This paper focuses on video summarization of abnormal behavior for remote invigilation of online exams. While the last decade has seen a massive increase in e-learning and online courses offered at postsecondary institutions, preserving the integrity of online examinations still heavily relies on web video conference invigilation performed by a remote proctor. Live remote invigilation is limited in the number of students that can be handled at once, and manual post-exam review is labor intensive. We propose a novel computer vision-based video content analysis system for the automatic creation of video summaries of online exams to assist remote proctors in post-exam reviews. The proposed method models normal and abnormal student behavior patterns using head pose estimations and a semantically meaningful two-state hidden Markov model. Video summaries are created from detected sequences of abnormal behavior. Experimental results are promising and demonstrate the viability of the proposed approach, which could readily be expanded to generate real-time alerts for live remote invigilation.

This review paper offers a contemporary literature survey on symbol spotting in architectural drawing images. Research on isolated symbol recognition is quite mature; the same cannot be said for recognizing a symbol in context. One important challenge is the segmentation/recognition paradox: a system should segment symbols before recognizing them, but some kind of recognition may be necessary to obtain a correct segmentation. Research has thus been recently directed toward symbol spotting, a way of locating possible symbol instances without using full recognition methods. In this paper, we thoroughly review symbol spotting methods with a focus on architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved. While most existing methods perform well in terms of recall, their performance is rather poor in terms of precision and false positives. In light of the review, we also propose a simple yet effective symbol spotting method based on template matching and a novel clutter-tolerant cross-correlation function that achieves state-of-the-art results with high precision, high recall, and few false positives, able to cope with “real-life clutter” found in industry-standard architectural drawings.

Echosounders are used by fisheries and ocean observatories, but significant manual effort is required to classify species of interest within multifrequency echograms. This article investigates the use of modified U-Net convolutional neural networks for the pixel-level classification of biological and physical data in echogram images with accurate classification of herring and salmon schools, bubbles, and the sea surface. Data were collected on the coast of British Columbia, Canada, over two years using an Acoustic Zooplankton and Fish Profiler at four frequencies (67, 125, 200, 455 kHz). In addition, simulated data (water depth and solar elevation angle) provide spatial and temporal context to improve the quality of predictions. Redundancy is built into the model by using a tiling strategy during training and classification. During training, using a limited set of annotated data, translational augmentation encodes the U-Nets with robust features that enable applications for alternate deployment configurations (lower sampling rates or alternate water depths). To ensure broad applicability, these networks were trained to classify echograms with noise left intact. The best-performing model classifies herring, salmon, and bubble classes with $\rm {F_{1}}$ scores of 93.0%, 87.3%, and 86.5%, respectively. The results are accurate even when multiple classes are in close proximity, thus, retaining biological data that would otherwise be discarded due to surface bubble noise.

Scoliosis is a complex deformation of the spine requiring, in severe cases, a highly delicate and invasive surgical instrumentation operation to correct the spinal deformities. Available traditional tools for surgical training have major drawbacks for which virtual reality (VR) technologies and computer simulation can offer solutions. In this paper, we introduce a surgical simulator integrating a complex patient-specific biomechanical model into a VR immersive environment in a collaborative context, the first of its kind for scoliosis surgery training. We present the results for the fully collaborative AVE (audio visual environment) aspects of the simulator. Haptic forces are computed in the biomechanical model, but not yet available as a haptic feedback because of the high forces and torques characteristic of scoliosis surgery, requiring the use of a specifically designed haptic device (in progress). Transatlantic collaborative tests showed that, with our simulator, users on different continents can train collaboratively for scoliosis surgery and visualise the forces and the resulting correction. With the eventual addition of haptic devices, they will also be able to feel the forces remotely.

This letter addresses the texture classification problem through a pixel-based local binary pattern (LBP) statistics aggregation mechanism. Real-world texture images often present challenges for classification algorithms in terms of intra-class variability due, among others, to variable illumination. The LBP operator, a state-of-the-art texture descriptor, possesses key properties for tackling real-world texture images: discriminative power and invariance against monotonic gray level changes. We propose a novel texture classification approach that increases the robustness of LBP-based methods with respect to any type of intra-class variations. The method locally characterizes each pixel with an LBP code histogram and globally computes the label of a textured image by aggregating pixel labels through a voting process. Our approach can be in principle applied to any LBP version, as it focuses on how statistics are computed from LBP codes. We show that the proposed pixel-based approach improves upon traditional LBP block-based approaches in terms of classification accuracy by up to 5.1 p.p. on the public Outex database for the classic LBP with various neighborhoods as well as for various LBP extensions.