This document summarizes a series of lectures on fundamentals of image processing and analysis delivered at Cambridge University's Engineering Department. The lectures covered topics such as digital imaging, point and local operations, frequency domain methods, image segmentation, representation of objects, and morphological operations. The goal was to introduce basic concepts and techniques in digital image processing and computerized image analysis.
This document discusses digital image processing and various image enhancement techniques. It begins with introductions to digital image processing and fundamental image processing systems. It then covers topics like image sampling and quantization, color models, image transforms like the discrete Fourier transform, and noise removal techniques like median filtering. Histogram equalization and homomorphic filtering are also summarized as methods for image enhancement.
Design and implementation of a Neural Network based image compression engine as part of Final Year Project by Jesu Joseph and Shibu Menon at Nanyang Technological University. The project won the best possible grade and excellent accolades from the research center.
WEBINAR ON FUNDAMENTALS OF DIGITAL IMAGE PROCESSING DURING COVID LOCK DOWN by by K.Vijay Anand , Associate Professor, Department of Electronics and Instrumentation Engineering , R.M.K Engineering College, Tamil Nadu , India
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses image compression algorithms using the Lapped Orthogonal Transform (LOT) and Discrete Cosine Transform (DCT) under the JPEG standard. It begins with an introduction to image compression and classification of compression schemes. It then describes LOT and DCT in detail and proposes a hybrid algorithm using both transforms simultaneously. The algorithm is tested on an image and achieves a peak signal-to-noise ratio of 36.76 decibels at a bit rate of 0.6 bits per pixel, providing higher quality than DCT alone. The document concludes the hybrid approach offers better energy compaction and quality at low bit rates than DCT.
This document outlines an assignment for a computer vision course. Students are asked to implement 4 vision algorithms: 2 using OpenCV and 2 using MATLAB. The algorithms are the log-polar transform, background subtraction, histogram equalization, and contrast stretching. Students must also answer 3 short questions about orthographic vs perspective projection, efficient filtering, and sensors beyond cameras for computer vision.
Translation Invariance (TI) based Novel Approach for better De-noising of Dig...IRJET Journal
1. The document discusses a novel Translation Invariance (TI) approach for improving the performance of various digital image processing filters for image denoising.
2. It describes applying filters like convolution, wiener, gaussian etc. both without TI (directly on noisy image) and with TI (by shifting the image and averaging results) to denoise images.
3. The results found that using the TI approach, where the filters are applied after shifting the image and averaging the outputs, produced better performance and noise removal compared to directly applying the filters without translation invariance. This was also verified using edge detection tests.
AN EMERGING TREND OF FEATURE EXTRACTION METHOD IN VIDEO PROCESSINGcscpconf
Recently the progress in technology and flourishing applications open up new forecast and defy
for the image and video processing community. Compared to still images, video sequences
afford more information about how objects and scenarios change over time. Quality of video is
very significant before applying it to any kind of processing techniques. This paper deals with
two major problems in video processing they are noise reduction and object segmentation on
video frames. The segmentation of objects is performed using foreground segmentation based
and fuzzy c-means clustering segmentation is compared with the proposed method Improvised
fuzzy c – means segmentation based on color. This was applied in the video frame to segment
various objects in the current frame. The proposed technique is a powerful method for image
segmentation and it works for both single and multiple feature data with spatial information.
The experimental result was conducted using various noises and filtering methods to show which is best suited among others and the proposed segmentation approach generates good quality segmented frames.
This document discusses digital image processing and various image enhancement techniques. It begins with introductions to digital image processing and fundamental image processing systems. It then covers topics like image sampling and quantization, color models, image transforms like the discrete Fourier transform, and noise removal techniques like median filtering. Histogram equalization and homomorphic filtering are also summarized as methods for image enhancement.
Design and implementation of a Neural Network based image compression engine as part of Final Year Project by Jesu Joseph and Shibu Menon at Nanyang Technological University. The project won the best possible grade and excellent accolades from the research center.
WEBINAR ON FUNDAMENTALS OF DIGITAL IMAGE PROCESSING DURING COVID LOCK DOWN by by K.Vijay Anand , Associate Professor, Department of Electronics and Instrumentation Engineering , R.M.K Engineering College, Tamil Nadu , India
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses image compression algorithms using the Lapped Orthogonal Transform (LOT) and Discrete Cosine Transform (DCT) under the JPEG standard. It begins with an introduction to image compression and classification of compression schemes. It then describes LOT and DCT in detail and proposes a hybrid algorithm using both transforms simultaneously. The algorithm is tested on an image and achieves a peak signal-to-noise ratio of 36.76 decibels at a bit rate of 0.6 bits per pixel, providing higher quality than DCT alone. The document concludes the hybrid approach offers better energy compaction and quality at low bit rates than DCT.
