Conducting an orchestra had been a privilege reserved for professional conductors, until there aremusic conducting systems that can follow the user's lead. The music playing and beat tracking components of conduct...
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Deep focus states, like Immersion and Flow are important parameters when it comes to an enjoyable experience during learning activities. Exploring the Physiology of deep focus, in the course of prior studies, physiolo...
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This paper describes a new application of the technique known as Gradient Pattern Analysis (GPA), focused here on computervision. In the GPA domain, the image is translated into a tessellation triangulation field bas...
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The challenge is a fundamental aspect of almost every gameplay, and immersion is one of the most widely recognized concepts in the video game industry. Since this is currently a work in progress, this study aims to pr...
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To address the visualization problem of surface streamline on complex curved surfaces, we present and implement an efficient surface streamline generation method for visualizing vector field on arbitrary geometry. The...
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Face recognition technology uses Deep Convolutional Neural Network (DCNN) to extract biometric features for identity authentication. It has been used in various application domains, including military, law enforcement...
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Accurately determining salient regions of an image is challenging when labeled data is scarce. DINO-based self-supervised approaches have recently leveraged meaningful image semantics captured by patch-wise features f...
ISBN:
(纸本)9798350307184
Accurately determining salient regions of an image is challenging when labeled data is scarce. DINO-based self-supervised approaches have recently leveraged meaningful image semantics captured by patch-wise features for locating foreground objects. Recent methods have also incorporated intuitive priors and demonstrated value in unsupervised methods for object partitioning. In this paper, we propose SEMPART, which jointly infers coarse and fine bi-partitions over an image's DINO-based semantic graph. Furthermore, SEMPART preserves fine boundary details using graph-driven regularization and successfully distills the coarse mask semantics into the fine mask. Our salient object detection and single object localization findings suggest that SEMPART produces high-quality masks rapidly without additional post-processing and benefits from co-optimizing the coarse and fine branches.
Incorporating prior knowledge into a segmentation task, whether it be under the form of geometrical constraints (area/volume penalisation, convexity enforcement, etc.) or of topological constraints (to preserve the co...
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Incorporating prior knowledge into a segmentation task, whether it be under the form of geometrical constraints (area/volume penalisation, convexity enforcement, etc.) or of topological constraints (to preserve the contextual relations between objects, to monitor the number of connected components), proves to increase accuracy in medical image segmentation. In particular, it allows to compensate for the issue of weak boundary definition, of imbalanced classes, and to be more in line with anatomical consistency even though the data do not explicitly exhibit those features. This observation underpins the introduced contribution that aims, in a hybrid setting, to leverage the best of both worlds that variational methods and supervised deep learning approaches embody: (a) versatility and adaptability in the mathematical formulation of the problem to encode geometrical/topological constraints, (b) interpretability of the results for the former formalism, while (c) more efficient and effective processing models, (d) ability to become more proficient at learning intricate features and executing more computationally intensive tasks, for the latter one. To be more precise, a unified variational framework involving topological prescriptions in the training of convolutional neural networks through the design of a suitable penalty in the loss function is provided. These topological constraints are implicitly enforced by viewing the segmentation procedure as a registration task between the processed image and its associated ground truth under incompressibility conditions, thus making them homeomorphic. A very preliminary version (Lambert et al., in Calatroni, Donatelli, Morigi, Prato, Santacesaria (eds) Scale space and variational methods in computervision, Springer, Berlin, 2023, pp. 363-375) of this work has been published in the proceedings of the Ninth internationalconference on Scale Space and Variational Methods in computervision, 2023. It contained neither all the theo
As an important part of the mobile robot platform to perceive the external environment, the computer transmits the collected real-time images to the processing unit. After the image information is analyzed and process...
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