The color levels are used to create a performance enhancing pre-lighting condition. Because it is very performance intensive to calculate lots of real-time lighting on the fly for every single sprite in every single cloud, a precalculation is done to enable simpler run-time calculations. The color levels mimic the fact that light tends to come from above the clouds (the sun) and travel through them. As this happens, the density of the cloud and the moisture particles deflect and diffuse the light, causing the bottoms of the clouds to appear much darker than the tops, especially for denser cloud coverage. There are 5 color levels that can be applied to the cloud upon export preparation. Each of these color levels has 3 possible settings. Here is a description of the color levels and export preparation process:
This tutorial shows you how to setup and simulate a street crumble using the RayFire Tracer tool as well as Thinking Particles and FumeFX. Learn how to prepare fast trace maps from texture reference and how to bring all this into TP and set up a crumble system that is stackable. Set up small procedural detail debris from crack edges as well as dynamically spawned particles for FumeFX to simulate dust upon breaking. The video is 84 minutes long in 1280720 WMV format and the download features the final max file as well as the trace map that was used.
thinking particles for 3ds max 2012 64 bit
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For devices with DirectX 11 or better class GPUs, where support for BC7 and BC6H formats is guaranteed to be available, the recommended choice of compression formats is:RGB textures - DXT1 at four bits/pixel.RGBA textures - BC7 (higher quality, slower to compress) or DXT5 (faster to compress), both at eight bits/pixel.HDR textures - BC6H at eight bits/pixel.If you need to support DirectX 10 class GPUs on PC (NVIDIA GPUs before 2010, AMD before 2009, Intel before 2012), then DXT5 instead of BC7 would be preferred, since these GPUs do not support BC7 nor BC6H.
For LDR RGB and RGBA textures, most modern Android GPUs that support OpenGL ES 3.1 or Vulkan also support ASTC format, including:Qualcomm GPUs since Adreno 4xx / Snapdragon 415 (2015), ARM GPUs since Mali T624 (2012), NVIDIA GPUs since Tegra K1 (2014), PowerVR GPUs since GX6250 (2014).
Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
Technological developments in the past two decades have greatly advanced the field of bioimaging and have enabled the investigation of dynamic processes in living cells at unprecedented spatial and temporal resolution. Examples include the study of cell membrane dynamics1, cytoskeletal filaments2, focal adhesions3, viral infection4, intracellular transport5, gene transcription6 and genome maintenance7. Apart from state-of-the-art light microscopy8,9 and fluorescent labeling10,11, a key technology in the quest for quantitative analysis of intracellular dynamic processes is particle tracking. Here, a 'particle' may be anything from a single molecule to a macromolecular complex, organelle, virus or microsphere12, and the task of detecting and following individual particles in a time series of images is often (somewhat confusingly) referred to as 'single-particle tracking'. As the number of particles may be very large (hundreds to thousands), requiring 'multiple-particle tracking'13,14,15, manual annotation of the image data is not feasible, and computer algorithms are needed to perform the task.
At present, dozens of software tools are available for particle tracking16. The image analysis methods on which they are based can generally be divided into two steps: (i) particle detection (the spatial aspect), in which spots that stand out from the background according to certain criteria are identified and their coordinates estimated in every frame of the image sequence, and (ii) particle linking (the temporal aspect), in which detected particles are connected from frame to frame using another set of criteria to form tracks. The two steps are commonly performed only once, but they may also be applied iteratively. For each of these steps, many methods have been devised over the years17,18,19,20,21,22, often originating from other areas of data analysis23,24. With so many methods currently known, the question arises as to what distinguishes them and how they perform relative to one another under different experimental conditions.
Though interesting, the cited studies were limited to either one aspect of the task (detection rather than tracking) or one application (tracking of viruses rather than a broader set of particles). Moreover, the methods were implemented by the same group who performed the evaluation rather than by the original inventors. Obtaining a more complete picture of performance by combining the results of independent studies is usually hampered by their being based on different data sets and different evaluation criteria. Such fundamental problems have been recognized in the field of medical image analysis for more than 5 years and have resulted in the organization of international competitions (see -challenge.org/). The rationale behind such competitions is that the most objective evaluation of methods is achieved by having research groups apply their own methods independently, on a commonly defined data set and using commonly defined evaluation criteria. The first study in this spirit to be organized in the field of bioimage analysis was the digital reconstruction of axonal and dendritic morphology (DIADEM) challenge30. For particle tracking, the organization of a competition was first advocated by Saxton12 and in an editorial31.
Here we present an objective comparison of particle tracking methods based on an open competition that we organized in 2012 (see ). By announcements made through various media (at conferences, on the Web and via mailing lists and targeted emails) over 2 months, research groups worldwide were invited to participate. Next, registered teams were given 1 month to prepare their methods using representative training data and corresponding ground truth provided on the website. After release of the actual competition data, without ground truth, the teams were given 3 weeks to submit tracking results to an independent evaluator (one member of the organizing team who was not a contestant and the only one to have the ground truth). Preliminary results were presented and discussed at a workshop organized at the 2012 IEEE International Symposium on Biomedical Imaging. All participating teams sent their software to the independent evaluator who verified the results and performed an objective measurement of the computation times needed by the competing methods. A full analysis of the results and a discussion of the practical conclusions of our study are presented in this paper.
A total of 14 teams (Table 1) took up the challenge and submitted tracking results. Together they used many different methods32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57 (Table 1 and Supplementary Note 1) based on well-known as well as newly developed concepts. Approaches to particle detection ranged from simple thresholding or local-maxima finding to morphological processing, linear filtering (in particular, Gaussian, Laplacian of Gaussian and difference of Gaussian), linear and nonlinear model fitting, and centroid estimation schemes. Most detection methods were based on a combination of two or more of these. Approaches to linking of detected particles ranged from simple nearest-neighbor to multiframe association, including multiple hypothesis tracking, dynamic programming and combinatorial schemes, with or without explicit use of motion models and state estimation (Kalman filtering). Each tracking method consisted of a specific combination of detection and linking approaches as deemed appropriate by the corresponding team, who also determined suitable parameter settings for their method (Supplementary Table 1). 2ff7e9595c
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