The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.

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It is an important task for a robot to bring objects requested by human via voice. In order to achieve the task, object recognition using speech and images is needed. Ozasa et al. has proposed the method for the object recognition by integrating speech and image information. Although this method requires both speech (word) and image models, the speech models are automatically constructed by combining phonemic acoustic models according to the dictionary. However, the image models have to be constructed manually in advance. In this paper, instead of the manual construction of the image models, we propose an automatic image model construction method for object recognition using Web images. The effectiveness of the proposed method is verified in the object recognition by integrating speech and image information.

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Despite the high recognition accuracy of recent deep neural networks, they can be easily deceived by spoofing. Spoofs (e.g., a printed photograph) visually resemble the actual objects quite closely. Thus, we propose a method for spoof detection with a hyperspectral image (HSI) that can effectively detect differences in surface materials. In contrast to existing anti-spoofing approaches, the proposed method learns the feature representation for spoof detection without spoof supervision. The informative pixels on an HSI are embedded onto the feature space, and we identify the spoof from their distribution. As this is the first attempt at unsupervised spoof detection with an HSI, a new dataset that includes spoofs, named Hyperspectral Spoof Dataset (HSSD), has been developed. The experimental results indicate that the proposed method performs significantly better than the baselines. The source code and the dataset are available on Github. 1