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The paper compares aspect maps created from the same elevation data at different resolutions (30m and 30,720m) using two different GIS software programs, Idrisi and ArcGIS. It finds that at the higher 30m resolution, the mean difference between the aspect maps (MDA) is 25 degrees, but at the lower 30,720m resolution the MDA decreases to 3 degrees. Therefore, the aspect maps and resulting flow models produced by the two programs will show a larger difference when using high resolution data compared to low resolution data.
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![Vector Addition to Coarsen Pixels Resultant Vector (a+b+c+d) = ,[object Object],[object Object],[object Object],[object Object],[object Object],2 30 m 120 m 480 m 1920 m 30720 m 7680 m Scatter Plots : X Axis: ArcGIS Aspect Values Y Axis: Idrisi Aspect Values X ranges from 0 to 360 degrees & Y ranges from 0 to 360 degrees Applying Slope Weightage O n the left, the Idrisi pixel is facing West with a slope of 1 0 and the ArcGIS pixel is facing East with a slope of 2 0 , on the right, the Idrisi pixel is facing West with a slope of 45 0 and the ArcGIS pixel is facing East with a slope of 70 0 . The pixel values on the right will be more important than that to the left. Slope acts as magnitude in vector addition for coarsening the pixels. Circular Variable : More than one ways of reaching between A & B Aspect is a Circular Variable I f the value of a pixel in Idrisi map is 350 0 (A) and the value of a pixel in ArcGIS map is 10 0 (B) then the difference between the two pixels can be both 340 0 (Anticlockwise) and 20 0 (Clockwise). We used the least difference (20 0 ) : X n = Value of a pixel in ArcGIS aspect map Y n = Value of a pixel in Idrisi aspect map in D n = Least difference in angle between X n and Y n If X n – Y n <= 180 Then D n = X n – Y n Else D n = 360 – (X n – Y n ) EndIf Sin θ a + Sin θ b + Sin θ c + Sin θ d Tan -1 Cos θ a + Cos θ b + Cos θ c + Cos θ d](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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The document provides information about computer graphics output primitives like points and lines. It discusses how points and lines are displayed on different output devices like raster and vector systems. It also describes algorithms for drawing lines, including the Digital Differential Analyzer (DDA) algorithm and Bresenham's line drawing algorithm, which uses only incremental integer calculations to determine pixel positions along a line path.
EFFECTIVE INTEREST REGION ESTIMATION MODEL TO REPRESENT CORNERS FOR IMAGE
One of the most important steps to describe local features is to estimate the interest region around the feature location to achieve the invariance against different image transformation. The pixels inside the interest region are used to build the descriptor, to represent a feature. Estimating the interest region
around a corner location is a fundamental step to describe the corner feature. But the process is challenging under different image conditions. Most of the corner detectors derive appropriate scales to estimate the region to build descriptors. In our approach, we have proposed a new local maxima-based
interest region detection method. This region estimation method can be used to build descriptors to represent corners. We have performed a comparative analysis to match the feature points using recent corner detectors and the result shows that our method achieves better precision and recall results than
existing methods.
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
03.Scan Conversion.ppt
Commonly a fixed area of the system memory is reserved for the frame buffer,
Video controller can direct access to the frame-buffer
Frame-buffer locations, and the corresponding screen positions, are referenced in Cartesian coordinates.
Some system employ lower-left corner as origin
But most common system employ upper-left corner as origin.
Scan lines are labeled from ymax, at the top of the screen to 0 at the bottom.
Along each scan line, screen pixel positions are labeled from 0 to xmax
B. SC CSIT Computer Graphics Unit1.2 By Tekendra Nath Yogi
1. The document discusses raster graphics and algorithms for drawing basic 2D primitives like points, lines, circles, and polygons.
2. It describes two common line drawing algorithms - the Digital Differential Analyzer (DDA) algorithm and Bresenham's line algorithm.