This document outlines an assignment for a computer vision course. Students are asked to implement 4 vision algorithms: 2 using OpenCV and 2 using MATLAB. The algorithms are the log-polar transform, background subtraction, histogram equalization, and contrast stretching. Students must also answer 3 short questions about orthographic vs perspective projection, efficient filtering, and sensors beyond cameras for computer vision.
Translation Invariance (TI) based Novel Approach for better De-noising of Dig...IRJET Journal
1. The document discusses a novel Translation Invariance (TI) approach for improving the performance of various digital image processing filters for image denoising.
2. It describes applying filters like convolution, wiener, gaussian etc. both without TI (directly on noisy image) and with TI (by shifting the image and averaging results) to denoise images.
3. The results found that using the TI approach, where the filters are applied after shifting the image and averaging the outputs, produced better performance and noise removal compared to directly applying the filters without translation invariance. This was also verified using edge detection tests.
AN EMERGING TREND OF FEATURE EXTRACTION METHOD IN VIDEO PROCESSINGcscpconf
Recently the progress in technology and flourishing applications open up new forecast and defy
for the image and video processing community. Compared to still images, video sequences
afford more information about how objects and scenarios change over time. Quality of video is
very significant before applying it to any kind of processing techniques. This paper deals with
two major problems in video processing they are noise reduction and object segmentation on
video frames. The segmentation of objects is performed using foreground segmentation based
and fuzzy c-means clustering segmentation is compared with the proposed method Improvised
fuzzy c – means segmentation based on color. This was applied in the video frame to segment
various objects in the current frame. The proposed technique is a powerful method for image
segmentation and it works for both single and multiple feature data with spatial information.
The experimental result was conducted using various noises and filtering methods to show which is best suited among others and the proposed segmentation approach generates good quality segmented frames.
This document is a seminar report on digital image processing submitted by a student, N.Ch. Karthik, in partial fulfillment of a Bachelor of Technology degree. It discusses correcting raw images by subtracting dark current and bias, flat fielding for pixel sensitivity variations, and displaying images by limiting histograms, using transfer functions, and histogram equalization. The report also covers mathematical image manipulations and references other works.
This document proposes a method for change detection in images that combines Change Vector Analysis, K-Means clustering, Otsu thresholding, and mathematical morphology. It involves detecting intensity changes using CVA, segmenting the difference image using K-Means, calculating a threshold with Otsu's method, applying the threshold and morphological operations, and comparing results to other change detection techniques. Experimental results on medical and other images show the proposed method achieves satisfactory change detection with fewer errors compared to other methods.
Iaetsd wavelet transform based latency optimized image compression forIaetsd Iaetsd
This document discusses wavelet transform based image compression. It proposes a new discrete wavelet transform (DWT) architecture based on fast convolution that reduces hardware complexity and memory requirements while also decreasing the critical path delay. This allows the system to produce outputs in fewer clock cycles for improved efficiency. The proposed architecture is evaluated against existing designs and shown to achieve better performance in terms of reduced area and processing time.
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 compares the DCT and DWT image compression techniques. It finds that DWT provides higher compression ratios and avoids blocking artifacts compared to DCT. DWT allows better localization in both spatial and frequency domains. It also finds that DCT takes more time for compression than DWT. Experimental results on test images show that DWT achieves higher PSNR and lower MSE and BER than DCT, indicating better reconstructed image quality at the same compression ratio. In conclusion, DWT is found to provide better compression performance than DCT for images.
This document presents a comparison of two image inpainting techniques - curvature driven diffusion (CDD) inpainting and total variation (TV) inpainting. The paper aims to apply these two inpainting methods to grayscale and color images to restore damaged regions. CDD inpainting works by solving partial differential equations of isophote intensity, while TV inpainting is based on texture filling. Experimental results on various images are shown to demonstrate the effectiveness of the two approaches. The document also discusses related work, provides implementation details of the two methods, and outlines potential future work including hardware implementation.
Frequency Domain Blockiness and Blurriness Meter for Image Quality AssessmentCSCJournals
Image and video compression introduces distortions (artefacts) to the coded image. The most prominent artefacts added are blockiness and blurriness. Many existing quality meters are normally distortion-specific. This paper proposes an objective quality meter for quantifying the combined blockiness and blurriness distortions in frequency domain. The model first applies edge detection and cancellation, then spatial masking to mimic the characteristics of the human visual system. Blockiness is then estimated by transforming image into frequency domain, followed by finding the ratio of harmonics to other AC components. Blurriness is determined by comparing the high frequency coefficients of the reference and coded images due to the fact that blurriness reduces the high frequency coefficients. Then, both blockiness and blurriness distortions are combined for a single quality metric. The meter is tested on blocky and blurred images from the LIVE image database, with a correlation coefficient of 95-96%.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
This document presents a method for detecting hard exudates in retinal fundus images using simulated annealing based thresholding. The proposed method involves 5 steps: 1) median filtering to reduce noise and blur exudates, 2) image subtraction between the input and filtered images to extract bright regions, 3) application of simulated annealing to determine an optimal threshold value, 4) thresholding with the determined value to segment exudates, and 5) image addition to enhance detection. The method was tested on 10 images and achieved an overall sensitivity of 98.66% and predictivity of 98.12% according to expert evaluation, demonstrating accurate exudate detection.
