This document describes a project to calibrate a camera using a calibration rig. Intrinsic and extrinsic camera parameters were calculated. Image and world coordinates of points on the calibration rig were collected. A projection matrix was calculated from the coordinates and used to determine the intrinsic parameters like focal length and extrinsic parameters like rotation and translation. The estimated image coordinates from the projection matrix were compared to measured coordinates to calculate errors, which improved when more points were used.
Using Generic Image Processing Operations to Detect a Calibration GridJan Wedekind
Camera calibration is an important problem in 3D computer vision. The problem of determining the camera parameters has been studied extensively. However the algorithms for determining the required correspondences are either semi-automatic (i.e. they require user interaction) or they involve difficult to implement custom algorithms.
We present a robust algorithm for detecting the corners of a calibration grid and assigning the correct correspondences for calibration . The solution is based on generic image processing operations so that it can be implemented quickly. The algorithm is limited to distortion-free cameras but it could potentially be extended to deal with camera distortion as well. We also present a corner detector based on steerable filters. The corner detector is particularly suited for the problem of detecting the corners of a calibration grid.
- See more at: http://figshare.com/articles/Using_Generic_Image_Processing_Operations_to_Detect_a_Calibration_Grid/696880#sthash.EG8dWyTH.dpuf
Output primitives computer graphics c versionMarwa Al-Rikaby
This document describes various algorithms for drawing lines in computer graphics, including the Digital Differential Analyzer (DDA) algorithm and Bresenham's line algorithm. The DDA algorithm samples a line at discrete positions by calculating changes in one coordinate by a fixed amount and determining the corresponding value of the other coordinate. Bresenham's algorithm uses only incremental integer calculations to determine which of two possible pixel positions is closer to the true line at each sample step.
is a range imaging technique; it refers to the process of estimating three-dimensional structures from two-dimensional image sequences which may be coupled with local motion signals
This document provides an overview of image processing using MATLAB. It discusses how images are represented as matrices in MATLAB and demonstrates various image processing functions and techniques.
Key points covered include:
- Loading and displaying an image using imread and image commands
- Converting between intensity, indexed, and RGB image representations
- Exploring image histograms and equalization
- Performing operations like resizing, rotation and filtering using functions like imresize, imrotate, and filters from fspecial
- Implementing convolution using custom kernels and built-in filters
- Understanding effects of different kernels on images
Templateless Marked Element Recognition Using Computer Visionshivam chaurasia
The document describes an algorithm for templateless marked element recognition in documents using computer vision. It discusses preprocessing steps like converting images to grayscale, blurring, and edge detection. It then describes detecting shapes like checkboxes and radio buttons using contour analysis and evaluating pixel thresholds to determine if elements are selected. Pseudocode provides details of the complete algorithm to detect and mark checked checkboxes and radio buttons on input images without predefined templates.
This document discusses various algorithms used for computer graphics rendering including scan conversion, line drawing, circle drawing, ellipse drawing, and polygon filling. It describes the Digital Differential Analyzer (DDA) algorithm for line drawing and Bresenham's algorithm as an improvement over DDA. Circle drawing is achieved using the midpoint circle algorithm and ellipse drawing using the midpoint ellipse algorithm. Polygon filling can be done using scan line filling or boundary filling algorithms.
Structure and Motion - 3D Reconstruction of Cameras and StructureGiovanni Murru
The document discusses structure from motion reconstruction from multiple images. It provides an overview of the steps to:
1. Estimate camera motion and 3D structure from a sequence of images using a stratified approach, starting with projective reconstruction and refining to affine and metric reconstruction.
2. Reconstruct structure and motion for two datasets - a public dataset and a personal dataset acquired by the student.
3. The key steps are feature detection, matching, estimating the fundamental matrix, triangulating 3D points, identifying the plane at infinity to upgrade from projective to affine reconstruction, and further refinement to metric reconstruction if possible.
METHOD FOR A SIMPLE ENCRYPTION OF IMAGES BASED ON THE CHAOTIC MAP OF BERNOULLIijcsit
In this document, we propose a simple algorithm for the encryption of gray-scale images, although the
scheme is perfectly usable in color images. Prior to encryption, the proposed algorithm includes a pair of
permutation processes, inspired by the Bernoulli mapping. The permutation disperses the image
information to hinder the unauthorized recovery of the original image. The image is encrypted using the
XOR function between a sequence generated from the same Bernoulli mapping and the image data,
obtained after two permutation processes. Finally, for the verification of the algorithm, the gray-scale Lena
pattern image was used; calculating histograms for each stage alongside of the encryption process. The
histograms prove dispersion evolution for pattern image during whole algorithm.
Using Generic Image Processing Operations to Detect a Calibration GridJan Wedekind
Camera calibration is an important problem in 3D computer vision. The problem of determining the camera parameters has been studied extensively. However the algorithms for determining the required correspondences are either semi-automatic (i.e. they require user interaction) or they involve difficult to implement custom algorithms.
