Search Results for author: Amogh Gudi

Found 7 papers, 0 papers with code

Proximally Sensitive Error for Anomaly Detection and Feature Learning

no code implementations1 Jun 2022 Amogh Gudi, Fritjof Büttner, Jan van Gemert

Mean squared error (MSE) is one of the most widely used metrics to expression differences between multi-dimensional entities, including images.

Anomaly Detection

Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation

no code implementations31 Dec 2020 Amogh Gudi, Marian Bittner, Jan van Gemert

We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability.

Benchmarking Heart Rate Variability

Efficiency in Real-time Webcam Gaze Tracking

no code implementations2 Sep 2020 Amogh Gudi, Xin Li, Jan van Gemert

To do so, we evaluate the computational speed/accuracy trade-off for the CNN and the calibration effort/accuracy trade-off for screen calibration.


Object-Extent Pooling for Weakly Supervised Single-Shot Localization

no code implementations19 Jul 2017 Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan van Gemert

To facilitate this, we propose a novel global pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for training this CAM-based network for object extent localisation with only weak image-level supervision.

Region Proposal Weakly-Supervised Object Localization

Human Pose Estimation in Space and Time using 3D CNN

no code implementations31 Aug 2016 Agne Grinciunaite, Amogh Gudi, Emrah Tasli, Marten den Uyl

This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles.

3D Human Pose Estimation

Recognizing Semantic Features in Faces using Deep Learning

no code implementations2 Dec 2015 Amogh Gudi

This thesis provides a study of the effects of various factors and hyper-parameters of deep neural networks in the process of determining an optimal network configuration for the task of semantic facial feature recognition.

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