On Formal Models of Interactions Between Detectors and Trackers in Crowd Analysis Tasks

Sluzek, Andrzej; Zitouni, M. Sami

doi:10.1007/978-3-031-22025-8_2

Cited by 1 publication

(4 citation statements)

References 24 publications

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“…3. The same monochrome image colorized (by the method discussed in [17], [18]) with different rgb2gray models adopted.…”

Section: A Significance Of Decolorization Modelsmentioning

confidence: 99%

“…In general, for smaller differences, the sub-range is narrower and shifted to the left of Cmod, while for larger differences, it is wider and shifted towards the end of Cmod). Detailed description of this step can be found in [18].…”

Section: B Colorization Of Individual Pixelsmentioning

confidence: 99%

“…move a random pixel from Q to the front of Q; if ps (south neighbor of p) exists and is uncolored then Despite the heavy presence of randomizing factors, the results produced by the outlined method are surprisingly repeatable, depending only (as expected) on the adopted rgb2gray model. Actually, all images shown in Figs 1 and 3 are generated by the method, and many more examples can be found in [17], [18].…”

Section: Incremental Colorizationmentioning

confidence: 99%

“…In our recent papers [17], [18], we overviewed the results obtained for unguided colorization of monochrome images, and the method for colorizing videos is a natural extension of those results. Therefore, Section II revisits a number of assumptions and techniques adopted in monochrome image colorization and repeated in video colorization.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Colorization of Digital Movies using Decolorization Models and SSIM Index

Śluzek,

Dudziński,

Świsłocki

2023

Annals of Computer Science and Information Systems

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Re-colorization of images or movies is a challenging problem due to the infinite RGB solutions for a monochrome object. In general, the process is assisted by humans, either by providing colorization hints or relevant training data for ML/AI algorithms. Our intention is to develop a mechanism for fully unguided (and with no training data used) colorization of movies. In other words, we aim to create acceptable colored counterparts of movies in domains where only monochrome visualizations physically exist (e.g. IR, UV, MRI, etc. data). Following our past approach to image colorization, the method assumes arbitrary rgb2gray models and utilizes a few probabilistic heuristics. Additionally, we maintain the temporal stability of colorization by locally using structural similarity (SSIM) between adjacent frames. The paper explains the details of the method, presents exemplary results and compares them to the state-of-the art solutions.NOTE: All figures are best viewed in color and high resolution.

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“…3. The same monochrome image colorized (by the method discussed in [17], [18]) with different rgb2gray models adopted.…”

Section: A Significance Of Decolorization Modelsmentioning

confidence: 99%

Section: B Colorization Of Individual Pixelsmentioning

confidence: 99%

Section: Incremental Colorizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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