We present a robust algorithm for detecting the corners of a calibration grid and assigning the correct correspondences for calibration . The solution is based on generic image processing operations so that it can be implemented quickly. The algorithm is limited to distortion-free cameras but it could potentially be extended to deal with camera distortion as well. We also present a corner detector based on steerable filters. The corner detector is particularly suited for the problem of detecting the corners of a calibration grid.
- See more at: http://figshare.com/articles/Using_Generic_Image_Processing_Operations_to_Detect_a_Calibration_Grid/696880#sthash.EG8dWyTH.dpuf
Output primitives computer graphics c versionMarwa Al-Rikaby
This document describes various algorithms for drawing lines in computer graphics, including the Digital Differential Analyzer (DDA) algorithm and Bresenham's line algorithm. The DDA algorithm samples a line at discrete positions by calculating changes in one coordinate by a fixed amount and determining the corresponding value of the other coordinate. Bresenham's algorithm uses only incremental integer calculations to determine which of two possible pixel positions is closer to the true line at each sample step.
is a range imaging technique; it refers to the process of estimating three-dimensional structures from two-dimensional image sequences which may be coupled with local motion signals
This document provides an overview of image processing using MATLAB. It discusses how images are represented as matrices in MATLAB and demonstrates various image processing functions and techniques.
Key points covered include:
- Loading and displaying an image using imread and image commands
- Converting between intensity, indexed, and RGB image representations
- Exploring image histograms and equalization
- Performing operations like resizing, rotation and filtering using functions like imresize, imrotate, and filters from fspecial
- Implementing convolution using custom kernels and built-in filters
- Understanding effects of different kernels on images
Templateless Marked Element Recognition Using Computer Visionshivam chaurasia
The document describes an algorithm for templateless marked element recognition in documents using computer vision. It discusses preprocessing steps like converting images to grayscale, blurring, and edge detection. It then describes detecting shapes like checkboxes and radio buttons using contour analysis and evaluating pixel thresholds to determine if elements are selected. Pseudocode provides details of the complete algorithm to detect and mark checked checkboxes and radio buttons on input images without predefined templates.
This document discusses various algorithms used for computer graphics rendering including scan conversion, line drawing, circle drawing, ellipse drawing, and polygon filling. It describes the Digital Differential Analyzer (DDA) algorithm for line drawing and Bresenham's algorithm as an improvement over DDA. Circle drawing is achieved using the midpoint circle algorithm and ellipse drawing using the midpoint ellipse algorithm. Polygon filling can be done using scan line filling or boundary filling algorithms.
Structure and Motion - 3D Reconstruction of Cameras and StructureGiovanni Murru
The document discusses structure from motion reconstruction from multiple images. It provides an overview of the steps to:
1. Estimate camera motion and 3D structure from a sequence of images using a stratified approach, starting with projective reconstruction and refining to affine and metric reconstruction.
2. Reconstruct structure and motion for two datasets - a public dataset and a personal dataset acquired by the student.
3. The key steps are feature detection, matching, estimating the fundamental matrix, triangulating 3D points, identifying the plane at infinity to upgrade from projective to affine reconstruction, and further refinement to metric reconstruction if possible.
METHOD FOR A SIMPLE ENCRYPTION OF IMAGES BASED ON THE CHAOTIC MAP OF BERNOULLIijcsit
In this document, we propose a simple algorithm for the encryption of gray-scale images, although the
scheme is perfectly usable in color images. Prior to encryption, the proposed algorithm includes a pair of
permutation processes, inspired by the Bernoulli mapping. The permutation disperses the image
information to hinder the unauthorized recovery of the original image. The image is encrypted using the
XOR function between a sequence generated from the same Bernoulli mapping and the image data,
obtained after two permutation processes. Finally, for the verification of the algorithm, the gray-scale Lena
pattern image was used; calculating histograms for each stage alongside of the encryption process. The
histograms prove dispersion evolution for pattern image during whole algorithm.
This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis and Local binary pattern (LBP) Algorithms. Experiments were carried out of The Japanese female facial expression (JAFFE) database. In all experiments conducted on JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with Average recognition rate of 90% under the same experimental setting.
The document discusses computer graphics concepts like points, pixels, lines, and circles. It begins with definitions of pixels and how they relate to points in geometry. It then covers the basic structure for specifying points in OpenGL and how to draw points, lines, and triangles. Next, it discusses algorithms for drawing lines, including the digital differential analyzer (DDA) method and Bresenham's line algorithm. Finally, it covers circle drawing and introduces the mid-point circle algorithm. In summary:
1) It defines key computer graphics concepts like pixels, points, lines, and circles.
2) It explains the basic OpenGL functions for drawing points and lines and provides examples of drawing simple shapes.
3) It
1. Simultaneous multi-slice imaging provides robust slice-level motion tracking for diffusion-weighted MRI by acquiring multiple coupled image planes simultaneously.
2. A novel registration-based motion tracking technique is proposed that uses simultaneous multi-slice acquisition and slice-to-volume registration to estimate and correct for motion, enabling reconstruction of neural microstructures in moving subjects.
3. The technique detects and rejects motion-corrupted images, tracks motion using simultaneous multi-slice registration, and performs robust reconstruction of diffusion tensors from motion-corrected data.
