2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)最新文献_第3页

REVERSIBLE COLOR-TO-GRAY MAPPING WITH RESISTANCE TO JPEG ENCODING 可逆的彩色到灰色映射与抵制jpeg编码

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470306

T. Horiuchi, Xu Wen, K. Hirai

引用次数: 1

Automatic Assessment of Hoarding Clutter from Images Using Convolutional Neural Networks 基于卷积神经网络的图像囤积杂波自动评估

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470375

M. Tezcan, J. Konrad, Jordana Muroff

{"title":"Automatic Assessment of Hoarding Clutter from Images Using Convolutional Neural Networks","authors":"M. Tezcan, J. Konrad, Jordana Muroff","doi":"10.1109/SSIAI.2018.8470375","DOIUrl":"https://doi.org/10.1109/SSIAI.2018.8470375","url":null,"abstract":"Hoarding is a mental and public health problem stemming from difficulty associated with discarding one’s possessions and resulting clutter. In the last decade, a visual method, called \"Clutter Image Rating\" (CIR), has been developed for the assessment of hoarding severity. It involves rating clutter in patient’s home on the CIR scale from 1 to 9 using a set of reference images. Such assessment, however, is time-consuming, subjective, and may be non-repeatable. In this paper, we propose a new automatic clutter assessment method from images, according to the CIR scale, based on deep learning. While, ideally, the goal is to perfectly classify clutter, trained professionals admit assigning CIR values within ±1. Therefore, we study two loss functions for our network: one that aims to precisely assign a CIR value and one that aims to do so within ±1. We also propose a weighted combination of these loss functions that, as a byproduct, allows us to control the CIR mean absolute error (MAE). On a recently-collected dataset, we achieved ±1 accuracy of 82% and MAE of 0.88, significantly outperforming our previous results of 60% and 1.58, respectively.","PeriodicalId":422209,"journal":{"name":"2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134043822","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: 1

Golden Number Sampling Applied to Compressive Sensing 黄金数抽样在压缩感知中的应用

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470345

F. B. D. Silva, R. V. Borries, C. Miosso

{"title":"Golden Number Sampling Applied to Compressive Sensing","authors":"F. B. D. Silva, R. V. Borries, C. Miosso","doi":"10.1109/SSIAI.2018.8470345","DOIUrl":"https://doi.org/10.1109/SSIAI.2018.8470345","url":null,"abstract":"In a common compressive sensing (CS) formulation, limited Discrete Fourier Transform samples of a signal allow someone to reconstruct it by using an optimization procedure provided that certain well-known conditions hold. However, the frequencies in the Discrete Fourier Transform correspond to equally spaced samples of the continuous frequency domain, and the other possible frequency distributions are not usually considered in compressive sensing. This paper presents an irregular sampling of the normalized frequencies of the Discrete Fourier Transform which converges to an equidistributed sequence. This is done by taking the sequence of the fractional parts of the successive multiples of the golden number. That sequence was considered in applications in computer graphics and in magnetic resonance imaging [1], [2]. We also show that sub-matrices of the Discrete Fourier Transform with frequencies corresponding to fractional parts of multiples of the golden number produce signal-to-error ratios almost as high as the equally spaced counterpart. In addition, we show that the proposed irregular sampling converges faster to a uniform distribution in the range (0, 1). Thus, it reduces the discrepancy of pairwise distances of consecutive elements in the frequency sampling.","PeriodicalId":422209,"journal":{"name":"2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121568819","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: 1

A New Hardware Architecture for the Ridge Regression Optical Flow Algorithm 一种新的脊回归光流算法硬件结构

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470370

Taylor Simons, Dah-Jye Lee

引用次数: 0

Complex Correntropy Induced Metric Applied to Compressive Sensing with Complex-Valued Data 复熵诱导度量在复值数据压缩感知中的应用

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470371

João P. F. Guimarães, A. I. R. Fontes, F. B. D. Silva, A. Martins, R. V. Borries

引用次数: 2

STRATEGIES FOR QUALITY-AWARE VIDEO CONTENT ANALYTICS 质量意识视频内容分析策略

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470354

A. Reibman

引用次数: 2

Fully Automatic Baseline Correction in Magnetic Resonance Spectroscopy 磁共振波谱的全自动基线校正

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470319

Omid Bazgir, S. Mitra, B. Nutter, E. Walden

引用次数: 1

Classification of Primary Cilia in Microscopy Images Using Convolutional Neural Random Forests 利用卷积神经随机森林对显微镜图像中的初级纤毛进行分类

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470320

Sundaresh Ram, Mohammed S. Majdi, Jeffrey J. Rodríguez, Yang Gao, H. Brooks

{"title":"Classification of Primary Cilia in Microscopy Images Using Convolutional Neural Random Forests","authors":"Sundaresh Ram, Mohammed S. Majdi, Jeffrey J. Rodríguez, Yang Gao, H. Brooks","doi":"10.1109/SSIAI.2018.8470320","DOIUrl":"https://doi.org/10.1109/SSIAI.2018.8470320","url":null,"abstract":"Accurate detection and classification of primary cilia in microscopy images is an essential and fundamental task for many biological studies including diagnosis of primary ciliary dyskinesia. Manual detection and classification of individual primary cilia by visual inspection is time consuming, and prone to induce subjective bias. However, automation of this process is challenging as well, due to clutter, bleed-through, imaging noise, and the similar characteristics of the non-cilia candidates present within the image. We propose a convolutional neural random forest classifier that combines a convolutional neural network with random decision forests to classify the primary cilia in fluorescence microscopy images. We compare the performance of the proposed classifier with that of an unsupervised k-means classifier and a supervised multi-layer perceptron classifier on real data consisting of 8 representative cilia images, containing more than 2300 primary cilia using precision/recall rates, ROC curves, AUC, and Fβ-score for classification accuracy. Results show that our proposed classifier achieves better classification accuracy.","PeriodicalId":422209,"journal":{"name":"2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158399","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: 6

Underwater Image Restoration using Deep Networks to Estimate Background Light and Scene Depth 利用深度网络估计背景光和场景深度的水下图像恢复

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470347

Keming Cao, Yan-Tsung Peng, P. Cosman

引用次数: 41

Thermal Image Enhancement Algorithm Using Local And Global Logarithmic Transform Histogram Matching With Spatial Equalization 基于局部和全局对数变换直方图匹配和空间均衡的热图像增强算法

2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) Pub Date : 2018-04-01 DOI: 10.1109/SSIAI.2018.8470344

V. Voronin, S. Tokareva, E. Semenishchev, S. Agaian

引用次数: 22