We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class.
These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials.
Beijing Institute of Artificial Intelligence（北京人工智能研究院）；Beijing University of Technology（北京工业大学）
3D action recognition is referred to as the classification of action sequences which consist of 3D skeleton joints. While many research work are devoted to 3D action recognition, it mainly suffers from three problems: highly complicated articulation, a great amount of noise, and a low implementation efficiency. To tackle all these problems, we propose a real-time 3D action recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first define the skeletonlet as a few combinations of joint offsets groupe…
Perceptual Hashing applied to Tor domains recognition
Universidad de León（莱昂大学）；INCIBE（西班牙国家网络安全研究所）
Tor暗网承载着不同类型的非法内容，这些内容受到网络安全机构的监控。然而，手动分类Tor内容可能会很慢并且容易出错。为了支持这一任务，我们引入了频率主导邻域结构（F-DNS），这是一种新的感知散列方法，用于通过截图自动分类域。首先，我们使用经过各种内容保留操作的图像来评估F-DNS。我们将它们与原始图像进行了比较，获得了比其他最先进方法更好的相关系数，特别是在旋转的情况下。然后，我们应用F-DNS使用Darknet Usage Service Images-2K（DUSI-2K）对Tor域名进行分类，DUSI-2K是一个包含活跃Tor服务域名截图的数据集。最后，我们测量了F-DNS对图像分类方法和最先进的哈希方法的性能。我们的建议在Tor图像中获得了98.75%的准确率，超过了所有其他方法。
Spatial-temporal local binary pattern (STLBP) has been widely used in dynamic texture recognition. STLBP often encounters the high-dimension problem as its dimension increases exponentially, so that STLBP could only utilize a small neighborhood. To tackle this problem, we propose a method for dynamic texture recognition using PDV hashing and dictionary learning on multi-scale volume local binary pattern (PHD-MVLBP). Instead of forming very high-dimensional LBP histogram features, it first uses hash functions to map the pixel difference vectors (PDVs) to binary vectors, then forms a dictionary …
LP$^{2}$DH: A Locality-Preserving Pixel-Difference Hashing Framework for Dynamic Texture Recognition
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；School of Electrical & Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
University of Warwick（华威大学）；Imperial College London（帝国理工学院）；Huawei（华为）；Aberystwyth University（阿伯里斯特威斯大学）
Near infrared (NIR) to Visible (VIS) face matching is challenging due to the significant domain gaps as well as a lack of sufficient data for cross-modality model training. To overcome this problem, we propose a novel method for paired NIR-VIS facial image generation. Specifically, we reconstruct 3D face shape and reflectance from a large 2D facial dataset and introduce a novel method of transforming the VIS reflectance to NIR reflectance. We then use a physically-based renderer to generate a vast, high-resolution and photorealistic dataset consisting of various poses and identities in the NIR…
Recently, inspired by Transformer, self-attention-based scene text recognition approaches have achieved outstanding performance. However, we find that the size of model expands rapidly with the lexicon increasing. Specifically, the number of parameters for softmax classification layer and output embedding layer are proportional to the vocabulary size. It hinders the development of a lightweight text recognition model especially applied for Chinese and multiple languages. Thus, we propose a lightweight scene text recognition model named Hamming OCR. In this model, a novel Hamming classifier, wh…
Material classification in natural settings is a challenge due to complex interplay of geometry, reflectance properties, and illumination. Previous work on material classification relies strongly on hand-engineered features of visual samples. In this work we use a Convolutional Neural Network (convnet) that learns descriptive features for the specific task of material recognition. Specifically, transfer learning from the task of object recognition is exploited to more effectively train good features for material classification. The approach of transfer learning using convnets yields significan…
Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. With basic alignment over several frames of a moving camera sequence, a distribution of intensities over multiple angles is obtained. It is well known from prior work that luminance histograms and the statistics of natural images provide a strong material recognition cue. We utilize per-pixel {\it angular luminance distributions} as a key feature in discriminating the material of the surface. The angle-space sampling in…
Rutgers University–New Brunswick（罗格斯大学新布朗斯维克分校）；Amazon AI, Amazon Web Services Inc（亚马逊人工智能，亚马逊网络服务公司）；Kyoto University（京都大学）
Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based representations based on internet-mined single-view images captured in the scene. We take a middle-ground approach for material recognition that takes advantage of both rich radiometric cues and flexible image capture. A key concept is differential angular imaging, where small angular variations in image capture enables angular-gradient features for an enhanced appearance representation that improves recognition. We build …
