Similarity quantification of bare footprint based on linear measurement and shape context contour combined score-based likelihood ratio evaluation

Bare footprints are a type of physical evidence that can be crucial for solving difficult cases. To assist with identification, we explored a similarity quantification method using 3062 bare footprints from 1391 volunteers. We used linear similarity to cover seven linear lengths and then calculated the contour similarity of the heel region, anterior margin, and toe region using the shape context. Discriminant analysis was applied to determine the weights of the linear and contour similarities. Linear similarity had a weight of 0.56 whereas contour similarity had a weight of 0.44. The similarity between the same source and non-matches bare footprints was significantly different, with a leave-one-out cross-validation accuracy of 98.8%. Using the constructed similarity model, we developed a score-based likelihood ratio model based on similarity scores. We applied this model to five representative test samples including different volunteers, six months apart bare footprints, dynamic walking and static bare footprints. This method eliminated the interference of motion states and allowed for accurate determination of the same source and non-matches test samples. Overall, we quantified the shape contour and established a similarity assessment system for bare footprints that can assist in evaluation and identification.

Analyzing temporal dynamics of cell deformation and intracellular movement with video feature aggregation

BACKGROUND

The research and analysis of cellular physiological properties has been an essential approach to studying some biological and biomedical problems. Temporal dynamics of cells therein are used as a quantifiable indicator of cellular response to extracellular cues and physiological stimuli.

METHODS

This work presents a novel image-based framework to profile and model the cell dynamics in live-cell videos. In the framework, the cell dynamics between frames are represented as frame-level features from cell deformation and intracellular movement. On the one hand, shape context is introduced to enhance the robustness of measuring the deformation of cellular contours. On the other hand, we employ Scale-Invariant Feature Transform (SIFT) flow to simultaneously construct the complementary movement field and appearance change field for the cytoplasmic streaming. Then, time series modeling is performed on these frame-level features. Specifically, temporal feature aggregation is applied to capture the video-wide temporal evolution of cell dynamics.

RESULTS

Our results demonstrate that the proposed cell dynamic features can effectively capture the cell dynamics in videos. They also prove that the Movement Field and Appearance Change Field Feature (MFAFF) can more precisely model the cytoplasmic streaming. Besides, temporal aggregation of cell dynamic features brings a substantial absolute increase of classification performance.

CONCLUSION

Experimental results demonstrate that the proposed framework outperforms competing mainstreaming approaches on the aforementioned datasets. Thus, our method has potential for cell dynamics analysis in videos.

An unsupervised learning approach for tracking mice in an enclosed area

BACKGROUND

In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis.

RESULTS

We present an unsupervised learning procedure that is basically built as a two-stage process to track mice in an enclosed area using shape matching and deformable segmentation models. The system is validated by comparing the tracking results with previously manually labeled landmarks in three setups with different environment, contrast and lighting conditions. Furthermore, we demonstrate that the system is able to automatically detect non-social and social behavior of interacting mice. The system demonstrates a high level of tracking accuracy and clearly outperforms the MiceProfiler, a recently proposed tracking software, which serves as benchmark for our experiments.

CONCLUSIONS

The proposed method shows promising potential to automate behavioral screening of mice and other animals. Therefore, it could substantially increase the experimental throughput in behavioral assessment automation.

Shape matching algorithm to validate the tracing protocol of placental chorionic surface vessel networks

Variability in placental chorionic surface vessel networks (PCSVNs) may mark developmental and functional changes in fetal health. Here we report a protocol of manually tracing PCSVNs from digital 2D images of post-delivery placentas and its validation by a shape matching method to compare the similarity between paint-injected and unmanipulated (uninjected and deflated vessels) tracings of PCSVNs. We show that tracings of unmanipulated vessels produce networks that are very comparable to the networks obtained by tracing paint-injected PCSVNs. We suggest that manual tracings of unmanipulated PCSVNs can extract features of PCSVN growth and structure that may impact fetal wellbeing.

Contour based object detection using part bundles

In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into part bundles, where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects.