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A large number of works in egocentric vision have concentrated on action and object recognition. Detection and segmentation of hands in first-person videos, however, has less been explored. For many applications in this domain, it is necessary to accurately segment not only hands of the camera wearer but also the hands of others with whom he is interacting. Here, we take an in-depth look at the hand segmentation problem. In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets. We fine-tune RefineNet, a leading semantic segmentation method, for hand segmentation and find that it does much better than the best contenders. Existing hand segmentation datasets are collected in the laboratory settings. To overcome this limitation, we contribute by collecting two new datasets: a) EgoYouTube-Hands including egocentric videos containing hands in the wild, and b) HandOverFace to analyze the performance of our models in presence of similar appearance occlusions. We further explore whether conditional random fields can help refine generated hand segmentations. To demonstrate the benefit of accurate hand maps, we train a CNN for hand-based activity recognition and achieve higher accuracy when a CNN was trained using hand maps produced by the fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for fine-grained action recognition and show that an accuracy of 58.6% can be achieved by just looking at a single hand pose which is much better than the chance level (12.5%).

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We address the problem of detecting and localizing cells in time lapse microscopy images during early stage embryo development. Our approach is based on a linear chain Markov model that estimates the number and location of cells at each time step. The state space for each time step is derived from a randomized ellipse fitting algorithm that attempts to find individual cell candidates within the embryo. These cell candidates are combined into embryo hypotheses, and our algorithm finds the most likely sequence of hypotheses over all time steps. We restrict our attention to detect and localize up to four cells, which is sufficient for many important applications such as predicting blastocyst and can be used for assessing embryos in in vitro fertilization procedures. We evaluate our method on twelve sequences of developing embryos and find that we can reliably detect and localize cells up to the four cell stage.

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