classification using Color Images for Autonomous Vehicle Navigation 基于彩色图像的自动驾驶汽车路径分类

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615834

K. Islam, S. Wijewickrema, Masud Pervez, S. O'Leary

引用次数: 6

Improving Supervised Microaneurysm Segmentation using Autoencoder-Regularized Neural Network 应用自编码器-正则化神经网络改进监督微动脉瘤分割

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615839

Rangwan Kasantikul, Worapan Kusakunniran

引用次数: 1

Satellite Multi-Vehicle Tracking under Inconsistent Detection Conditions by Bilevel K-Shortest Paths Optimization 基于双k最短路径优化的非一致检测条件下卫星多飞行器跟踪

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615873

Junpeng Zhang, X. Jia, Jiankun Hu, Kun Tan

{"title":"Satellite Multi-Vehicle Tracking under Inconsistent Detection Conditions by Bilevel K-Shortest Paths Optimization","authors":"Junpeng Zhang, X. Jia, Jiankun Hu, Kun Tan","doi":"10.1109/DICTA.2018.8615873","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615873","url":null,"abstract":"Tracking multiple vehicles in optical satellite videos can be achieved by detecting vehicles in individual frames and then link detections across frames. This is challenging since the low spatial resolution of satellite videos would lead to miss or partial detections. Failure in consistently detecting an identical vehicle would terminate a trajectory for loss of identity, or reinitialize a new trajectory for re-detection. Both would result in fragmented tracklets for an identical vehicle. In this paper, we propose a two-step global data association approach for multiple target tracking, which first generates local tracklets then merges them to global trajectories. In order to handle the frequent terminals of tracklets caused by the miss or partial detections, the spatial grid flow model has been extended to temporal neighboring grids to cover the possible connectivities in a wider temporal neighboring, so that the detection association could match temporal-unlinked detections. In the tracklet association model, we customize a tracklet transition probability based on Kalman Filter to link tracklets with large temporal intervals. A near-optimal solution to the proposed two-step association problem is found by Bilevel K-shortest Paths Optimization. Experiments on a satellite video show that our approach improves both detection and tracking performance of moving vehicles.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114357518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Adversarial Context Aggregation Network for Low-Light Image Enhancement 用于弱光图像增强的对抗上下文聚合网络

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615848

Y. Shin, M. Sagong, Yoon-Jae Yeo, S. Ko

{"title":"Adversarial Context Aggregation Network for Low-Light Image Enhancement","authors":"Y. Shin, M. Sagong, Yoon-Jae Yeo, S. Ko","doi":"10.1109/DICTA.2018.8615848","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615848","url":null,"abstract":"Image captured in the low-light environments usually suffers from the low dynamic ranges and noise which degrade the quality of the image. Recently, convolutional neural network (CNN) has been employed for low-light image enhancement to simultaneously perform the brightness enhancement and noise removal. Although conventional CNN based techniques exhibit superior performance compared to traditional non-CNN based methods, they often produce the image with visual artifacts due to the small receptive field in their network. In order to cope with this problem, we propose an adversarial context aggregation network (ACA-net) for low-light image enhancement, which effectively aggregates the global context via full-resolution intermediate layers. In the proposed method, we first increase the brightness of a low-light image using the two different gamma correction functions and then feed the brightened images to CNN to obtain the enhanced image. To this end, we train ACA network using L1 pixel-wise reconstruction loss and adversarial loss which encourages the network to generate a natural image. Experimental results show that the proposed method achieves state-of-the-art results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).1","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Hyperspectral Image Segmentation of Retinal Vasculature, Optic Disc and Macula 视网膜血管、视盘和黄斑的高光谱图像分割

2018 Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2018-12-01 DOI: 10.1109/DICTA.2018.8615761

A. Garifullin, Peeter Koobi, Pasi Ylitepsa, Kati Adjers, M. Hauta-Kasari, H. Uusitalo, L. Lensu

引用次数: 7