Robust 3D Geological Models: Hard Data is KeyFF Explore 3D
Understanding and incorporating 2D data, whether from surface field work or underground mine mapping, should always be the starting point of an integrated and coherent 3D geologic model, especially for areas with great geometric contrasts. Without this valuable data, 3D modelling is essentially performed with blinders on, and its absence results in a model that is too theory-driven, and lacks input from geologists and “real” field data.
Three-dimensional geologic models require complete, homogeneous and valid databases. The resulting 3D models are directly based on and rely on high-quality data. The data comprises both surface and underground observations. “Raw” or “hard” data should always be assigned more weight and act as rigid control points in 3D models. Hard data should always be distinguishable from interpreted data in 3D models. Investing the necessary time to learn how to homogenize and structure raw data in a rigorous way will be paid back during the 3D interpretation process.
Once 3D models are completed, they should be used as an exploration tool, populating their cells with user-chosen properties. Both quantitative and qualitative properties can be interpolated throughout the cells of the 3D model for further querying and questioning. Thus, the extra benefit of 3D map models is their use as dynamic interactive tools to help define new mineral exploration targets at depth.
A 3D map model is not a goal but a tool that should be dynamic, modified, questioned, shared and updated. Its future usefulness is determined by how well it can be utilized by a multi-disciplinary team of geologists, geophysicists, geochemists, engineers, metallurgists and environmental experts.
Collinearity Equations
Kinds of product that can be derived by the collinearity equation
- Space Resection By Collinearity
- Space Intersection By Collinearity
- Interior Orientation
- Relative Orientation
- Absolute Orientation
- Self-Calibration
Remote Exploration Technique. Dr. V. GalkineVadim Galkine
Evaluation of the accumulated permeability field of the uppermost crust using analogue modeling and lineament analysis combination.
Method results in building a series of Exploration Target Maps for further ground exploration. Unique on the market. Low cost, fast, effective. Base Metals, Gold, Silver, Uranium, Oil and gas, Kimberlites.
Robust 3D Geological Models: Hard Data is KeyFF Explore 3D
Understanding and incorporating 2D data, whether from surface field work or underground mine mapping, should always be the starting point of an integrated and coherent 3D geologic model, especially for areas with great geometric contrasts. Without this valuable data, 3D modelling is essentially performed with blinders on, and its absence results in a model that is too theory-driven, and lacks input from geologists and “real” field data.
Three-dimensional geologic models require complete, homogeneous and valid databases. The resulting 3D models are directly based on and rely on high-quality data. The data comprises both surface and underground observations. “Raw” or “hard” data should always be assigned more weight and act as rigid control points in 3D models. Hard data should always be distinguishable from interpreted data in 3D models. Investing the necessary time to learn how to homogenize and structure raw data in a rigorous way will be paid back during the 3D interpretation process.
Once 3D models are completed, they should be used as an exploration tool, populating their cells with user-chosen properties. Both quantitative and qualitative properties can be interpolated throughout the cells of the 3D model for further querying and questioning. Thus, the extra benefit of 3D map models is their use as dynamic interactive tools to help define new mineral exploration targets at depth.
A 3D map model is not a goal but a tool that should be dynamic, modified, questioned, shared and updated. Its future usefulness is determined by how well it can be utilized by a multi-disciplinary team of geologists, geophysicists, geochemists, engineers, metallurgists and environmental experts.
Collinearity Equations
Kinds of product that can be derived by the collinearity equation
- Space Resection By Collinearity
- Space Intersection By Collinearity
- Interior Orientation
- Relative Orientation
- Absolute Orientation
- Self-Calibration
Remote Exploration Technique. Dr. V. GalkineVadim Galkine
Evaluation of the accumulated permeability field of the uppermost crust using analogue modeling and lineament analysis combination.
Method results in building a series of Exploration Target Maps for further ground exploration. Unique on the market. Low cost, fast, effective. Base Metals, Gold, Silver, Uranium, Oil and gas, Kimberlites.
Photogrammetry - Space Resection by Collinearity EquationsAhmed Nassar
Space resection is commonly used to determine the exterior orientation parameters (which refers to position and orientation related to an exterior coordinate system) associated with one or more photos based on measurements of ground control points (GCPs). space resection is a nonlinear problem, existing methods involve linearization of the collinearity condition and the use of an iterative process to determine the final solution using the least-squares method. The process also requires initial approximate values of the unknown parameters, some of which must be estimated by another least-squares solution.
