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In this paper, we propose a new technique for automatically generating pencil drawing from 2D images using line integral convolution. Our idea is inspired by the similarity between the stroke textures of pencil drawings and the flow textures generated with line integral convolution. We succeeded in creating pencil drawings with their tone matching that of the original images simply by taking the vector field defining stroke directions and the white noise images generated by referring to the intensity of the original images as the input to the line integral convolution algorithm. By employing the texture analysis techniques, we also succeeded in automatically orienting strokes in the directions that convey the textures of objects appropriately. キーワード ノンフォトリアリスティック，絵画風画像生成，鉛筆画，LIC

This paper proposes an extension to the existing automatic pencil drawing generation technique based on Line Integral Convolution (LIC). The original LIC pencil filter utilizes image segmentation and texture direction detection techniques for defining outlines and stroke directions, and the quality of a resulting image depends largely on the result of image segmentation. It may fail to generate a reasonable result when the segmentation result is not consistent with the structure of the input image.To solve this problem, we propose in this paper to avoid the explicit region subdivision. Instead, we divide a source image into layers of successive intensity ranges, generate a stroke image for each layer, and add them together to obtain the final pencil drawing

New technologies are making it possible to provide an enriched view of interaction for researchers using multimodal information. This preliminary study explores the use of multiple information streams in usability evaluation. In the study, easy, medium and difficult versions of a game task were used to vary the levels of mental effort. Multimodal data streams during the three versions were analyzed, including eye tracking, pupil size, hand movement, heart rate variability (HRV) and subjectively reported data. Four findings indicate the potential value of usability evaluations based on multimodal information: First, subjective and physiological measures showed significant sensitivity to task difficulty. Second, different mental workload levels appeared to correlate with eye movement patterns, especially with a combined eye-hand movement measure. Third, HRV showed correlations with saccade speed. Finally, we present a new method using the ratio of eye fixations over mouse clicks to evaluate performance in more detail. These results warrant further investigations and take an initial step toward establishing usability evaluation methods based on multimodal information.

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