Anomaly Detection in Textured Surfaces

Detecting anomalies in textured surfaces is an important and interesting problem that has practical applications in industrial defect detection and infrastructure asset management with a lot of potential financial benefits. The main challenges in this task are that the definition of anomaly changes from domain to domain, even noise can differ from the normal data but should not be classified as an anomaly, lack of labelled datasets and a limited number of anomalous instances. In this research, we have explored weak supervision and network-based transfer learning for anomaly detection. We developed a technique called AnoNet, which is a novel and compact fully convolutional network architecture capable of learning to detect the actual shape of anomalies not only from weakly labelled data but also from a limited number of examples. It uses a unique filter bank initialization technique that allows faster training. For a HxWx1 input image, it outputs a HxWx1 segmentation mask and also generalises to similar anomaly detection tasks. AnoNet on an average across four challenging datasets achieved an impressive F1 Score and AUROC value of 0.98 and 0.94 respectively. The second approach involved the use of network-based transfer learning for anomaly detection using pre-trained CNN architectures. In this investigation, fixed feature extraction and full network fine tuning approaches were explored. Results on four challenging datasets showed that the full network fine tuning based approach gave promising results with an average F1 Score and AUROC values of 0.89 and 0.98 respectively. While we have successfully explored and developed a method each for anomaly detection with weak supervision and supervision from a limited number of samples, research potential exists in semi-supervised and unsupervised anomaly detection

Learned versus Handcrafted Features for Person Re-identification

International audiencePerson re-identification is one of the indispensable elements for visual surveillance. It assigns consistent labeling for the same person within the field of view of the same camera or even across multiple cameras. While handcrafted feature extraction is certainly one way of approaching this problem, in many cases, these features are becoming more and more complex. Besides, training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. This paper explores the following three main strategies for solving the person re-identification problem: (i) using handcrafted features, (ii) using transfer learning based on a pre-trained deep CNN (trained for object categorization) and (iii) training a deep CNN from scratch. Our experiments consistently demonstrated that: (1) The handcrafted features may still have favorable characteristics and benefits especially in cases where the learning database is not sufficient to train a deep network. (2) A fully trained Siamese CNN outperforms handcrafted approaches and the combination of pre-trained CNN with different re-identification processes. (3) Moreover, our experiments demonstrated that pre-trained features and handcrafted features perform equally well. These experiments have also revealed the most discriminative parts in the human body