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Found 10 papers, 1 papers with code
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise
Thus, we were motivated to utilize the isotropy of the additive noise as a constraint on the objective function to enhance the fidelity of DDPMs.
Dual mode multispectral imaging system for food and agricultural product quality estimation
An innovative merged mode is introduced in which both reflectance and transmittance information of a specimen are combined to form a higher dimensional dataset with more features.
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness
In the Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we proposed using one-hot encoded abundances as the pseudo-ground truth to guide the UN; computed using the k-means algorithm to exclude the use of prior HU methods.
Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations
The system strives to holistically capture and interpret the information content of CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors.
Assessment of Fetal and Maternal Well-Being During Pregnancy Using Passive Wearable Inertial Sensor
Assessing the health of both the fetus and mother is vital in preventing and identifying possible complications in pregnancy.
A generalized forecasting solution to enable future insights of COVID-19 at sub-national level resolutions
In this study, a forecasting solution is proposed, to predict daily new cases of COVID-19 in regions small enough where containment measures could be locally implemented, by targeting three main shortcomings that exist in literature; the unreliability of existing data caused by inconsistent testing patterns in smaller regions, weak deploy-ability of forecasting models towards predicting cases in previously unseen regions, and model training biases caused by the imbalanced nature of data in COVID-19 epi-curves.
An Optical physics inspired CNN approach for intrinsic image decomposition
There is a lack of unsupervised learning approaches for decomposing an image into reflectance and shading using a single image.
Comprehensive Study on Denoising of Medical Images Utilizing Neural Network Based Auto-Encoder
Fetal motion discernment utilizing spectral images extracted from accelerometric data incident on pregnant mothers abdomen has gained substantial attention in the state-of-the-art research.
Convolutional Autoencoder for Blind Hyperspectral Image Unmixing
In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel into two fundamental representatives: endmembers and abundances.
A Retinex based GAN Pipeline to Utilize Paired and Unpaired Datasets for Enhancing Low Light Images
Low light image enhancement is an important challenge for the development of robust computer vision algorithms.