This document describes an image encryption and decryption technique using chaos algorithms. It uses the chaotic properties of the Henon map and Arnold cat map. The Henon map is used to generate pseudo-random key values for pixel shuffling. Pixel positions of the input image are first shuffled using the Arnold cat map. Then they are shuffled again using the sorted key values from the Henon map. This encrypts the image. Decryption reverses the process to recover the original pixel values and image. Experimental results show the encrypted image is secure and the original image can be recovered accurately using the correct key during decryption. The technique provides efficient and secure encryption of images for transmission.
3D Reconstruction from Multiple uncalibrated 2D Images of an ObjectAnkur Tyagi
3D reconstruction is the process of capturing the shape and appearance of real objects. In this project we are using passive methods which only use sensors to measure the radiance reflected or emitted by the objects surface to infer its 3D structure.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
Fuzzy c-means clustering is an unsupervised learning technique where each data point can belong to multiple clusters with varying degrees of membership. It works by assigning membership values between 0 and 1 to indicate how close each point is to the cluster centers. The algorithm aims to minimize an objective function to determine these optimal membership values and cluster centers. It is useful for overlapping data and outperforms hard clustering methods like k-means.
This document describes a framework for 2D pose estimation using active shape models and learned entropy field approximations. A dataset of manually annotated poses was created from NBA footage to train the models. Active shape models use principal component analysis to represent poses as a linear combination of modes of variation learned from the training data. To evaluate pose likelihood, image entropy is proposed as a texture similarity measure and regression is used to learn a function mapping poses to entropy fields, which can be compared to the image entropy. Current results are presented and future work to improve and speed up the approach is discussed.
The document discusses different algorithms for rasterizing lines in computer graphics. It describes how rasterization works by converting vector graphics into pixel representations. It then explains three strategies for rasterizing a line between two points: using the explicit line equation, parametric form, and incremental algorithms like the Digital Differential Analyzer (DDA) algorithm. The DDA algorithm works by incrementally calculating the next x and y pixel coordinates along the line using step sizes, avoiding expensive floating-point calculations.
At the end of this lecture, you should be able to;
describe the importance of morphological features in an image.
describe the operation of erosion, dilation, open and close operations.
identify the practical advantage of the morphological operations.
apply morphological operations for problem solving.
The document summarizes two papers on visual secret sharing methods for images. The first paper proposes using Hill cipher to divide a gray-level image into sub-images, and applying a random grid to construct shares. The original image can be perfectly recovered by combining the shares. The second paper uses a random grid method to allow secret image recovery either by stacking shares directly or applying an XOR operation to shares with computational assistance.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
The document discusses line drawing algorithms in computer graphics. It defines a line segment and provides equations to determine the slope and y-intercept of a line given two endpoints. It then introduces the Digital Differential Analyzer (DDA) algorithm, an incremental scan conversion method that calculates the next point on the line based on the previous point's coordinates and the line's slope. The algorithm involves less floating point computation than directly using the line equation at each step. An example demonstrates applying DDA to scan convert a line between two points. Limitations of DDA include the processing costs of rounding and floating point arithmetic as well as accumulated round-off error over long line segments.
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
This document provides an overview of various digital image processing techniques including morphological transformations, geometric transformations, image gradients, Canny edge detection, image thresholding, and a practical demo assignment. It discusses the basic concepts and algorithms for each technique and provides examples code. The document is presented as part of a practical course on digital image processing.
This document contains 25 multiple choice questions about digital image processing concepts. It covers topics like the steps in image processing (acquisition, sampling, quantization), neighbor pixels, distances between pixels, interpolation, aliasing, image sensors, contrast, storage requirements, and neighbors of a pixel. The questions range from basic to intermediate levels, testing understanding of foundational imaging concepts.
The document discusses computer graphics and line drawing algorithms. It begins with introductions to raster and vector images, as well as rasterization. It then describes the digital differential analyzer (DDA) line drawing algorithm, providing examples of how it works for lines with slopes less than and greater than 1. The DDA algorithm pseudocode is also presented. Finally, drawbacks of the DDA algorithm are noted and an optimized alternative, the Bresenham algorithm, is mentioned. The task for the next lab is to add OpenGL libraries in Visual Studio.
Comparison of Distance Transform Based FeaturesIJERA Editor
The distance transform based features are widely used in pattern recognition applications. A distance transform
assigns to each background pixel in a binary image a value equal to its distance to the nearest foreground pixel
according to a defined metric. Among these metrics the Chessboard, Euclidean, Chamfer and City-block are
popular. The role of a feature extraction method is quite important in pattern recognition applications. Before
applying a feature, it is essential to judge its performance on the given applications. In this research work, a
study on the performance of above mentioned distance transform based features is made. We have conducted
experiments with 500 hand-printed characters/class and the study has been performed on 43 classes. The
classifiers used are k-NN, MLP, SVM and PNN.