A frequently used class of objects are the quadric surfaces, which are described with second-degree equations (quadratics). They include spheres, ellipsoids, tori, paraboloids, and hyperboloids.
Quadric surfaces, particularly spheres and ellipsoids, are common elements of graphics scenes
Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.
Here in this presentation we will be dealing with Nurbs and the major difference between polygons and nurbs, modelling, technical specifications, basic control points, general equations of nurbs and nurb surfaces, nurb manpulating, knob removal
It provides topography modification options using 2d_zsh, 2d_zln and 2d_ztin for TUFLOW with respect to the DTM. It also discusses about various conditions while modifying topography.
a spline is a flexible strip used to produce a smooth curve through a designated set of points.
Polynomial sections are fitted so that the curve passes through each control point, Resulting curve is said to interpolate the set of control points.
Image segmentation is based on three principal concepts
Detection of discontinuities.
Thresholding
Region Processing
Morphological Watershed Image Segmentation embodies many of the concepts of above three approaches
Photogrammetry - Space Resection by Collinearity EquationsAhmed Nassar
Space resection is commonly used to determine the exterior orientation parameters (which refers to position and orientation related to an exterior coordinate system) associated with one or more photos based on measurements of ground control points (GCPs). space resection is a nonlinear problem, existing methods involve linearization of the collinearity condition and the use of an iterative process to determine the final solution using the least-squares method. The process also requires initial approximate values of the unknown parameters, some of which must be estimated by another least-squares solution.
A frequently used class of objects are the quadric surfaces, which are described with second-degree equations (quadratics). They include spheres, ellipsoids, tori, paraboloids, and hyperboloids.
Quadric surfaces, particularly spheres and ellipsoids, are common elements of graphics scenes
Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.
Here in this presentation we will be dealing with Nurbs and the major difference between polygons and nurbs, modelling, technical specifications, basic control points, general equations of nurbs and nurb surfaces, nurb manpulating, knob removal
It provides topography modification options using 2d_zsh, 2d_zln and 2d_ztin for TUFLOW with respect to the DTM. It also discusses about various conditions while modifying topography.
a spline is a flexible strip used to produce a smooth curve through a designated set of points.
Polynomial sections are fitted so that the curve passes through each control point, Resulting curve is said to interpolate the set of control points.
Image segmentation is based on three principal concepts
Detection of discontinuities.
Thresholding
Region Processing
Morphological Watershed Image Segmentation embodies many of the concepts of above three approaches
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
Medical image segmentation is a very important field for the medical science. In medical images, edge detection is an important work for object recognition of the human organs such as brain, heart or kidney etc. and it is an essential pre-processing step in medical image segmentation.
Medical images such as CT, MRI or X-Ray visualizes the various information’s of internal organs which is very important for doctors diagnoses as well as medical teaching, learning and research.
It is a tough job to locate the internal organs if images contains noise or rough structure of human body organs.
Automatic Delineation of Grid based and Geo-Morphological Slope Units for Sus...Omar F. Althuwaynee
+ Introduction to mapping units theory and practice
+ How to Build, edit and run a Graphical modeler tool in QGIS?
+ How to run QGIS modeler to integrate thematic maps with training/ testing landslides data
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
Image segmentation is a computer vision task that involves dividing an image into multiple segments or regions, where each segment corresponds to a distinct object, region, or feature within the image. The goal of image segmentation is to simplify and analyze an image by partitioning it into meaningful and semantically relevant parts. This is a crucial step in various applications, including object recognition, medical imaging, autonomous driving, and more.
Key points about image segmentation:
Semantic Segmentation: This type of segmentation assigns each pixel in an image to a specific class, essentially labeling each pixel with the object or region it belongs to. It's commonly used for object detection and scene understanding.
Instance Segmentation: Here, individual instances of objects are separated and labeled separately. This is especially useful when multiple objects of the same class are present in the image.
Boundary Detection: Some segmentation methods focus on identifying the boundaries that separate different objects or regions in an image.
