Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

Fig 12 - by Nisha Yadav (366131)

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…Here, we propose a solution to address the limitation of identifiable motor activities by using combined MIs (i.e., MIs involving 2 or more body parts at the same time). And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”

Images of mouse popliteal lymph node vascular structure derived using phase-contrast synchrotron micro-computed tomography (µCT) by Mohammad Jafarnejad (6852338)

“…The freeze-dried samples were scanned with high-resolution synchrotron tomography and the radiographs were reconstructed into stack images using a phase-retrieval algorithm. The images were pre-processed by removing the pipette tip image using a cone crop before intensity-based segmentation and manual artefact processing. …”

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.…”