The document discusses various methods for image processing and analysis in MATLAB. It describes 4 basic types of images: indexed, grayscale, binary, and true color. It explains how to convert between these image types using functions like rgb2gray(), gray2ind(), im2bw(), etc. It also covers spatial transformations like resizing images with imresize(), rotating with imrotate(), and cropping with imcrop(). Finally, it discusses edge detection methods like Sobel, Prewitt, Roberts, and Canny using the edge() function.
The document describes an Advanced Lane Finding project that uses computer vision techniques to identify lane boundaries in video from a front-facing car camera. The steps include: 1) removing distortion, 2) isolating lanes using color and gradient thresholds, 3) warping the image to a bird's-eye view, 4) fitting curves to the lane pixels, and 5) creating the final output image with lane and radius of curvature information overlaid. Improvement areas include automating parameter selection using machine learning rather than manual tuning.
This document provides a summary of key concepts in computed tomography (CT) imaging. It discusses back projection reconstruction techniques which can produce blurred images. It also describes filtered back projection which uses digital subtraction filtering to reduce blurring. Fourier reconstruction techniques are described where projection data is transformed to the frequency domain using Fourier transforms before being reconstructed into a spatial domain image. Different window widths and window levels are discussed for optimizing soft tissue versus bone imaging.
The document provides a lab manual for computer graphics experiments in C language. It includes experiments on digital differential analyzer algorithm, Bresenham's line drawing algorithm, midpoint circle generation algorithm, ellipse generation algorithm, text and shape creation, 2D and 3D transformations, curve generation, and basic animations. It outlines the hardware and software requirements to run the experiments and provides background, algorithms, sample programs and outputs for each experiment.
This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis and Local binary pattern (LBP) Algorithms. Experiments were carried out of The Japanese female facial expression (JAFFE) database. In all experiments conducted on JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with Average recognition rate of 90% under the same experimental setting.
The document discusses computer graphics concepts like points, pixels, lines, and circles. It begins with definitions of pixels and how they relate to points in geometry. It then covers the basic structure for specifying points in OpenGL and how to draw points, lines, and triangles. Next, it discusses algorithms for drawing lines, including the digital differential analyzer (DDA) method and Bresenham's line algorithm. Finally, it covers circle drawing and introduces the mid-point circle algorithm. In summary:
1) It defines key computer graphics concepts like pixels, points, lines, and circles.
2) It explains the basic OpenGL functions for drawing points and lines and provides examples of drawing simple shapes.
3) It
1. Simultaneous multi-slice imaging provides robust slice-level motion tracking for diffusion-weighted MRI by acquiring multiple coupled image planes simultaneously.
2. A novel registration-based motion tracking technique is proposed that uses simultaneous multi-slice acquisition and slice-to-volume registration to estimate and correct for motion, enabling reconstruction of neural microstructures in moving subjects.
3. The technique detects and rejects motion-corrupted images, tracks motion using simultaneous multi-slice registration, and performs robust reconstruction of diffusion tensors from motion-corrected data.
This document describes an image encryption and decryption technique using chaos algorithms. It uses the chaotic properties of the Henon map and Arnold cat map. The Henon map is used to generate pseudo-random key values for pixel shuffling. Pixel positions of the input image are first shuffled using the Arnold cat map. Then they are shuffled again using the sorted key values from the Henon map. This encrypts the image. Decryption reverses the process to recover the original pixel values and image. Experimental results show the encrypted image is secure and the original image can be recovered accurately using the correct key during decryption. The technique provides efficient and secure encryption of images for transmission.
3D Reconstruction from Multiple uncalibrated 2D Images of an ObjectAnkur Tyagi
3D reconstruction is the process of capturing the shape and appearance of real objects. In this project we are using passive methods which only use sensors to measure the radiance reflected or emitted by the objects surface to infer its 3D structure.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
Fuzzy c-means clustering is an unsupervised learning technique where each data point can belong to multiple clusters with varying degrees of membership. It works by assigning membership values between 0 and 1 to indicate how close each point is to the cluster centers. The algorithm aims to minimize an objective function to determine these optimal membership values and cluster centers. It is useful for overlapping data and outperforms hard clustering methods like k-means.
This document describes a framework for 2D pose estimation using active shape models and learned entropy field approximations. A dataset of manually annotated poses was created from NBA footage to train the models. Active shape models use principal component analysis to represent poses as a linear combination of modes of variation learned from the training data. To evaluate pose likelihood, image entropy is proposed as a texture similarity measure and regression is used to learn a function mapping poses to entropy fields, which can be compared to the image entropy. Current results are presented and future work to improve and speed up the approach is discussed.
The document discusses different algorithms for rasterizing lines in computer graphics. It describes how rasterization works by converting vector graphics into pixel representations. It then explains three strategies for rasterizing a line between two points: using the explicit line equation, parametric form, and incremental algorithms like the Digital Differential Analyzer (DDA) algorithm. The DDA algorithm works by incrementally calculating the next x and y pixel coordinates along the line using step sizes, avoiding expensive floating-point calculations.
At the end of this lecture, you should be able to;
describe the importance of morphological features in an image.
describe the operation of erosion, dilation, open and close operations.
identify the practical advantage of the morphological operations.
apply morphological operations for problem solving.
