摘要翻译：
红外图像的特征提取一直是一项具有挑战性的任务。因此，基于学习的方法可以在原始图像/补丁上工作，具有重要意义。我们提出了一种新的多任务扩展的稀疏表示分类(SRC)方法在单视图和多视图的设置。也就是说，测试样本可以是单个IR图像或来自不同视角的图像。当根据训练字典展开时，多视图场景中的系数矩阵允许一个稀疏结构，而传统的稀疏诱导度量（如$L_0$-行伪范数）不易捕捉到这种稀疏结构。为此，我们在系数矩阵上采用协同的尖峰先验和板条先验，可以捕获相当一般的稀疏结构。我们的工作包括联合参数和稀疏系数估计(JPCEM)，这减少了在分类前对先验参数的手工选择。在美国陆军夜视和电子传感器局提供的具有挑战性的中波红外图像(MWIR)ATR数据库上，通过与其他先进方法的比较，证实了JPCEM的实验优点。
---
英文标题：
《Collaborative Sparse Priors for Infrared Image Multi-view ATR》
---
作者：
Xuelu Li and Vishal Monga
---
最新提交年份：
2018
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--

---
英文摘要：
  Feature extraction from infrared (IR) images remains a challenging task. Learning based methods that can work on raw imagery/patches have therefore assumed significance. We propose a novel multi-task extension of the widely used sparse-representation-classification (SRC) method in both single and multi-view set-ups. That is, the test sample could be a single IR image or images from different views. When expanded in terms of a training dictionary, the coefficient matrix in a multi-view scenario admits a sparse structure that is not easily captured by traditional sparsity-inducing measures such as the $l_0$-row pseudo norm. To that end, we employ collaborative spike and slab priors on the coefficient matrix, which can capture fairly general sparse structures. Our work involves joint parameter and sparse coefficient estimation (JPCEM) which alleviates the need to handpick prior parameters before classification. The experimental merits of JPCEM are substantiated through comparisons with other state-of-art methods on a challenging mid-wave IR image (MWIR) ATR database made available by the US Army Night Vision and Electronic Sensors Directorate.
---
PDF链接：
https://arxiv.org/pdf/1803.0722