718 research outputs found

    Towards Laparoscopic Visual AI: Development of a Visual Guidance System for Laparoscopic Surgical Palpation

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    Currently, there are numerous obstacles to performing palpation during laparoscopic surgery. The laparoscopic interface does not allow access into a patient’s body anything other than the tools that are inserted through the trocars. Palpation is usually done with the surgeon’s hands to detect lumps and certain anomalies underneath the skin, muscle, or tissues. It can be useful technique for augmenting surgical decision-making during laparoscopic surgery, especially when discerning operations involving cancerous tumors. Previous research demonstrated the use of tactile sensors and mechanical sensors placed at the end-effectors for palpating laparoscopically. In this study, a visual guidance system is proposed for use during laparoscopic palpation, specifically engineered to be part of a motion-based laparoscopic palpation system. In particular, the YOLACT++ model is used to localize a target organ, the gall bladder, on a custom dataset of laparoscopic cholecystectomy. Our experiments showed an AP score of 90.10 for bounding boxes and 87.20 on masks. In terms of the speed performance, the model achieved a playback speed of approximately 20 fps, which translates to approximately 48 ms video latency. The palpation path guides are guidelines that are computer-generated within the identified organ, and they show potential in helping the surgeon implement the palpation more accurately. Overall, this study demonstrates the potential of deep learning-based real-time image processing models to complete our motion-based laparoscopic palpation system, and to realize the promising role of artificial intelligence in surgical decision-making. Visual presentation of our results can be seen on our project page: https://kerwincaballas.github.io/lap-palpation

    Co-interest Person Detection from Multiple Wearable Camera Videos

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    Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views. In this paper, we tackle a new problem of locating the co-interest person (CIP), i.e., the one who draws attention from most camera wearers, from temporally synchronized videos taken by multiple wearable cameras. Our basic idea is to exploit the motion patterns of people and use them to correlate the persons across different videos, instead of performing appearance-based matching as in traditional video co-segmentation/localization. This way, we can identify CIP even if a group of people with similar appearance are present in the view. More specifically, we detect a set of persons on each frame as the candidates of the CIP and then build a Conditional Random Field (CRF) model to select the one with consistent motion patterns in different videos and high spacial-temporal consistency in each video. We collect three sets of wearable-camera videos for testing the proposed algorithm. All the involved people have similar appearances in the collected videos and the experiments demonstrate the effectiveness of the proposed algorithm.Comment: ICCV 201

    A Review on Outlier/Anomaly Detection in Time Series Data

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    Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.KK/2019-00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

    Real-time Aerial Detection and Reasoning on Embedded-UAVs

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    We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade-off. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition for the emerging UAV applications of autonomous surveying and activity reporting. In particular, our pipeline architectures operate in a time-sensitive manner, have high accuracy in detecting pedestrians from various aerial orientations, use a novel attention map for multi-activities recognition, and jointly refine its detection with temporal information. Numerically, we demonstrate our model's accuracy and fast inference speed on embedded systems. We empirically deployed our prototype hardware with full live feeds in a real-world open-field environment.Comment: In TGR
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