The document summarizes two papers on visual secret sharing methods for images. The first paper proposes using Hill cipher to divide a gray-level image into sub-images, and applying a random grid to construct shares. The original image can be perfectly recovered by combining the shares. The second paper uses a random grid method to allow secret image recovery either by stacking shares directly or applying an XOR operation to shares with computational assistance.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
The document discusses line drawing algorithms in computer graphics. It defines a line segment and provides equations to determine the slope and y-intercept of a line given two endpoints. It then introduces the Digital Differential Analyzer (DDA) algorithm, an incremental scan conversion method that calculates the next point on the line based on the previous point's coordinates and the line's slope. The algorithm involves less floating point computation than directly using the line equation at each step. An example demonstrates applying DDA to scan convert a line between two points. Limitations of DDA include the processing costs of rounding and floating point arithmetic as well as accumulated round-off error over long line segments.
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
This document provides an overview of various digital image processing techniques including morphological transformations, geometric transformations, image gradients, Canny edge detection, image thresholding, and a practical demo assignment. It discusses the basic concepts and algorithms for each technique and provides examples code. The document is presented as part of a practical course on digital image processing.
This document contains 25 multiple choice questions about digital image processing concepts. It covers topics like the steps in image processing (acquisition, sampling, quantization), neighbor pixels, distances between pixels, interpolation, aliasing, image sensors, contrast, storage requirements, and neighbors of a pixel. The questions range from basic to intermediate levels, testing understanding of foundational imaging concepts.
The document discusses computer graphics and line drawing algorithms. It begins with introductions to raster and vector images, as well as rasterization. It then describes the digital differential analyzer (DDA) line drawing algorithm, providing examples of how it works for lines with slopes less than and greater than 1. The DDA algorithm pseudocode is also presented. Finally, drawbacks of the DDA algorithm are noted and an optimized alternative, the Bresenham algorithm, is mentioned. The task for the next lab is to add OpenGL libraries in Visual Studio.
Comparison of Distance Transform Based FeaturesIJERA Editor
The distance transform based features are widely used in pattern recognition applications. A distance transform
assigns to each background pixel in a binary image a value equal to its distance to the nearest foreground pixel
according to a defined metric. Among these metrics the Chessboard, Euclidean, Chamfer and City-block are
popular. The role of a feature extraction method is quite important in pattern recognition applications. Before
applying a feature, it is essential to judge its performance on the given applications. In this research work, a
study on the performance of above mentioned distance transform based features is made. We have conducted
experiments with 500 hand-printed characters/class and the study has been performed on 43 classes. The
classifiers used are k-NN, MLP, SVM and PNN.
The document discusses various methods for image processing and analysis in MATLAB. It describes 4 basic types of images: indexed, grayscale, binary, and true color. It explains how to convert between these image types using functions like rgb2gray(), gray2ind(), im2bw(), etc. It also covers spatial transformations like resizing images with imresize(), rotating with imrotate(), and cropping with imcrop(). Finally, it discusses edge detection methods like Sobel, Prewitt, Roberts, and Canny using the edge() function.
The document describes an Advanced Lane Finding project that uses computer vision techniques to identify lane boundaries in video from a front-facing car camera. The steps include: 1) removing distortion, 2) isolating lanes using color and gradient thresholds, 3) warping the image to a bird's-eye view, 4) fitting curves to the lane pixels, and 5) creating the final output image with lane and radius of curvature information overlaid. Improvement areas include automating parameter selection using machine learning rather than manual tuning.
This document provides a summary of key concepts in computed tomography (CT) imaging. It discusses back projection reconstruction techniques which can produce blurred images. It also describes filtered back projection which uses digital subtraction filtering to reduce blurring. Fourier reconstruction techniques are described where projection data is transformed to the frequency domain using Fourier transforms before being reconstructed into a spatial domain image. Different window widths and window levels are discussed for optimizing soft tissue versus bone imaging.
The document provides a lab manual for computer graphics experiments in C language. It includes experiments on digital differential analyzer algorithm, Bresenham's line drawing algorithm, midpoint circle generation algorithm, ellipse generation algorithm, text and shape creation, 2D and 3D transformations, curve generation, and basic animations. It outlines the hardware and software requirements to run the experiments and provides background, algorithms, sample programs and outputs for each experiment.
Stereo Vision Distance Estimation Employing Canny Edge Detector with Interpol...ZaidHussein6
This document summarizes a research paper that proposes a stereo vision algorithm called the Canny Block Matching Algorithm (CBMA) to estimate distance from stereo images. CBMA uses the Canny edge detector to extract edges from images and block matching with Sum of Absolute Difference (SAD) to determine disparity maps and reduce processing time. The algorithm was tested on stereo image pairs and achieved an error reduction of about 2% and processing time reduction compared to other methods. Interpolation techniques including bilinear, 1st order polynomial and 2nd order polynomial were also evaluated to enhance the output images and further reduce errors.
