Perception-motivated interpolation of image sequences

Stich, Timo; Linz, Christian; Wallraven, Christian; Cunningham, Douglas W.; Magnor, Marcus

doi:10.1145/1870076.1870079

Cited by 41 publications

(22 citation statements)

References 54 publications

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“…Although physically correct image interpolation (e.g., between day-7 and day-10i) is infeasible, sophisticated image interpolation methods, as demonstrated by Stich et al [ 30 ], are available to create high-quality, convincing model images that represent unobserved transitions between recorded images of the same object. The criterion for an acceptable interpolation used by Stich et al, is qualitative: the interpolated images are perceived as visually correct by human observers.…”

Section: Methodsmentioning

confidence: 99%

Simulation enabled search for explanatory mechanisms of the fracture healing process

et al. 2018

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A significant portion of bone fractures fail to heal properly, increasing healthcare costs. Advances in fracture management have slowed because translation barriers have limited generation of mechanism-based explanations for the healing process. When uncertainties are numerous, analogical modeling can be an effective strategy for developing plausible explanations of complex phenomena. We demonstrate the feasibility of engineering analogical models in software to facilitate discovery of biomimetic explanations for how fracture healing may progress. Concrete analogical models—Callus Analogs—were created using the MASON simulation toolkit. We designated a Target Region initial state within a characteristic tissue section of mouse tibia fracture at day-7 and posited a corresponding day-10 Target Region final state. The goal was to discover a coarse-grain analog mechanism that would enable the discretized initial state to transform itself into the corresponding Target Region final state, thereby providing an alternative way to study the healing process. One of nine quasi-autonomous Tissue Unit types is assigned to each grid space, which maps to an 80×80 μm region of the tissue section. All Tissue Units have an opportunity each time step to act based on individualized logic, probabilities, and information about adjacent neighbors. Action causes transition from one Tissue Unit type to another, and simulation through several thousand time steps generates a coarse-grain analog—a theory—of the healing process. We prespecified a minimum measure of success: simulated and actual Target Region states achieve ≥ 70% Similarity. We used an iterative refinement protocol to explore many combinations of Tissue Unit logic and action constraints. Workflows progressed through four stages of analog mechanisms. Similarities of 73–90% were achieved for Mechanisms 2–4. The range of Upper-Level similarities increased to 83–94% when we allowed for uncertainty about two Tissue Unit designations. We have demonstrated how Callus Analog experiments provide domain experts with a fresh medium and tools for thinking about and understanding the fracture healing process.

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Section: Methodsmentioning

confidence: 99%

Simulation enabled search for explanatory mechanisms of the fracture healing process

et al. 2018

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“…Although physically correct image interpolation (e.g., between day-7 and day-10i) is infeasible, sophisticated image interpolation methods, as demonstrated by Stich et al [29], are available to create high-quality, convincing model images that represent unobserved transitions between recorded images of the same object. The criterion for an acceptable interpolation used by Stich et al, is qualitative: the interpolated images are perceived as visually correct by human observers.…”

Section: Tissue Section Images and Their Interpolationmentioning

confidence: 99%

“…Our operating hypothesis is that information available in day-7 and day-10 tissue section images can be used to draw simplified inferences about unobserved transitions occurring during intervening days, analogous to the approach used by Stich et al in simulating unobserved transitions between recorded images of the same object [29].…”

Section: Callus Analog Mechanismsmentioning

confidence: 99%

Simulation Enabled Search for Explanatory Mechanisms of the Fracture Healing Process

Kennedy

Marmor

Marcucio

et al. 2017

Preprint

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A significant portion of bone fractures fail to heal properly, increasing healthcare costs. Advances in fracture management have slowed because translational barriers have limited generation of mechanism-based explanations for the healing process. When uncertainties are numerous, analogical modeling can be an effective strategy for developing plausible explanations of complex phenomena. We demonstrate the feasibility of engineering analogical models in software to provide plausible biomimetic explanations for how fracture healing may progress. Concrete analogical models -Callus Analogs -were created using the MASON simulation toolkit. We designated a Target Region initial state within a characteristic tissue section of mouse tibia fracture at day-7 and posited a corresponding day-10 Target Region final state. The goal was to discover a coarse-grain analog mechanism that would enable the discretized initial state to transform itself into the corresponding Target Region final state, thereby providing a new way to study the healing process. One of nine quasi-autonomous Tissue Unit types is assigned to each grid space, which maps to an 80x80 µm region of the tissue section. All Tissue Units have an opportunity each time step to act based on individualized logic, probabilities, and information about adjacent neighbors. Action causes transition from one Tissue Unit type to another, and simulation through several thousand time steps generates a coarse-grain analog -a theory -of the healing process. We prespecified a minimum measure of success: simulated and actual Target Region states achieve ≥ 70% Similarity. We used an iterative protocol to explore many combinations of Tissue Unit logic and action constraints. Workflows progressed through four stages of analog mechanisms. Similarities of 73-90% were achieved for Mechanisms 2-4. The range of Upper-Level similarities increased to 83-94% when we allowed for uncertainty about two Tissue Unit designations. We have demonstrated how Callus Analog experiments provide domain experts with a new medium and tools for thinking about and understanding the fracture healing process. Author summaryTranslational barriers have limited the generation of mechanism-based explanations of fracture healing processes. Those barriers help explain why, to date, biological therapeutics have had only a minor impact on fracture management. New approaches are needed, and we present one that is intended to overcome those barriers incrementally. We created virtual Callus Analogs to simulate how the histologic appearance of a mouse fracture callus may transition from day-7 to day-10. Callus Analogs use softwarebased model mechanisms. Simulation experiments enable challenging and improving those model mechanisms. During execution, model mechanism operation provides a coarse-grain explanation (a theory) of a four-day portion of the healing process. Simulated day-10 callus histologic images achieved 73-94% similarity to a corresponding day-10 fracture callus image, thus demonstrating feasibility....

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“…Frame interpolation plays a very important role in intelligent monitoring systems. This technology can not only increase the frame rate of surveillance video to meet the requirements of monitoring display devices [1] but also forecast the missing frames to obtain smooth surveillance video. The essence of frame interpolation is to predict intermediate frames by using two given frames in video sequences [2], and it can be roughly divided into two categories: the optical flow method and the block matching method.…”

Section: Introductionmentioning

confidence: 99%

Frame Interpolation Based on Visual Correspondence and Coherency Sensitive Hashing

Wang

2013

Mathematical Problems in Engineering

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The technology of frame interpolation can be applied in intelligent monitoring systems to improve the quality of surveillance video. In this paper, a region-guided frame interpolation algorithm is proposed by introducing two innovative improvements. On the one hand, a detection approach is presented based on visual correspondence for detecting the motion regions that correspond to attracted objects in video sequences, which can narrow the prediction range of interpolated frames. On the other hand, spatial and temporal mapping rules are proposed using coherency sensitive hashing, which can obtain more accurate predicted values of interpolated pixels. Experiments show that the proposed method can achieve encouraging performance in terms of visual quality and quantitative measures.

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Perception-motivated interpolation of image sequences

Cited by 41 publications

References 54 publications

Simulation enabled search for explanatory mechanisms of the fracture healing process

Simulation enabled search for explanatory mechanisms of the fracture healing process

Simulation Enabled Search for Explanatory Mechanisms of the Fracture Healing Process

Frame Interpolation Based on Visual Correspondence and Coherency Sensitive Hashing

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