论文标题
一种新型的照明条件，可从食物中提供公平可靠的消费者可接受性预测，多种图像数据集视觉数据集（FVD）
A novel illumination condition varied image dataset-Food Vision Dataset (FVD) for fair and reliable consumer acceptability predictions from food
论文作者
论文摘要
人工智能的最新进展促进了许多不同领域的广泛计算机视觉应用。充当人眼的数码相机可以感知基本对象属性，例如形状和颜色，并可以进一步用于执行高级任务，例如图像分类和对象检测。人类的看法已被广泛认为是训练和评估计算机视觉模型的基础真理。但是，在某些情况下，人类可能会被他们所看到的欺骗。功能齐全的人类视野依赖于稳定的外部照明，而不自然的照明会影响人类对商品基本特征的看法。为了评估对人类和计算机感知的照明影响，该小组提出了一种新颖的数据集，“食品视觉数据集”（FVD），以创建一个评估基准来量化照明效应，并推动照明估算方法的前进，以实现公平可靠的消费者的可接受性可接受性预测，从食物出现中。 FVD由675张图像组成，在3个不同的功率下捕获和5个不同的温度设置，每天替代五天。
Recent advances in artificial intelligence promote a wide range of computer vision applications in many different domains. Digital cameras, acting as human eyes, can perceive fundamental object properties, such as shapes and colors, and can be further used for conducting high-level tasks, such as image classification, and object detections. Human perceptions have been widely recognized as the ground truth for training and evaluating computer vision models. However, in some cases, humans can be deceived by what they have seen. Well-functioned human vision relies on stable external lighting while unnatural illumination would influence human perception of essential characteristics of goods. To evaluate the illumination effects on human and computer perceptions, the group presents a novel dataset, the Food Vision Dataset (FVD), to create an evaluation benchmark to quantify illumination effects, and to push forward developments of illumination estimation methods for fair and reliable consumer acceptability prediction from food appearances. FVD consists of 675 images captured under 3 different power and 5 different temperature settings every alternate day for five such days.
