E-commerce companies often provide marketing incentives such as price discount coupons to motivate new customers to make their first purchase. However, many customers make purchases only when coupons are distributed to them; they stop making purchases after using the coupons. Thus, for cost-effective marketing, it is desirable for companies to distribute marketing coupons to new customers that have the highest potential to make future purchases without continued coupon incentives. However, it is difficult for e-commerce companies to identify the new customers to be targeted within 30 hours of registration. In this study, we address this problem using uplift modeling for cost-effective marketing. Uplift modeling can be used to identify the time when there is a causal relationship between coupon distribution and future non-coupon purchases. The ability to identify these causal relationships can allow a company to distribute coupons to the most promising customers and improve its business. Several studies have explained the benefits of uplift modeling in real-world e-commerce businesses. In this study, we demonstrate the results of uplift modeling for coupon distribution in a real-world Customer-to-Customer (C-to-C) e-commerce platform. We show that uplift modeling decreases marketing costs by 39.0% with only a negligible reduction in the number of acquired customers who make non-coupon purchases. However, it is difficult for E-commerce companies to identify the new customers to be targeted within 30 hours of registration. In this study, we address this problem using uplift modeling for cost-effective marketing. Uplift modeling can be used to identify the time when there is a causal relationship between coupon distribution and future non-coupon purchases. The ability to identify these causal relationships can allow a company to distribute coupons to the most promising customers and improve its business. Several studies have explained the benefits of uplift modeling in real-world E-commerce businesses. In this study, we demonstrate the results of uplift modeling for coupon distribution in a real-world Customer-to-Customer (C-to-C) E-commerce platform. We show that uplift modeling decreases marketing costs by 38.6% with only a negligible reduction in the number of acquired customers who make non-coupon purchases.

Hyperspectral reconstruction from RGB imaging has recently achieved significant progress via sparse coding and deep learning. However, a largely ignored fact is that existing RGB cameras are tuned to mimic human trichromatic perception, thus their spectral responses are not necessarily optimal for hyperspectral reconstruction. In this paper, rather than use RGB spectral responses, we simultaneously learn optimized camera spectral response functions (to be implemented in hardware) and a mapping for spectral reconstruction by using an end-to-end network. Our core idea is that since camera spectral filters act in effect like the convolution layer, their response functions could be optimized by training standard neural networks. We propose two types of designed filters: a three-chip setup without spatial mosaicing and a single-chip setup with a Bayer-style 2x2 filter array. Numerical simulations verify the advantages of deeply learned spectral responses compared to existing RGB cameras. More interestingly, by considering physical restrictions in the design process, we are able to realize the deeply learned spectral response functions by using modern film filter production technologies, and thus construct data-inspired multispectral cameras for snapshot hyperspectral imaging.

Spectral analysis of natural scenes can provide much more detailed information about the scene than an ordinary RGB camera. The richer information provided by hyperspectral images has been beneficial to numerous applications, such as understanding natural environmental changes and classifying plants and soils in agriculture based on their spectral properties. In this paper, we present an efficient manifold learning based method for accurately reconstructing a hyperspectral image from a single RGB image captured by a commercial camera with known spectral response. By applying a nonlinear dimensionality reduction technique to a large set of natural spectra, we show that the spectra of natural scenes lie on an intrinsically low dimensional manifold. This allows us to map an RGB vector to its corresponding hyperspectral vector accurately via our proposed novel manifold-based reconstruction pipeline. Experiments using both synthesized RGB images using hyperspectral datasets and real world data demonstrate our method outperforms the state-of-the-art.

In this work, our goal is to communicate spatial knowledge in Japanese with autonomous robots. We first describe the data collection scheme. We then conduct a study to investigate how Japanese describe spatial relations and what relations they prefer. Based on the observations, we formalize the knowledge by ontologies. With the help of an inference mechanism, our knowledge base is able to store commonsense and discover inexplicit knowledge. We model 16 spatial relations using geometry information. At the language level, we present a natural language interface to execute commands and answer questions. The integrated robotic system unifies visual and spatial information in concert with parsing of semantic interpretation of sentences. Finally, we describe two tasks for validation.

Hyperspectral imaging is beneficial to many applications but most traditional methods do not consider fluorescent effects which are present in everyday items ranging from paper to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflection and fluorescence so proper separation of these components is necessary for analyzing them. In recent years, effective imaging methods have been proposed but most require capturing the scene under multiple illuminants. In this paper, we demonstrate efficient separation and recovery of reflectance and fluorescence emission spectra through the use of two high frequency illuminations in the spectral domain. With the obtained fluorescence emission spectra from our high frequency illuminants, we then describe how to estimate the fluorescence absorption spectrum of a material given its emission spectrum. In addition, we provide an in depth analysis of our method and also show that filters can be used in conjunction with standard light sources to generate the required high frequency illuminants. We also test our method under ambient light and demonstrate an application of our method to synthetic relighting of real scenes.

