论文标题
通过部分不可能的编码器成本函数的照明归一化
Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function
论文作者
论文摘要
基于计算机视觉系统的一生中记录的图像经历了各种照明和环境条件，影响了先前训练的机器学习模型的可靠性。图像归一化是一种有价值的预处理组件，以增强模型的鲁棒性。为此，我们介绍了一种新的策略，以平均分发编码编号的成本函数来平均输入图像中所有不重要的信息（例如，环境特征和照明更改），以重点介绍重建显着特征（例如，类实例）。我们的方法利用了在不同的照明和环境条件下的相同风景的可用性，我们制定了部分不可能的重建目标：输入图像将无法传达足够的信息来完整地重建目标。它的适用性在三个公开可用的数据集上进行了评估。我们将三胞胎损失与潜在空间表示形式和最近的邻居搜索相结合，以改善从看不见的照明和班级实例的概括。在汽车应用程序上突出显示了上述后处理的重要性。为此，我们从三个不同的乘客隔间中释放一个综合数据集，其中每种风景在十种不同的照明和环境条件下呈现：请参阅https：//sviro.kl.dfki.de
Images recorded during the lifetime of computer vision based systems undergo a wide range of illumination and environmental conditions affecting the reliability of previously trained machine learning models. Image normalization is hence a valuable preprocessing component to enhance the models' robustness. To this end, we introduce a new strategy for the cost function formulation of encoder-decoder networks to average out all the unimportant information in the input images (e.g. environmental features and illumination changes) to focus on the reconstruction of the salient features (e.g. class instances). Our method exploits the availability of identical sceneries under different illumination and environmental conditions for which we formulate a partially impossible reconstruction target: the input image will not convey enough information to reconstruct the target in its entirety. Its applicability is assessed on three publicly available datasets. We combine the triplet loss as a regularizer in the latent space representation and a nearest neighbour search to improve the generalization to unseen illuminations and class instances. The importance of the aforementioned post-processing is highlighted on an automotive application. To this end, we release a synthetic dataset of sceneries from three different passenger compartments where each scenery is rendered under ten different illumination and environmental conditions: see https://sviro.kl.dfki.de