The document discusses content-based image retrieval (CBIR) which involves retrieving desired images from a large collection based on automatically extracted visual features like color, texture, and shape. It describes using exact Legendre moments to represent images and support vector machines (SVM) to classify images. The algorithm trains each class independently against other classes and constructs hyperplanes to classify new images based on which planes an image's features satisfy. The method achieved over 96% accuracy on a database with features up to order 5 and 18 training images per class.
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.
Signature recognition using clustering techniques dissertatiDr. Vinayak Bharadi
This document summarizes Vinayak Ashok Bharadi's dissertation on signature recognition using clustering techniques. It introduces the topic, outlines the problem definition and steps in signature recognition. It then discusses several preprocessing techniques, feature extraction methods like global features, grid and texture information, vector quantization, Walsh coefficients, and successive geometric centers. The document presents results and concludes by discussing the application of clustering techniques to signature recognition.
This document proposes a hardware implementation of a fixed-function 3D graphics pipeline for mobile applications. It presents the design of modules for vertex transformation, rasterization, texture mapping, and data transmission. Simulation results show the design can render 3D objects with color, textures, and different rendering modes. The design was fabricated in a 130nm technology and achieved a core power consumption of 1.768mW. Future work could involve replacing the fixed-function pipeline with programmable shaders to improve flexibility.
The document discusses Bresenham's line algorithm and how it avoids floating point operations. It explains that the algorithm uses integer arithmetic and a decision parameter to determine the next y-coordinate as it moves across the x-axis in unit intervals. It then describes modifications made to avoid any remaining floating point arithmetic by changing variables and comparisons. The algorithm is faster than other algorithms as it only requires additions and subtractions.
This document summarizes a research paper that proposes a content-based image retrieval system using cascaded color and texture features. Color features are first extracted from images using statistical measures like mean, standard deviation, energy, entropy, skewness and kurtosis. Similarity to a query image is then measured using distance metrics. The top 150 most similar images are then analyzed to extract Haralick texture features. Similarity is again measured to retrieve the most relevant images. The paper finds that Canberra distance provides better retrieval results than other distance metrics like City Block and Minkowski.
The document discusses a method for 3D object recognition from 2D images using centroidal representation. It involves several steps: filtering and binarizing the image, detecting edges, calculating the object center point, extracting features around the centroid, and creating mathematical models using wavelet transforms and autoregression. Centroidal samples represent distances from the center to the boundary every 45 degrees. Wavelet transforms and autoregression are used to create scale and position invariant representations of the object for recognition.
IRJET- 3D Vision System using Calibrated Stereo CameraIRJET Journal
This document describes a 3D vision system that uses calibrated stereo cameras to estimate the depth of objects. It discusses using two digital cameras placed at different positions to capture images of the same object. Feature matching and disparity calculation algorithms are used to calculate depth based on the difference between images. The cameras are calibrated using camera parameters derived from images of a checkerboard pattern. Trigonometry formulas are then used to calculate depth based on the camera positions and disparity. A servo system is used to independently and synchronously move the cameras along the x and y axes to capture views of objects from different angles.
Computer graphics uses programming and algorithms to draw pictures on screens. It involves computations to create and manipulate images. Common applications include GUIs, presentations, maps, medical imaging, engineering drawings, and entertainment like animation. Algorithms are used to generate basic shapes like lines, circles, and polygons. Line drawing algorithms include DDA, Bresenham's line algorithm, and the midpoint line algorithm. Circle generation uses Bresenham's or the midpoint circle algorithm. Polygon filling determines border versus interior pixels using techniques like the scan line or flood fill algorithms. Computer animation plays back recorded images fast enough to fool the eye into seeing motion. Traditional animation uses keyframing where keyframes define object changes and computers generate in-between frames. The
This document discusses scan conversion and line drawing algorithms. Scan conversion is the process of representing graphics objects as a collection of pixels. It converts vector images into raster images for display. Common objects that can be scan converted include points, lines, polygons, and characters. The document describes two algorithms for line drawing in scan conversion: DDA (Digital Differential Analyzer) and Bresenham's algorithm. It provides examples of how to use the DDA algorithm to plot lines between points by calculating the change in x and y values at each step and setting pixels accordingly. The DDA algorithm allows lines to be drawn rapidly but has disadvantages related to rounding operations.
This document discusses preprocessing QR codes through image processing techniques to improve readability. It outlines using thresholding to convert images to binary, tilt correction through calculating gradient and rotation, and nearest neighbor interpolation for rotation. Experimental results showed the approach was able to read QR codes from images taken at different angles and distances, with tilt and distortions corrected to decode the embedded information.
This document summarizes a computer vision project that aims to allow a camera fixed to a drone to determine its position relative to a pipe. The method uses images of a pipe covered in a known pattern to extract the camera's orientation. Key steps include binarizing images, detecting pattern dots, calculating 3D coordinates, and using EPnP to retrieve the camera pose from 2D-3D correspondences. The project achieves accurate pose estimation but has limitations such as not distinguishing pattern orientation. Future work could involve a modified pattern to address these limitations.
