Search for: All records

This work explores the use of a triplet neural net-work for assessing the similarity of paper textures in a collection of Henri Matisse’s lithographs. The available dataset contains digital photomicrographs of papers in the lithograph collection, consisting of four views: two raking light orientations and both sides of the paper. A triplet neural network is first trained to extract features sensitive to anisotropy, and subsequently used to ensure that all papers in the dataset are in the same orientation and side. Another triplet neural network is then used to extract the texture features that are used to assess paper texture similarity. These results can then be used by art conservators and historians to answer questions of art historical significance, such as artist intent. 

This paper considers the problem of tracking and predicting dynamical processes with model switching. The classical approach to this problem has been to use an interacting multiple model (IMM) which uses multiple Kalman filters and an auxiliary system to estimate the posterior probability of each model given the observations. More recently, data-driven approaches such as recurrent neural networks (RNNs) have been used for tracking and prediction in a variety of settings. An advantage of data-driven approaches like the RNN is that they can be trained to provide good performance even when the underlying dynamic models are unknown. This paper studies the use of temporal convolutional networks (TCNs) in this setting since TCNs are also data-driven but have certain structural advantages over RNNs. Numerical simulations demonstrate that a TCN matches or exceeds the performance of an IMM and other classical tracking methods in two specific settings with model switching: (i) a Gilbert-Elliott burst noise communication channel that switches between two different modes, each modeled as a linear system, and (ii) a maneuvering target tracking scenario where the target switches between a linear constant velocity mode and a nonlinear coordinated turn mode. In particular, the results show that the TCN tends to identify a mode switch as fast or faster than an IMM and that, in some cases, the TCN can perform almost as well as an omniscient Kalman filter with perfect knowledge of the current mode of the dynamical system. 

In the context of papers used in the graphic arts, including silver gelatin, inkjet, and wove papers, prior work has studied measures of texture similarity for purposes of classifying such papers. The majority of prior work has been based on classical image processing approaches such as Fourier, wavelet, and fractal analysis. In this work, recent advances in deep learning are used to develop a texture similarity approach for measuring paper texture similarity. Since the available datasets generally lack labels, the convolutional neural network is trained using triplet loss to minimize the feature distance of tiles from the same image while simultaneously maximizing the feature distance of tiles drawn from different images. The approach is tested on three paper texture image databases considered in prior works and the results suggest the proposed approach achieves state-of-the-art performance.