DETECTION OF HARD EXUDATES USING SIMULATED ANNEALING BASED THRESHOLDING MECHA...cscpconf
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes severe damage to retina and may lead to complete or partial visual loss. In case of diabetic retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked out of the damaged blood vessels and are deposited in the intra-retinal space. They are normally seen as whitish marks of various shape and are called as exudates. Exudates are primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible in nature, the disease must be detected in early stages to prevent visual loss. But detection of exudates in early stages of the disease is extremely difficult only by visual inspection because of small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such method.
IRJET - Change Detection in Satellite Images using Convolutional Neural N...IRJET Journal
The document describes a method for detecting changes in satellite images using convolutional neural networks. It discusses how existing methods have limitations in terms of accuracy and speed. The proposed method uses preprocessing techniques like median filtering and non-local means filtering. It then applies convolutional neural networks to extracted compressed image features and classify detected changes. The method forms a difference image without explicitly training on change images, making it unsupervised. Testing achieved 91.63% accuracy in change detection, showing the effectiveness of the proposed convolutional neural network approach.
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level
representations of input data which has been introduced to many practical and challenging learning
problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and
conduct training a large image database to get the experimental output. The result shows the robustness
and efficient our our algorithm.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the high-level implementation of the Canny algorithm using Simulink. The design and system-level block diagram of the implementation on an FPGA is shown, including loading an input image and displaying the output. Simulation and synthesis results are presented, showing the resource utilization on a Spartan 3E FPGA board. The implementation provides real-time edge detection to interface an FPGA with a monitor.
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
IMAGE DE-NOISING USING DEEP NEURAL NETWORKaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSINGPriyanka Rathore
Image processing techniques can involve converting images to digital form and applying transformations like the Laplace transform. The Laplace transform is useful for applications like image sharpening, edge detection, and blob detection. It involves calculating the second derivative of the image to help identify edges and other discontinuities. The zero crossings of the Laplace transform output are particularly useful for edge detection as they indicate where the slope of the image changes most rapidly. While the Laplace transform provides benefits like simpler implementation and reliable noise performance, it can also result in spaghetti-like edge effects with complex computations.
A Review on Image Compression using DCT and DWTIJSRD
This document reviews image compression techniques using discrete cosine transform (DCT) and discrete wavelet transform (DWT). It discusses how DCT transforms images from spatial to frequency domains, allowing for energy compaction and efficient encoding. DWT is a multi-resolution technique that represents images at different frequency bands. The document analyzes various studies that have used DCT and DWT for compression and compares their performance in terms of metrics like peak signal-to-noise ratio and compression ratio. It finds that DWT generally provides better compression performance than DCT, though DCT requires less computational resources. A hybrid DCT-DWT technique is also proposed to combine the advantages of both methods.
This document discusses image analysis using wavelet transformation. It provides an overview of digital image processing and compares Fourier transforms, short-term Fourier transforms, and wavelet transforms. Wavelet transforms provide better time-frequency localization than Fourier transforms. The document demonstrates Haar wavelets and how they can be used to decompose an image into different frequency subbands. It discusses applications of wavelet transforms such as image compression, denoising, and feature extraction. The document includes MATLAB code for performing wavelet decomposition on an image.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
This document is a seminar report on digital image processing submitted by a student, N.Ch. Karthik, in partial fulfillment of a Bachelor of Technology degree. It discusses correcting raw images by subtracting dark current and bias, flat fielding for pixel sensitivity variations, and displaying images by limiting histograms, using transfer functions, and histogram equalization. The report also covers mathematical image manipulations and references other works.
This document proposes a method for change detection in images that combines Change Vector Analysis, K-Means clustering, Otsu thresholding, and mathematical morphology. It involves detecting intensity changes using CVA, segmenting the difference image using K-Means, calculating a threshold with Otsu's method, applying the threshold and morphological operations, and comparing results to other change detection techniques. Experimental results on medical and other images show the proposed method achieves satisfactory change detection with fewer errors compared to other methods.
Iaetsd wavelet transform based latency optimized image compression forIaetsd Iaetsd
This document discusses wavelet transform based image compression. It proposes a new discrete wavelet transform (DWT) architecture based on fast convolution that reduces hardware complexity and memory requirements while also decreasing the critical path delay. This allows the system to produce outputs in fewer clock cycles for improved efficiency. The proposed architecture is evaluated against existing designs and shown to achieve better performance in terms of reduced area and processing time.
