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Register a new userEdge Detection for Event Cameras using Intra-pixel-area Events
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Added by
Sangil Lee
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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In this work, we propose an edge detection algorithm by estimating a lifetime of an event produced from dynamic vision sensor (DVS), also known as event camera. The event camera, unlike traditional CMOS camera, generates sparse event data at a pixel whose log-intensity changes. Due to this characteristic, theoretically, there is only one or no event at the specific time, which makes it difficult to grasp the world captured by the camera at a particular moment. In this work, we present an algorithm that keeps the event alive until the corresponding event is generated in a nearby pixel so that the shape of an edge is preserved. Particularly, we consider a pixel area to fit a plane on Surface of Active Events (SAE) and call the point inside the pixel area closest to the plane as a intra-pixel-area event. These intra-pixel-area events help the fitting plane algorithm to estimate life time robustly and precisely. Our algorithm performs better in terms of sharpness and similarity metric than the accumulation of events over fixed counts or time intervals, when compared with the existing edge detection algorithms, both qualitatively and quantitatively.
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Autonomous inspection of powerlines with quadrotors is challenging. Flights require persistent perception to keep a close look at the lines. We propose a method that uses event cameras to robustly track powerlines. Event cameras are inherently robust to motion blur, have low latency, and high dynamic range. Such properties are advantageous for autonomous inspection of powerlines with drones, where fast motions and challenging illumination conditions are ordinary. Our method identifies lines in the stream of events by detecting planes in the spatio-temporal signal, and tracks them through time. The implementation runs onboard and is capable of detecting multiple distinct lines in real time with rates of up to $320$ thousand events per second. The performance is evaluated in real-world flights along a powerline. The tracker is able to persistently track the powerlines, with a mean lifetime of the line $10times$ longer than existing approaches.
In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a birds eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset.
The operation of solid-state qubits often relies on single-shot readout using a nanoelectronic charge sensor, and the detection of events in a noisy sensor signal is crucial for high fidelity readout of such qubits. The most common detection scheme, comparing the signal to a threshold value, is accurate at low noise levels but is not robust to low-frequency noise and signal drift. We describe an alternative method for identifying charge sensor events using wavelet edge detection. The technique is convenient to use and we show that, with realistic signals and a single tunable parameter, wavelet detection can outperform thresholding and is significantly more tolerant to 1/f and low-frequency noise.
Event cameras are biologically-inspired sensors that gather the temporal evolution of the scene. They capture pixel-wise brightness variations and output a corresponding stream of asynchronous events. Despite having multiple advantages with respect to traditional cameras, their use is partially prevented by the limited applicability of traditional data processing and vision algorithms. To this aim, we present a framework which exploits the output stream of event cameras to synthesize RGB frames, relying on an initial or a periodic set of color key-frames and the sequence of intermediate events. Differently from existing work, we propose a deep learning-based frame synthesis method, consisting of an adversarial architecture combined with a recurrent module. Qualitative results and quantitative per-pixel, perceptual, and semantic evaluation on four public datasets confirm the quality of the synthesized images.
Junctions reflect the important geometrical structure information of the image, and are of primary significance to applications such as image matching and motion analysis. Previous event-based feature extraction methods are mainly focused on corners, which mainly find their locations, however, ignoring the geometrical structure information like orientations and scales of edges. This paper adapts the frame-based a-contrario junction detector(ACJ) to event data, proposing the event-based a-contrario junction detector(e-ACJ), which yields junctions locations while giving the scales and orientations of their branches. The proposed method relies on an a-contrario model and can operate on asynchronous events directly without generating synthesized event frames. We evaluate the performance on public event datasets. The result shows our method successfully finds the orientations and scales of branches, while maintaining high accuracy in junctions location.
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