Jörgen Ahlberg

Multiple targets in the same radar micro-Doppler spectrogram, such as an unmanned aerial vehicle surrounded by flocking birds, can confuse classification algorithms. Without knowing which of the targets to classify, the decision is ambiguous. We propose to inform the classifier which target to classify by encoding the detected target position as a separate channel. This instructs the convolutional neural network to pay attention to the selected target without removing context. We thereby enable the model to classify individual objects in multi-target spectrograms, paving the way for higher classification performance in complex environments. Different representations of the detection-guiding matrix are tested and the approach is compared to alternative approaches, such as centering and cropping and we show that it is superior in cases with multiple targets. The efficacy of the approach is demonstrated on synthetic multi-target spectrograms using multiple datasets.

Ren et al. recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.

Summary: We combine the near‐sensor image processing concept with address‐event representation leading to an intensity‐ranking image sensor (IRIS) and show the benefits of using this type of sensor for image classification. The functionality of IRIS is to output pixel coordinates (X and Y values) continuously as each pixel has collected a certain number of photons. Thus, the pixel outputs will be automatically intensity ranked. By keeping track of the timing of these events, it is possible to record the full dynamic range of the image. However, in many cases, this is not necessary—the intensity ranking in itself gives the needed information for the task at hand. This paper describes techniques for classification and proposes a particular variant (groves) that fits the IRIS architecture well as it can work on the intensity rankings only. Simulation results using the CIFAR‐10 dataset compare the results of the proposed method with the more conventional ferns technique. It is concluded that the simultaneous sensing and classification obtainable with the IRIS sensor yields both fast (shorter than full exposure time) and processing‐efficient classification.

Mammalian herbivores have important top‐down effects on ecological processes and landscapes by generating vegetation changes through grazing and trampling. For free‐ranging herbivores on large landscapes, trampling is an important ecological factor. However, whereas grazing is widely studied, low‐intensity trampling is rarely studied and quantified. The cold‐adapted northern tundra reindeer (Rangifer tarandus) is a wide‐ranging keystone herbivore in large open alpine and Arctic ecosystems. Reindeer may largely subsist on different species of slow‐growing ground lichens, particularly in winter. Lichen grows in dry, snow‐poor habitats with frost. Their varying elasticity makes them suitable for studying trampling. In replicated factorial experiments, high‐resolution 3D laser scanning was used to quantify lichen volume loss from trampling by a reindeer hoof. Losses were substantial, that is, about 0.3 dm³ per imprint in dry thick lichen, but depended on type of lichen mat and humidity. Immediate trampling volume loss was about twice as high in dry, compared to humid thin (2–3 cm), lichen mats and about three times as high in dry vs. humid thick (6–8 cm) lichen mats, There was no significant difference in volume loss between 100% and 50% wetted lichen. Regained volume with time was insignificant for dry lichen, whereas 50% humid lichen regained substantial volumes, and 100% humid lichen regained almost all lost volume, and mostly within 10–20 min. Reindeer trampling may have from near none to devastating effects on exposed lichen forage. During a normal week of foraging, daily moving 5 km across dry 6‐ to 8‐cm‐thick continuous lichen mats, one adult reindeer may trample a lichen volume corresponding to about a year's supply of lichen. However, the lichen humidity appears to be an important factor for trampling loss, in addition to the extent of reindeer movement.

This paper gives an introduction to some of the problems of modern camera surveillance, and how these problems are, or can be, addressed using visualization techniques. The paper is written from an engineering point of view, attempting to communicate visualization techniques invented in recent years to the non-engineer reader. Most of these techniques have the purpose of facilitating for the surveillance operator to recognize or detect relevant events (such as violence), while, in contrast, some have the purpose of hiding information in order to be less privacy-intrusive. Furthermore, there are also cameras and sensors that produce data that have no natural visible form, and methods for visualizing such data are discussed as well. Finally, in a concluding discussion an attempt is made to predict how the discussed methods and techniques will be used in the future.

We address the problem of reducing the number of false alarms among automatically detected leakages in district heating networks. The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps. First, we use a building segmentation scheme in order to remove detections on buildings. Second, we extract features from the detections and use a Random forest classifier on the remaining detections. We provide extensive experimental analysis on real-world data, showing that this post-processing step significantly improves the usefulness of the system.