Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections.
We review the concept of presence in virtual reality, normally thought of as the sense of “being there” in the virtual world. We argued in a 2009 paper that presence consists of two orthogonal illusions that we refer to as Place Illusion (PI, the illusion of being in the place depicted by the VR) and Plausibility (Psi, the illusion that the virtual situations and events are really happening). Both are with the proviso that the participant in the virtual reality knows for sure that these are illusions. Presence (PI and Psi) together with the illusion of ownership over the virtual body that self-represents the participant, are the three key illusions of virtual reality. Copresence, togetherness with others in the virtual world, can be a consequence in the context of interaction between remotely located participants in the same shared virtual environments, or between participants and virtual humans. We then review several different methods of measuring presence: questionnaires, physiological and behavioural measures, breaks in presence, and a psychophysics method based on transitions between different system configurations. Presence is not the only way to assess the responses of people to virtual reality experiences, and we present methods that rely solely on participant preferences, including the use of sentiment analysis that allows participants to express their experience in their own words rather than be required to adopt the terminology and concepts of researchers. We discuss several open questions and controversies that exist in this field, providing an update to the 2009 paper, in particular with respect to models of Plausibility. We argue that Plausibility is the most interesting and complex illusion to understand and is worthy of significant more research. Regarding measurement we conclude that the ideal method would be a combination of a psychophysical method and qualitative methods including sentiment analysis.
In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground is modeled using a Gaussian Mixture Model with feature vectors consisting of the spatial (x, y) and colour (r, g, b) components. The spatial components of this model are updated using the Expectation Maximization algorithm after the classification of each frame. The background model is formulated in the 5 dimensional feature space in order to be able to apply a Maximum A Posteriori framework for the classification. The classification is done using a graph cut algorithm that allows taking into account neighborhood information. The results presented in the paper show the improvement of the system in situations where the foreground objects have similar colors to those of the background.
In this paper we present a real-time object tracking system for monocular video sequences with static camera. The work flow is based on a pixel-based foreground detection system followed by foreground object tracking. The foreground detection method performs the segmentation in three levels: Moving Foreground, Static Foreground and Background level. The tracking uses the foreground segmentation for identifying the tracked objects, but minimizes the reliance on the foreground segmentation, using a modified Mean Shift tracking algorithm. Combining this tracking system with the Multi-Level foreground segmentation, we have improved the tracking results using the classification in static or moving objects. The system solves successfully a high percentage of the moving objects occlusions, and most of the occlusions between static and moving objects.
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