Feature point (FP) detection is a fundamental step of many computer vision
tasks. However, FP detectors are usually designed for low dynamic range (LDR)
images. In scenes with extreme light conditions, LDR images present saturated
pixels, which degrade FP detection. On the other hand, high dynamic range (HDR)
images usually present no saturated pixels but FP detection algorithms do not
take advantage of all the information present in such images. FP detection
frequently relies on differential methods, which work well in LDR images.
However, in HDR images, the differential operation response in bright areas
overshadows the response in dark areas. As an alternative to standard FP
detection methods, this study proposes an FP detector based on a coefficient of
variation (CV) designed for HDR images. The CV operation adapts its response
based on the standard deviation of pixels inside a window, working well in both
dark and bright areas of HDR images. The proposed and standard detectors are
evaluated by measuring their repeatability rate (RR) and uniformity. Our
proposed detector shows better performance when compared to other standard
state-of-the-art detectors. In uniformity metric, our proposed detector
surpasses all the other algorithms. In other hand, when using the repeatability
rate metric, the proposed detector is worse than Harris for HDR and SURF
detectors.

本文提出一种基于高动态范围图像的变异系数的特征点检测算法，该算法在低光和高光条件下均表现良好，并且在一致性度量方面优于其他算法。

基于变异系数的 HDR 图像特征点检测

Feature point detection in HDR images based on coefficient of variation

Exploring deep convolutional neural networks of high efficiency and low
memory usage is very essential for a wide variety of machine learning tasks.
Most of existing approaches used to accelerate deep models by manipulating
parameters or filters without data, e.g., pruning and decomposition. In
contrast, we study this problem from a different perspective by respecting the
difference between data. An instance-wise feature pruning is developed by
identifying informative features for different instances. Specifically, by
investigating a feature decay regularization, we expect intermediate feature
maps of each instance in deep neural networks to be sparse while preserving the
overall network performance. During online inference, subtle features of input
images extracted by intermediate layers of a well-trained neural network can be
eliminated to accelerate the subsequent calculations. We further take
coefficient of variation as a measure to select the layers that are appropriate
for acceleration. Extensive experiments conducted on benchmark datasets and
networks demonstrate the effectiveness of the proposed method.

本篇研究探讨了从不同角度解决深度神经网络在机器学习任务中高效且低内存使用的问题，提出了基于实例的特征修剪方法，并使用变异系数作为度量标准进行层选择，实验结果表明该方法有效。