3. The DDA algorithm draws lines by calculating pixel positions using the slope of the line, while Bresenham's algorithm uses only integer calculations to find the next pixel position along the line.
Bresenham circlesandpolygons
The document discusses algorithms for drawing lines and circles on a discrete pixel display. It begins by describing what characteristics an "ideal line" would have on such a display. It then introduces several algorithms for drawing lines, including the simple line algorithm, digital differential analyzer (DDA) algorithm, and Bresenham's line algorithm. The Bresenham algorithm is described in detail, as it uses only integer calculations. Next, a simple potential circle drawing algorithm is presented and its shortcomings discussed. Finally, the more accurate and efficient mid-point circle algorithm is introduced. This algorithm exploits the eight-way symmetry of circles and only calculates points in one octant.
Bresenham circles and polygons derication
The document discusses algorithms for drawing lines and circles on a discrete pixel display. It begins by describing what characteristics an "ideal line" would have on such a display. It then introduces several algorithms for drawing lines, including the simple line algorithm, digital differential analyzer (DDA) algorithm, and Bresenham's line algorithm. The Bresenham algorithm is described in detail, as it uses only integer calculations. Next, a simple potential circle drawing algorithm is presented and its shortcomings discussed. Finally, the more accurate and efficient mid-point circle algorithm is described. This algorithm exploits the eight-way symmetry of circles and uses incremental calculations to determine the next pixel point.
PART I.3 - Physical Mathematics
General Path Rules; Fluid Mechanics; Generalized Coordinates; Vector Calculus; The Line Integral; Vector Theorems; Kinematics
3-1_geo Spatial analysis_spatial_modeling.pptx
This document provides an overview of spatial analysis and modeling concepts and techniques. It discusses how spatial analysis can be used to analyze data by location, detect patterns, predict values, and identify relationships between geographic features. It also describes different types of surface models like digital surface models (DSM), digital elevation models (DEM), and digital terrain models (DTM). Finally, it explains common surface modeling operations that can be performed on raster data like contour, slope, aspect, hillshade and viewshed analysis as well as interpolation techniques like inverse distance weighting and kriging.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
This document discusses using the Radon transform to estimate scaling and rotation between two images of the same object. It first extracts features from both images using active contours and level sets. Control points are identified from the extracted features. Triangles are formed by connecting control points and Radon transforms are applied to determine scaling. Lines between control points are used with Radon transforms to determine rotation by finding the angle difference that produces maximum correlation. The method is demonstrated on images of a building that is scaled and rotated. Scaling of 1.8251 and rotation of 37 degrees are accurately estimated when features are well extracted.
Content Based Image Retrieval Using Gray Level Co-Occurance Matrix with SVD a...
In this paper, gray level co-occurrence matrix, gray level co-occurrence matrix with singular value
decomposition and local binary pattern are presented for content based image retrieval. Based upon the
feature vector parameters of energy, contrast, entropy and distance metrics such as Euclidean distance,
Canberra distance, Manhattan distance the retrieval efficiency, precision, and recall of the images are
calculated. The retrieval results of the proposed method are tested on Corel-1k database. The results after
being investigated shows a significant improvement in terms of average retrieval rate, average retrieval
precision and recall of different algorithms such as GLCM, GLCM & SVD, LBP with radius one and LBP
with radius two based on different distance metrics.
Algorithm
The document discusses various image processing techniques for detecting clouds in visible band camera images including:
1) Bilinear interpolation to demosaic Bayer pattern images into full-color images
2) Converting RGB images to HSI color space since clouds are detected well in red color
3) Applying thresholding techniques like median and standard deviation thresholds to differentiate clouds from other regions
4) Using histogram equalization to increase contrast between clouds and other objects like vegetation and water
5) Employing Otsu clustering segmentation to coarsely detect cloud and non-cloud regions by finding the optimal threshold that maximizes inter-class variance between two classes.
