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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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Blog Post number 1
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portfolio
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publications
Event transformer FlowNet for optical flow estimation
Published in 2022 British Machine Vision Conference, 2022
Event cameras are bioinspired sensors that produce asynchronous and sparse streams of events at image locations where intensity change is detected. They can detect fast motion with low latency, high dynamic range, and low power consumption. Over the past decade, efforts have been conducted in developing solutions with event cameras for robotics applications. In this work, we address their use for fast and robust computation of optical flow. We present ET-FlowNet, a hybrid RNN-ViT architecture for optical flow estimation. Visual transformers (ViTs) are ideal candidates for the learning of global context in visual tasks, and we argue that rigid body motion is a prime case for the use of ViTs since long-range dependencies in the image hold during rigid body motion. We perform end-to-end training with self-supervised learning method. Our results show comparable and in some cases exceeding performance with state-of-the-art coarse-to-fine event-based optical flow estimation. 
Recommended citation: Y. Tian and J. Andrade-Cetto. Event transformer FlowNet for optical flow estimation, 2022 British Machine Vision Conference, 2022, London. http://www.iri.upc.edu/files/scidoc/2645-Event-transformer-FlowNet-for-optical-flow-estimation.pdf
Egomotion from event-based SNN optical flow
Published in 2023 ACM International Conference on Neuromorphic Systems, 2023
We present a method for computing egomotion using event cameras with a pre-trained optical flow spiking neural network (SNN). To address the aperture problem encountered in the sparse and noisy normal flow of the initial SNN layers, our method includes a sliding-window bin-based pooling layer that computes a fused full flow estimate. To add robustness to noisy flow estimates, instead of computing the egomotion from vector averages, our method optimizes the intersection of constraints. The method also includes a RANSAC step to robustly deal with outlier flow estimates in the pooling layer. We validate our approach on both simulated and real scenes and compare our results favorably to the state-of-the-art methods. However, our method may be sensitive to datasets and motion speeds different from those used for training, limiting its generalizability. Results for translational motion on windmill dataset: 
Results for rotational motion on windmill dataset: 
Results for roational motion on natural scene dataset: 
Recommended citation: Y. Tian and J. Andrade-Cetto. Egomotion from event-based SNN optical flow, 2023 ACM International Conference on Neuromorphic Systems, 2023, Santa Fe, NM, USA, pp. 8:1-8. http://www.iri.upc.edu/files/scidoc/2747-Egomotion-from-event-based-SNN-optical-flow.pdf
talks
Spiking neural network for event camera ego-motion estimation
Published:
I was an invited speaker at NFM22 to give a talk about my research on SNN for event camera egomotion and optical flow.