This document discusses single object tracking and velocity determination. It begins with an introduction and objectives of the project which is to develop an algorithm for tracking a single object and determining its velocity in a sequence of video frames. It then provides details on preprocessing techniques like mean filtering, Gaussian smoothing and median filtering to reduce noise. It describes segmentation methods including histogram-based, single Gaussian background and frame difference approaches. Feature extraction methods like edges, bounding boxes and color are explained. Object detection using optical flow and block matching is covered. Finally, it discusses tracking and calculating velocity of the moving object. MATLAB is introduced as a technical computing language for solving these types of problems.
This document proposes a new method for corner detection in images using difference chain coding as a measure of curvature. The method involves extracting a one-pixel thick boundary from the image, chain encoding it to determine slope, smoothing the boundary to remove noise, and calculating difference codes to determine points of high curvature change, which indicate corners. Preliminary results show the method is simple, efficient, and performs comparably to standard corner detection techniques like Harris and Yung.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
1. Name-Anish Hemmady
Project Report
Assignment No-1
1. Problem statement- To calibrate a camera using data points provided from image of
calibration rig and to find intrinsic and extrinsic parameters.
2. Procedure- Below is the image of calibration rig which consists of 3 axis x,y,z denoting
different axis. X axis is increasing from left to right. Z axis from below to upside its increasing.
The real world co-ordinates are given to us from this diagram. Each white block spans 2cm in
height and width. The x, y, z axis denotes world co-ordinates in cms.
Fig 1. Calibration rig image
2.1. Steps taken:
a) Note down world co-ordinates manually,some initial co-ordinates given in the image like
length from center to first square on leftside is 4,0,0 and on rightside its 0,4,0.Also we
know gap between 2 squares is 2cms each and height is also 2cms then you can go on
counting and find world coordinates of points of interest.
b) Image size is 1920*1280
2. c) You can note down 6 world coordinates and 6 image coordinates atleast.
d) Note down image coordinates using imagej or build your own api. I have built my own
api using python programming.The tkinter interface gui captures user mouse clicks
as events and returns them image coordinates.
e) Now prepare a program that automatically takes in these world and image coordinates and
generates camera matrix.
f) By using Projection matrix we can calculate intrinsic and extrinsic parameters.
2.2.Calculating Image co-ordinates using Tkinter (Python gui)
I programmed a gui using python programming which picked out image coordinates x,y as we
wanted x increasing from left to right and y coordinate increasing from bottom to up I had to
subtract y coordinate value from height which gave me real y coordinates as we wanted.x
coordinate was kept untouched.I collected 6 coordinates from tkinter.
2.3.Calculation of World coordinates
World coordinates were chosen manually as per scale given in the project question.Each square
object measured 2 cms in height and width.Distance from x,y,z origin marked in image to left
and rightside squares measured 4cms.From these given information I was able to find world
coordinates.
3. Calculation of Projection Matrix
Projection matrix is given by following matrix:
∏ = (
𝑎11, 𝑎12, 𝑎13, 𝑎14
𝑎21, 𝑎22, 𝑎23, 𝑎24
𝑎31, 𝑎32, 𝑎33, 𝑎34
)
A(ij) are the parameters to estimate.
Camera equation is given by:
Image points(u,v,w)= [matrix of (3*4)] *world coordinates(x,y,z,1)
There are 2 approaches to solve Projection matrix:
1) Using pseudo inverse property and multiplying it with known column vectors and
calculating the a(i,j) parameters.
2) Second approach is to use minimum eigenvalues and solving it
From the above matrix camera equations can be generated which will be inserted into our
program and a matrix of 12 rows and 11 columns would be formed.Its 11 columns since we
will solve by approach 1 where scale factor is set to 1 and 11 parameters are calculated
first.
3. I will be using approach 1 to solve projection matrix which is as follows:
a) After manipulating equations from above matrix we have to solve for Bp=0 where B is
the matrix formed by collecting data of image coordinates and world coordinates which is
12 rows by 11 columns.p is the unknown a(ij) parameters represented as 12 rows by 1
column as column vector.
b) Since we are scaling by factor of 1 i.e keeping a34=1 now we will get constants in
equations which will be shifted onto right handside and 12*1 known column vector of
image coordinates will be created.i.e. Bp=[12*1 image cords]
c) Now calculate matrix B from this equation.
d) After this step calculate pseudo-inverse of matrix B using (BT
B)-1
BT
e) We are taking transpose of B matrix since its not a square matrix,so we need to first
convert it into square matrix and find its inverse.
f) I have used numpy libraries pseudo inverse method to directly calculate the above result.
g) Now after finding pseudo inverse multiply B matrix by known column vector of image
coordinates and you will get output as 11 unknown parameters.
h) Now to find a34 value as we assumed it 1.0 previously,take the third row of known B
matrix which we found out from above procedure.Take a31,a32,a33 elements and do the
following.
i) √a31
2
+ a32
2
+ a33
2
value
j) Whatever value we got from taking square root of these 3 elements,divide entire matrix
by this value and we will get our final projection matrix.
4. Calculating Intrinsic and Extrinsic parameters
Intrinsic parameters-Intrinsic parameters are those parameters which depends on internal
configuration of camera. Focal length, skew and origin values are considered Inrinsic
parameters.(uo , vo, α,β)
Intrinsic parameters are calculated by using projection matrix we obtained from above.
