Titlebook: Computer Vision Systems; 14th International C Henrik I. Christensen,Peter Corke,Markus Vincze Conference proceedings 2023 The Editor(s) (if

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WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32constrained environments while preserving visual privacy. Despite the presence of numerous WiFi-enabled devices around us, few expose CSI to users, resulting in a lack of sensing hardware options. Recently, variants of the Espressif ESP32 have emerged as potential low-cost, easy-to-deploy solutions

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PseudoDepth-SLR: Generating Depth Data for Sign Language Recognitionth information from RGB data to boost performance and enable generalizability in scenarios where depth data is not available. For the depth generation, we rely on an approach that utilizes vision transformers as a backbone for depth prediction. We examine the effect of pseudo depth data on the perfo

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Slovo: Russian Sign Language Datasetring and hearing societies. In addition, the sign language in each country differs significantly, which obliges the creation of new data for each of them. This paper presents the Russian Sign Language (RSL) video dataset Slovo, produced using crowdsourcing platforms. The dataset contains 20,000 Full

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Non-contact Heart Rate Monitoring: A Comparative Study of Computer Vision and Radar Approachesg HR measurement into Driver Monitoring Systems (DMS), providing physiological measurements to help address long-existing road safety issues by minimising human error. In real-world driving scenarios, the HR must be measured using non-contact approaches that avoid distracting or restricting the driv

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CFAB: An Online Data Augmentation to Alleviate the Spuriousness of Classification on Medical Ultrasoriousness in CNNs usually occurs in building connections between the background of images and labels. Such spurious correlation limits the generalizability of CNNs in classification tasks. Changing backgrounds and foregrounds of original samples can reduce the spuriousness in natural image classific

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DeepLabV3+ Ensemble for Diagnosis of Cardiac Transplant Rejection regular invasive and time-consuming biopsies followed by histopathological analysis. Deep learning has the potential to significantly enhance speed and objectivity and introduce new information from the obtained sample to increase the chances of predicting rejection. Our study presents several deep

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SIFT-Guided Saliency-Based Augmentation for Weed Detection in Grassland Images: Fusing Classic Compuis paper investigates how the density of structural features can be used to assist the training process of a Deep-Learning-based object detector. SIFT keypoint density is used to create overlay masks to augment images, emphasizing low-density areas—typically corresponding to weed plants. Our method

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Key Point-Based Orientation Estimation of Strawberries for Robotic Fruit Pickingobotic harvester requires the precise location and orientation of the fruit to effectively plan the trajectory of the end effector. The current methods for estimating fruit orientation employ either complete 3D information registered from multiple views or rely on fully-supervised learning technique