In recent years, fluorescence analysis of scenes has received attention in computer vision. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescent emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescent emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. However, hyperspectral images often times suffer from degradation due to the limited light, which introduces noise into the imaging process. In this paper, we propose an effective model for hyperspectral image (HSI) denoising that considers underlying characteristics of HSIs: sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space. We first exploit high correlation across spectra and non-local self-similarity over space in the noisy HSI to learn an adaptive spatial-spectral dictionary. Then, we employ the local and non-local sparsity of the HSI under the learned spatial-spectral dictionary to design an HSI denoising model, which can be effectively solved by an iterative numerical algorithm with parameters that are adaptively adjusted for different clusters and different noise levels. Experimental results on HSI denoising show that the proposed method can provide substantial improvements over the current state-of-the-art HSI denoising methods in terms of both objective metric and subjective visual quality.

The ability to remotely measure heart rate from videos without requiring any special setup is beneficial to many applications. In recent years, a number of papers on heart rate (HR) measurement from videos have been proposed. However, these methods typically require the human subject to be stationary and for the illumination to be controlled. For methods that do take into account motion and illumination changes, strong assumptions are still made about the environment (e.g. background can be used for illumination rectification). In this paper, we propose an HR measurement method that is robust to motion, illumination changes, and does not require use of an environment's background. We present conditions under which cardiac activity extraction from local regions of the face can be treated as a linear Blind Source Separation problem and propose a simple but robust algorithm for selecting good local regions. The independent HR estimates from multiple local regions are then combined in a majority voting scheme that robustly recovers the HR. We validate our algorithm on a large database of challenging videos.

This paper introduces a novel method to separate fluorescent and reflective components in the spectral domain. In contrast to existing methods, which require to capture two or more images under varying illuminations, we aim to achieve this separation task by using a single hyperspectral image. After identifying the critical hurdle in single-image component separation, we mathematically design the optimal illumination spectrum, which is shown to contain substantial high-frequency components in the frequency domain. This observation, in turn, leads us to recognize a key difference between reflectance and fluorescence in response to the frequency modulation effect of illumination, which fundamentally explains the feasibility of our method. On the practical side, we successfully find an off-the-shelf lamp as the light source, which is strong in irradiance intensity and cheap in cost. A fast linear separation algorithm is developed as well. Experiments using both synthetic data and real images have confirmed the validity of the selected illuminant and the accuracy of our separation algorithm.

In recent years, fluorescence analysis of scenes has received attention. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescence emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescence emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

Antony Lam1 http://research.nii.ac.jp/~antony Art Subpa-Asa2 [email protected] Imari Sato1 http://research.nii.ac.jp/~imarik Takahiro Okabe3 http://www.pluto.ai.kyutech.ac.jp/~okabe Yoichi Sato4 http://www.hci.iis.u-tokyo.ac.jp/~ysato 1 National Institute of Informatics Tokyo, Japan 2 The Stock Exchange of Thailand Bangkok, Thailand 3 Kyushu Institute of Technology Fukuoka, Japan 4 The University of Tokyo Tokyo, Japan

Hyper spectral reflectance data allows for highly accurate spectral relighting under arbitrary illumination, which is invaluable to applications ranging from archiving cultural e-heritage to consumer product design. Past methods for capturing the spectral reflectance of scenes has proven successful in relighting but they all share a common assumption. All the methods do not consider the effects of fluorescence despite fluorescence being found in many everyday objects. In this paper, we describe the very different ways that reflectance and fluorescence interact with illuminants and show the need to explicitly consider fluorescence in the relighting problem. We then propose a robust method based on well established theories of reflectance and fluorescence for imaging each of these components. Finally, we show that we can relight real scenes of reflective-fluorescent surfaces with much higher accuracy in comparison to only considering the reflective component.

Hyper spectral imaging is beneficial to many applications but current methods do not consider fluorescent effects which are present in everyday items ranging from paper, to clothing, to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflectance and fluorescence. So proper separation of these components is necessary for analyzing them. In this paper, we demonstrate efficient separation and recovery of reflective and fluorescent emission spectra through the use of high frequency illumination in the spectral domain. With the obtained fluorescent emission spectra from our high frequency illuminants, we then present to our knowledge, the first method for estimating the fluorescent absorption spectrum of a material given its emission spectrum. Conventional bispectral measurement of absorption and emission spectra needs to examine all combinations of incident and observed light wavelengths. In contrast, our method requires only two hyper spectral images. The effectiveness of our proposed methods are then evaluated through a combination of simulation and real experiments. We also demonstrate an application of our method to synthetic relighting of real scenes.

Face recognition in the presence of pose changes remains a largely unsolved problem. Severe pose changes, resulting in dramatically different appearances, is one of the main difficulties. We present a support vector machine (SVM) based system that learns the relations between corresponding local regions of the face in different poses as well as a simple SVM based system for automatic alignment of faces in differing poses. We then present experimental results from multiple random splits of the CMU PIE Database to verify the strength of our approach.

Neural networks are used to predict the drape coefficient (DC) and circularity (CTR) of many different kinds of fabrics. The neural network models used were the multilayer perceptron using backpropagation (BP) and the radial basis function (RBF) neural network. The BP method was found to be more effective than the RBF method but the RBF method was the fastest when it came to training. Comparisons of the two models as well as comparisons of the same models using different parameters are presented. It was also found that prediction for CIR was less accurate than for DC for both neural network architectures.