Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images ) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.

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This paper addresses the problem of feature extraction for estimating users' transportation modes from their movement trajectories. Previous studies have adopted supervised learning approaches and used engineers' skills to find effective features for accurate estimation. However, such handcrafted features cannot always work well because human behaviors are diverse and trajectories include noise due to measurement error. To compensate for the shortcomings of handcrafted features, we propose a method that automatically extracts additional features using a deep neural network (DNN). In order that a DNN can easily handle input trajectories, our method converts a raw trajectory data structure into an image data structure while maintaining effective spatiotemporal information. A classification model is constructed in a supervised manner using both of the deep features and handcrafted features. We demonstrate the effectiveness of the proposed method through several experiments using two real datasets, such as accuracy comparisons with previous methods and feature visualization.

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