Classification of Streetsigns Using Gaussian Process Latent Variable Models Since the rise of deep artificial neuronal nets, object detection and classification became an autonomous procedure, where both, feature extraction and feature processing (e.g.: classification) is done using an architecture based on artificial neurons. The shortcomings of deep neuronal nets are mainly based the black box models and the architecture of the networks, which cannot be estimated. Unknown behavior and overfitting is still an unsolved problem. Thus, humanmade parameters like the number of neurons or the definition of activation functions must be set. This work presents a nonparametric and nonlinear approach for image processing using latent variable models. We used Gaussian process latent variable models for street sign feature extraction, where a latent representation is estimated without prior knowledge such as class label. Based on the latent representation, we visualizes the features and use stateoftheart classifier for street sign classification. Our results proves, that our approach extracts useful features for classification. Our approach has still shortcomings, such as computational time, which are current areas of research.

Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition Onboard vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and realworld samples from known classes to perform synthetictoreal domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIATS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio     xe2x88xbc 1  4     for newknown classes; even for more challenging ratios such as     xe2x88xbc 4  1    , the results are also very positive.