We demonstrate the use of DeCAF and Improved Fisher Vector image features to
classify clothing texture. The issue of choosing clothes is a problem for the
blind every day. This work attempts to solve the issue with a finger-mounted
camera and state-of-the-art classification algorithms. To evaluate our
solution, we collected 520 close-up images across 29 pieces of clothing. We
contribute (1) the HCTD, an image dataset taken with a NanEyeGS camera, a
camera small enough to be mounted on the finger, and (2) evaluations of
state-of-the-art recognition algorithms applied to our dataset - achieving an
accuracy >95%. Throughout the paper, we will discuss previous work, evaluate
the current work, and finally, suggest the project's future direction.

使用 DeCAF 和改进的 Fisher Vector 图像特征来分类服装纹理。通过使用手指装配的相机和最先进的分类算法，解决盲人日常选择衣物的问题。通过一个能够装配在手指上的 NanEyeGS 相机拍摄的图像数据集 HCTD 以及应用于我们的数据集的最新识别算法评估，取得了超过 95％的准确率。在整篇论文中，我们将讨论先前的工作，评估当前的工作，并最后提出项目的未来方向。

HandSight: 利用戴在手指上的摄像头，使用 DeCAF 和改进的 Fisher Vectors 对衣物颜色和纹理进行分类

HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color  and Texture with a Finger-Mounted Camera

While researches on model-based blind single image super-resolution (SISR)
have achieved tremendous successes recently, most of them do not consider the
image degradation sufficiently. Firstly, they always assume image noise obeys
an independent and identically distributed (i.i.d.) Gaussian or Laplacian
distribution, which largely underestimates the complexity of real noise.
Secondly, previous commonly-used kernel priors (e.g., normalization, sparsity)
are not effective enough to guarantee a rational kernel solution, and thus
degenerates the performance of subsequent SISR task. To address the above
issues, this paper proposes a model-based blind SISR method under the
probabilistic framework, which elaborately models image degradation from the
perspectives of noise and blur kernel. Specifically, instead of the traditional
i.i.d. noise assumption, a patch-based non-i.i.d. noise model is proposed to
tackle the complicated real noise, expecting to increase the degrees of freedom
of the model for noise representation. As for the blur kernel, we novelly
construct a concise yet effective kernel generator, and plug it into the
proposed blind SISR method as an explicit kernel prior (EKP). To solve the
proposed model, a theoretically grounded Monte Carlo EM algorithm is
specifically designed. Comprehensive experiments demonstrate the superiority of
our method over current state-of-the-arts on synthetic and real datasets. The
source code is available at this https URL

本研究提出了一种基于概率框架的盲超分辨率图像重建方法，针对复杂的噪声和模糊核问题，该方法采用了非独立同分布的噪声模型和一种新颖的模糊核生成器来增加模型的自由度。经过综合实验，该方法在合成数据集和真实数据集上均优于当前最先进的技术。