3 citations · 7 across the 6 of their papers we have counts for
6 papers
Identifying Factors to Help Improve Existing Decomposition-Based PMI Estimation Methods
Anna-Maria Nau, Phillip Ditto, Dawnie Wolfe Steadman +1
Accurately assessing the postmortem interval (PMI) is an important task in forensic science. Some of the existing techniques use regression models that use a decomposition score to…
Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach
Anna-Maria Nau, Phillip Ditto, Dawnie Wolfe Steadman +1
Determining the stage of decomposition (SOD) is crucial for estimating the postmortem interval and identifying human remains. Currently, labor-intensive manual scoring methods are…
SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images
Sara Mousavi, Zhenning Yang, Kelley Cross +2
Semantic segmentation is a challenging computer vision task demanding a significant amount of pixel-level annotated data. Producing such data is a time-consuming and costly process…
Pseudo Pixel-level Labeling for Images with Evolving Content
Sara Mousavi, Zhenning Yang, Kelley Cross +2
Annotating images for semantic segmentation requires intense manual labor and is a time-consuming and expensive task especially for domains with a scarcity of experts, such as Fore…
Collaborative Learning of Semi-Supervised Clustering and Classification for Labeling Uncurated Data
Sara Mousavi, Dylan Lee, Tatianna Griffin +2
Domain-specific image collections present potential value in various areas of science and business but are often not curated nor have any way to readily extract relevant content. T…
An Analytical Workflow for Clustering Forensic Images
Sara Mousavi, Dylan Lee, Tatianna Griffin +2
Large collections of images, if curated, drastically contribute to the quality of research in many domains. Unsupervised clustering is an intuitive, yet effective step towards cura…