Computer graphics
Computer graphics deals with generating, manipulating, and displaying images using computers. It has revolutionized graphic design by moving the industry from physical tools like pasteboards to digital tools using computers and software. Now designers use computers and graphics software to do everything from page layouts to preparing documents for printing. Some key features of computer graphics include vector graphics which use lines and shapes, raster graphics which use pixels, and transformations which allow simulated spatial manipulation of objects.
Dense Visual Odometry Using Genetic Algorithm
Our work aims to estimate the camera motion mounted on the head of a mobile robot or a moving object from RGB-D images in a static scene. The problem of motion estimation is transformed into a nonlinear least squares function. Methods for solving such problems are iterative. Various classic methods gave an iterative solution by linearizing this function. We can also use the metaheuristic optimization method to solve this problem and improve results. In this paper, a new algorithm is developed for visual odometry using a sequence of RGB-D images. This algorithm is based on a genetic algorithm. The proposed iterative genetic algorithm searches using particles to estimate the optimal motion and then compares it to the traditional methods. To evaluate our method, we use the root mean square error to compare it with the based energy method and another metaheuristic method. We prove the efficiency of our innovative algorithm on a large set of images.
Scaling Transform Methods For Compressing a 2D Graphical image
This document summarizes a research paper that proposes using 2D scaling transformations to compress grayscale images. It begins by defining scaling and different types of scaling transformations. It then describes how to represent 2D scaling mathematically using transformation matrices. The paper applies 2D scaling with different factors to compress the Lena test image and evaluates the compressed images using PSNR and MSE metrics. Scaling by factors of 2, 4, and 8 are tested, with higher scaling factors achieving better compression but lower image quality. In conclusions, the paper finds the proposed 2D scaling technique provides comparable or better performance than other image transformation methods for compression.
Chapter 2 1
Vectors can be used to describe the position of a point in space using a coordinate system with an origin and axes. A vector quantity has both magnitude and direction, while a scalar only has magnitude. Vectors can be added graphically by drawing them tip to tail or algebraically using their components. A vector's components are its projections onto the x- and y-axes and are found using trigonometric functions of the vector's angle. The problem provides the displacements of a hiker over two days and uses vector addition to find the total displacement, expressing it in terms of its components and as a single vector in unit vector form.
Vf sift
The document presents a method called VF-SIFT that aims to speed up SIFT feature matching. VF-SIFT extends the standard SIFT feature by adding 4 pairwise independent angles computed from the SIFT descriptor. During feature extraction, SIFT features are clustered based on their angles and stored in a multidimensional table. This allows the matching process to only compare SIFT features from clusters where correct matches may be found, greatly increasing the speed of matching compared to exhaustive search. The method was tested on stereo images and standard datasets, and achieved a 1250x speedup in matching compared to exhaustive search without a noticeable loss in correct matches.
MAP MAKING FROM TABLES
This paper presents a geometric approach to the coordinatization of a measured space called the Map
Maker’s algorithm. The measured space is defined by a distance matrix for sites which are reordered and
mapped to points in a two-dimensional Euclidean space. The algorithm is tested on distance matrices
created from 2D random point sets and the resulting coordinatizations compared with the original point
sets for confirmation. Tolerance levels are set to deal with the cumulative numerical errors in the
processing of the algorithm. The final point sets are found to be the same apart from translations,
reflections and rotations as expected. The algorithm also serves as a method for projecting higher
dimensional data to 2D.
MAP MAKING FROM TABLES
This paper presents a geometric approach to the coordinatization of a measured space called the Map
Maker’s algorithm. The measured space is defined by a distance matrix for sites which are reordered and
mapped to points in a two-dimensional Euclidean space. The algorithm is tested on distance matrices
created from 2D random point sets and the resulting coordinatizations compared with the original point
sets for confirmation. Tolerance levels are set to deal with the cumulative numerical errors in the
processing of the algorithm. The final point sets are found to be the same apart from translations,
reflections and rotations as expected. The algorithm also serves as a method for projecting higher
dimensional data to 2D.