1) Take the first row of projection matrix and do its transpose and multiply it with
third row of projection matrix i.e dot product with third row you will get uo
2) Similarly take the second row and do transpose of second row and multiply it with third
row then you will get vo
3) For α calculation multiply first row transpose with first row itself.After getting
value from this take squared value of uo and then subtract uo squared value from
value of dot product you got earlier.Take square root of this value after subtraction
and you have got your α.
4) Similarly take dot product of second row with its transpose and take vo squared value and
follow above steps as mentioned in 3.You will get β.
4. Extrinsic parameters
We can calculate extrinsic parameters from the projection matrix and using intrinsic
parameter matrix formed from above methods.
Extrinsic parameters are Rotation matrix and Translation matrix
Follow foll steps for calculating Extrinsic parameters:
1) Take first 3 rows and first 3 columns of projection matrix these denote the rotation values
from which we will get rotation matrix.This above mentioned 3*3 matrix multiply
should be multiplied with inverse of Intrinsic parameter matrix.We will get
rotational parameters from this.
2) The third column of projection matrix should be taken and multiplied with inverse
of intrinsic matrix.We will get translation parameter from this.
5. Results
Image coordinates values
(794.0,331.0),(456.0,845.0),(844.0,799.0),(1036.0,335.0),(1402.0,325.0),(1398.0,852.0)
World coordinates values :(6,0,0),(18,0,14),(6,0,14),(0,4,0),(0,16,0),(0,16,14)
B matrix containing 11 parameters I have found plus it contains a34 value which we had
assumed 1.0
[[ -3.84833298e+01 9.95643616e+00 8.04584312e+00 9.48233887e+02]
[ -8.70588875e+00 -5.85336113e+00 3.49512482e+01 3.48210083e+02]
[ -1.54169165e-02 -1.30057931e-02 5.71391918e-03 1.00000000e+00]]
Final B matrix known as Normalized matrix which contains all 12 parameters value
[[ -1.83570407e+03 4.74934743e+02 3.83797011e+02 4.52319695e+04]
[ -4.15282031e+02 -2.79212815e+02 1.66721925e+03 1.66100665e+04]
[ -7.35406639e-01 -6.20392970e-01 2.72561255e-01 4.77012794e+01]]
5. Intrinsic parameters matrix
[[ 1.54828496e+03 0.00000000e+00 1.15995098e+03]
[ 0.00000000e+00 1.46951395e+03 9.33042203e+02]
[ 0.00000000e+00 0.00000000e+00 1.00000000e+00]]
Extrinsic parameters matrix for Rotation
[[-0.63468189 0.77153767 0.04368661]
[ 0.18433537 0.20390416 0.96147988]
[-0.73540664 -0.62039297 0.27256126]]
Extrinsic parameters for Translation
[ 0.04368661 0.96147988 0.27256126]
Parameter Value
uo 1159.95097641
vo 933.042202582
α 1548.28495503
β 1469.51395002
6. Reconstructing Image coordinates (Error checking)
To check whether earlier projection matrix what we got gives us how precise image
coordinates we do error checking by reconstructing image coordinates from world
coordinates. Here only 6 coordinates were taken into account so we have high error rate
Image coordinates
(measured using
Imagej)
World coordinates
In cms
Image coordinates
estimated using my
program
Error Normalization
(688,394) (10,0,2,1) (675.98,386.17) 15.2
(794,398) (6,0,2,1) (798,398.15) 4.0
(946,342) (0,0,0,1) (948,348) 5.65
(1234,398) (0,12,2,1) (1267,390) 34
6. Error Normalization is done by calculating difference between x and y coordinates in Image
coordinates measured using Imagej and then squaring these difference values. After this both
squared values are added and square root is taken to calculate Error normalization. Most of the
errored values are medium but the last one little big which draws me to the conclusion that if we
are able to take more number of points of image we get less error.
Below table is formed by taking 12 coordinates instead of 6 coordinates. You can cross
check it with the values of 12 coordinates commented in my program.
Image coordinates
(measured using
Imagej)
World coordinates
In cms
Image coordinates
estimated using my
program
Error Normalization
(688,394) (10,0,2,1) (694.26,396.1) 6.32
(794,398) (6,0,2,1) (800,398) 6.0
(946,342) (0,0,0,1) (940,340) 6.32
(1234,398) (0,12,2,1) (1245,399) 11.0
Error Normalization e.g. for 10,0,21√(694.26 – 688)2
+ (396.1-394)2
=6.32
Errors can be introduced while noting down world coordinates manually and if you take less
number of points for matrix error rate while reconstructing image would be high.Errors can also
be introduce intrinsically while matrix multiplication and stuff.
Conclusion: For image calibration one should collect as many as points possible to get the
exact projection matrix. If number of points collected is less error rate would be high.Also it
depends how you choose your points. I observed if we take more points from left handside of
image then error rate would be high on righthandside since we have taken less points from
right side.Equal number of points should be collected from both sides.