hasemberlin.blogg.se - Nima vs spine2d vs spriter

Nima vs spine2d vs spriter manual#

I just want a simple 2D skeletal animation tool so that I can swap out images for the body parts and generate the spritesheets for new characters of any saved animation I have created. So, I am making my own tool with AppGameKit, and I know that AppGameKit supports Atlases and Spritesheets in PNG format. I decided to go with the latter, and didn't want to pay $70 for a simple tool for 2D skeletal animations. do I really need in-code bone control (2D Rag-doll?), or just a tool to develop 2D spritesheets. Ground truth values were used for comparison to evaluate pose accuracy, and the correct labelling rate and rate of vertebra/non-vertebra classification were evaluated.I can't say, because I was interested in using the 2D spines but didn't like the prices of the programs for the two spine formats that AppGameKit supports. Specific slices were sampled for 3D volume data and multiple-slice data. The HDM 3D spine model was manually built. HDM planar templates were constructed from lumbar and thoracic patches. The deep network of local appearance module was trained using randomly sampled planar patches. The initial HDM model was constructed from MR+CT image patches collected from different spine sections/views. Data collected covers from lumbar, thoracic, cervical, and the whole spine. Validation was performed on T1/T2 MR and CT modalities using a combined total of 140 MR and CT samples from three different datasets. Lumbar scans and whole spine MR and CT slices were tested to show generality of our method. Testing was conducted using 110 MR and CT sagittal slices from 90 MR-CT volumes, excluding training volumes.

Nima vs spine2d vs spriter manual#

The ground truth of the testing data and slice selection followed the standard radiology protocol of spine physician, and are in separated manual processes.

The SVM was trained for vertebra/non-vertebra classification, and a set of 1150 patches, sampled from a combined total of 10 volumes from MR and CT, were used to train the TDCN system.

Validation was performed on cross-modality MR-CT datasets containing a total of 150 volumes with varying pathologies. Existing vertebra recognitions, simplified as vertebrae detections, mainly focus on the identification of vertebra locations and labels, but cannot support further spine quantitative assessment. The large appearance variations in different image modalities/views and the high geometric distortions in spine shape make vertebra recognition a challenging task.

Vertebrae have complex shape compositions.

Reconstruction of the global spine geometry from limited CT/MR slices can be ill-posed and requires sophisticated learning algorithms. Except for the local pose and appearance problems, the global geometry of spine is often difficult to recover in some medical situations, i.e., spine deformity and scoliosis.

The vertebrae sizes and orientations are highly diverse in pathological data that regular detectors, such as appearance detectors are insufficient to match all vertebrae.

The vertebrae and intervertebral discs lack unique characteristic features and have highly repetitive appearances that automatic naming could fail and mismatching could happen easily.

Image contrast, resolution, and appearance, for the same spine structure, could be very different when exposed to MR/CT or T1/T2 weighted MR images resulting in difficulty with vertebrae detection.

High variability from multiple image modalities or from shape deformations of the vertebrae.