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EFFECTIVE INTEREST REGION ESTIMATION MODEL TO REPRESENT CORNERS FOR IMAGE
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A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUES
B. SC CSIT Computer Graphics Unit1.2 By Tekendra Nath Yogi
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IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
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Scaling Transform Methods For Compressing a 2D Graphical image
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More from Rahul Rakshit
holmes
The document summarizes a project to create a high-resolution land cover dataset for 26 towns in Massachusetts. The project goals are to create the land cover dataset, assess its accuracy using virtual globes, produce land cover summaries at various geographic levels, develop a classification tutorial, and automate processing. Key steps include object-oriented image classification, accuracy assessment using Google Earth, and data analysis of land cover percentages by town, census block and parcel.
Site Suitability Analysis
Massachusetts Biodiversity Consultants aims to protect half of developable land in central Massachusetts while designating areas for additional protection, new urban development, and residential land. They use multi-objective site suitability analysis to balance these goals based on factors like proximity to existing development and priority areas for biodiversity like wetlands. Their analysis found areas best suited for new protected, urban, and residential land uses, but noted issues around fragmentation of habitats that their model did not fully capture.
Comparing directional maps
This document summarizes research comparing aspect and slope data at different map resolutions. It describes using vector addition to coarsen high resolution topographic data and calculates the mean absolute error between the original and coarsened data. Thank you to Prof. Robert Pontius for advising this research on methods for comparing maps with different resolutions.
Post Accuracy Assessment Classification
This document presents a study with the following objectives:
1. Create a soft-classified map of residential lawns using accuracy assessment from sampled image data.
2. Incorporate supplemental variables to distinguish residential lawns from other grassy areas.
3. Use virtual fieldwork to validate the results.
Segmentation scale selection
This document presents a method for selecting an optimal scale for image segmentation. It compares various segmentation algorithms like watershed and region growing at different scales using reference data. Parameters like circularity, shape index, over/under-segmentation, closeness and boundary matching are used to evaluate the segments. Weights are assigned to these parameters, which are combined to select the best scale. The analysis found that watershed segmentation with a scale of 20 provided segments most similar to the reference data. The tool and research was supported by various grants and conducted under advisors at Clark University.
OBIA accuracy survey
This document summarizes a survey of accuracy assessment methods used in object-based image analysis (OBIA). The survey examined 100 papers that used OBIA for land cover classification. It found that 88% of the papers performed some type of accuracy assessment, most commonly comparing the OBIA outputs visually to the original imagery. For sampling design, most papers used points or polygons as the sampling unit, with around 50 total samples on average. Reference data was typically from the same year as the imagery and independently validated in around a third of studies.
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Overview
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Comparing directions
- 1. A n aspect map that shows the direction of slope of the landscape is an essential input in modeling the flow direction of a stream. This paper’s objective is to illustrate a novel method of comparing and quantifying the difference between two aspect maps created using different algorithms from two industry leading GIS softwares Idrisi and ArcGIS but same elevation data at different resolutions. The paper demonstrates that at high resolution (30m), the mean of difference between the aspect maps (MDA) from the two softwares is 25 0 but as we coarsen the resolution(30,720 m), MDA decreases to 3 0 . Thus, we can conclude that the flow models produced by using the aspect maps created using Idrisi and ArcGIS will show a bigger difference when using high resolution data when compared to flow models produced by using the low resolution aspect maps. 1 Elevation Aspect ArcGIS Slope Idrisi Slope ArcGIS Aspect Idrisi Sin ArcGIS Cos ArcGIS Sin Idrisi Cos Idrisi Coarser Aspect ArcGIS Coarser Aspect Idrisi Scatter Plots (Coarse) Scatter Plot (Fine)