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Two experiments were designed to investigate the effect of plate color and plate size on taste expectations, subjective ratings of, and willingness‐to‐pay for, Asian noodles and Italian pastas. Chinese participants viewed photographs of these foods served on plates of different colors and sizes, rated their liking, familiarity, taste expectations for the foods, and indicated how much they would be willing to pay for them. The foods were presented against the backdrop of store‐bought or computer‐edited colored plates. Presenting the food on white plates resulted in the highest familiarity scores. Interestingly, the participants were willing to pay approximately 16% more for the same quantity of Asian noodles when served on smaller (rather than larger) plates. Different patterns of results were observed with two types of Italian pasta that the Chinese participants were less familiar with, suggesting a moderating role of the familiarity people have with the foods. Practical applications The present study provides novel findings concerning the influence of plateware on Asian noodles, a commonly eaten food in many Asian countries. The findings suggest a fundamental difference between the role of plateware in the subjective ratings of, and taste expectations concerning, regularly consumed familiar and unfamiliar foods as in the present study and the snack food in previous studies. These findings are therefore relevant to those researchers and practitioners interested in how the receptacle, as an important contextual factor, influences consumers' perception and consumption of foods. These findings also have direct implications for those serving food in restaurants.

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other types of facial analysis. However, most of the existing works still focus on the 2D facial image which is quite sensitive to the lighting condition changes. In order to address this limitation, this paper proposed a coarse-to-fine method only based on the 3D facial scan data extracted from professional equipment to automatically and accurately estimate the landmark localization. Specifically, we firstly trained a convolutional neural network (CNN) to initialize the face landmarks instead of the mean shape. Then the proposed cascade regression networks learn the mapping function between 3D facial geometry feature and landmarks location. Tested on Bosphorus database, the experimental results demonstrated effectiveness and accuracy of the proposed method for [Formula: see text] landmarks. Compared with other methods, the results in several points demonstrate state-of-the-art performance.

When detecting the large objects by infrared thermal method, it is difficult to get a whole panoramic picture. So it needs to stitch some infrared thermography. Image mosaic includes 4 steps, feature detection, feature registration, image transformation and image fusion. This paper studies about an infrared thermograph mosaic algorithm based on the feature point detection and registration, and realizes it in MATLAB.

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