Methods: Image segmentation can be achieved through various techniques, including traditional methods like thresholding, clustering, and region growing, as well as more advanced techniques involving deep learning, such as using convolutional neural networks (CNNs) and fully convolutional networks (FCNs).
Challenges: Image segmentation can be challenging due to variations in lighting, color, texture, and object shape. Overlapping objects and unclear boundaries further complicate the task.
Applications: Image segmentation is used in diverse fields. For example, in medical imaging, it helps identify organs or abnormalities. In autonomous vehicles, it aids in identifying pedestrians, other vehicles, and obstacles.
Evaluation: Measuring the accuracy of segmentation methods can be complex. Metrics like Intersection over Union (IoU) and Dice coefficient are often used to compare segmented results to ground truth.
Data Annotation: Creating ground truth annotations for segmentation can be labor-intensive, as each pixel must be labeled. This has led to the development of datasets and tools to facilitate annotation.
Semantic Segmentation Networks: Deep learning architectures like U-Net, Mask R-CNN, and Deeplab have significantly improved the accuracy of image segmentation by effectively learning complex patterns and features.
Image segmentation plays a fundamental role in understanding and processing images, enabling computers to "see" and interpret visual information in ways that mimic human perception.
Image segmentation is a computer vision task that involves dividing an image into meaningful and distinct segments or regions. The goal is to partition an image into segments that represent different objects or areas of interest within the image. Image segmentation plays a crucial role in various applications, such as object detection, medical imaging, autonomous vehicles, and more.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
3. Topics:
Background Of Segmentation
Global Thresholding
Adaptive Thresholding
Basic Formulation
Region Growing
Region Splitting and Merging
Morphological Watershed
Motion Based Segmentation
4. Background Of Segmentation
The objective of segmentation is to partition an image into regions based on specific criteria.
Object point by threshold
Region Based Segmentation
Segmentation by Morphological Watersheds
5. Global Thresholding
Heuristic approach, and Automatic threshold selection
1. Select an initial estimate for T
2. Segment the image using T. Two group G1 and G2
3. Compute the average gray level values μ1 and μ2 corresponding to G1 and G2
4. Compute the new threshold T = ½(u1+ u2)
5. Repeat 2-4 until the difference in T in successive
iteration is smaller than predefined parameter T0
Gray Level Histogram
6. Adaptive Thresholding
Uneven illumination can transform a perfectly segment able histogram into a histogram that
cannot be partition effectively by a single global threshold.
To handle uneven illumination,
Divide the original image into sub-images
Use different threshold to segment each sub-image. Continue this process so that the illumination of
each sub-image is approximately uniform.
Threshold for each pixel depend on the point of the pixel in the sub-image.
7. Region Based Segmentation
(Basic Formulation)
Let R represent the entire image region and R1, R2,…Rn, are subregions
(a)
(b) Ri is a connected region, i = 1,2,………,n
(c) Ri∩ Rj = φ for all i and j, i ≠ j // φ Null set (regions are disjointed)
(d) P(Ri) = True, for i = 1,2,……..,n // P(Ri) logical predicate defined over the points in set Ri, properties
satisfied by the pixels in a segmented region.
(e) P(RiURj) = False for i ≠ j // Two regions are different in the sense of predicate P.// one condition is not
valid in other Regions
8. Region Growing
Group pixels to sub-regions into a larger regions based on predefined criteria.
Limitations:
Formulation of Stopping rule
When no more pixels satisfy the criteria growing region should be stopped.
do not consider the history of region growth – size and shape
9.
10.
11. Morphological Watershed
Find the watershed lines:
(a)Punch a hole in each regional minimum
(b)Flood the entire topography from below by letting water rise through the holes at a uniform
rate.
(c)When the rising water in distinct catchment basin is about to merge, build dams to prevent
catchment basin from merging
(d)Once fully flooded, only the top of the dams are remained.
(e)These dams (closed) boundaries corresponding to the divide lines of the watersheds.
13. Morphological Watershed
FE
HG
E. Result of Further
Flooding
F. Beginning of Merging of
Water from two catchment
basin
G. Longer Dams
H. Watershed (segmentation
lines)
14. Motion Based Segmentation
Background subtraction from each image frame: when camera and background is fixed
Mi=Xt-B (B=background image)
Subtraction two successive image frames: if camera or background or both is moving.
Mi=Xt-Xt+1