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 compares the DCT and DWT image compression techniques. It finds that DWT provides higher compression ratios and avoids blocking artifacts compared to DCT. DWT allows better localization in both spatial and frequency domains. It also finds that DCT takes more time for compression than DWT. Experimental results on test images show that DWT achieves higher PSNR and lower MSE and BER than DCT, indicating better reconstructed image quality at the same compression ratio. In conclusion, DWT is found to provide better compression performance than DCT for images.
This document presents a comparison of two image inpainting techniques - curvature driven diffusion (CDD) inpainting and total variation (TV) inpainting. The paper aims to apply these two inpainting methods to grayscale and color images to restore damaged regions. CDD inpainting works by solving partial differential equations of isophote intensity, while TV inpainting is based on texture filling. Experimental results on various images are shown to demonstrate the effectiveness of the two approaches. The document also discusses related work, provides implementation details of the two methods, and outlines potential future work including hardware implementation.
Frequency Domain Blockiness and Blurriness Meter for Image Quality AssessmentCSCJournals
Image and video compression introduces distortions (artefacts) to the coded image. The most prominent artefacts added are blockiness and blurriness. Many existing quality meters are normally distortion-specific. This paper proposes an objective quality meter for quantifying the combined blockiness and blurriness distortions in frequency domain. The model first applies edge detection and cancellation, then spatial masking to mimic the characteristics of the human visual system. Blockiness is then estimated by transforming image into frequency domain, followed by finding the ratio of harmonics to other AC components. Blurriness is determined by comparing the high frequency coefficients of the reference and coded images due to the fact that blurriness reduces the high frequency coefficients. Then, both blockiness and blurriness distortions are combined for a single quality metric. The meter is tested on blocky and blurred images from the LIVE image database, with a correlation coefficient of 95-96%.
Detection of hard exudates using simulated annealing based thresholding mecha...csandit
This document presents a method for detecting hard exudates in retinal fundus images using simulated annealing based thresholding. The proposed method involves 5 steps: 1) median filtering to reduce noise and blur exudates, 2) image subtraction between the input and filtered images to extract bright regions, 3) application of simulated annealing to determine an optimal threshold value, 4) thresholding with the determined value to segment exudates, and 5) image addition to enhance detection. The method was tested on 10 images and achieved an overall sensitivity of 98.66% and predictivity of 98.12% according to expert evaluation, demonstrating accurate exudate detection.
DETECTION OF HARD EXUDATES USING SIMULATED ANNEALING BASED THRESHOLDING MECHA...cscpconf
Diabetic retinopathy is a disease commonly found in case of diabetes mellitus patients. It causes severe damage to retina and may lead to complete or partial visual loss. In case of diabetic retinopathy retinal blood vessel gets damaged and protein and fat based particles gets leaked out of the damaged blood vessels and are deposited in the intra-retinal space. They are normally seen as whitish marks of various shape and are called as exudates. Exudates are primary indication of diabetic retinopathy. As changes occurs due to the disease is irreversible in nature, the disease must be detected in early stages to prevent visual loss. But detection of exudates in early stages of the disease is extremely difficult only by visual inspection because of small diameter of human eye. But an efficient automated computerized system can have the
ability to detect the disease in very early stage. In this paper we have proposed one such method.
IRJET - Change Detection in Satellite Images using Convolutional Neural N...IRJET Journal
The document describes a method for detecting changes in satellite images using convolutional neural networks. It discusses how existing methods have limitations in terms of accuracy and speed. The proposed method uses preprocessing techniques like median filtering and non-local means filtering. It then applies convolutional neural networks to extracted compressed image features and classify detected changes. The method forms a difference image without explicitly training on change images, making it unsupervised. Testing achieved 91.63% accuracy in change detection, showing the effectiveness of the proposed convolutional neural network approach.
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level
representations of input data which has been introduced to many practical and challenging learning
problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and
conduct training a large image database to get the experimental output. The result shows the robustness
and efficient our our algorithm.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the high-level implementation of the Canny algorithm using Simulink. The design and system-level block diagram of the implementation on an FPGA is shown, including loading an input image and displaying the output. Simulation and synthesis results are presented, showing the resource utilization on a Spartan 3E FPGA board. The implementation provides real-time edge detection to interface an FPGA with a monitor.
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
Canny Edge Detection Algorithm on FPGA IOSR Journals
This document summarizes the implementation of the Canny edge detection algorithm on an FPGA. It begins with an introduction to edge detection and digital image processing. It then describes the Canny edge detection algorithm and its benefits. The document outlines the high-level implementation in Simulink and shows the input, grayscaled, and edge detected output images. It presents the system design with the FPGA reading in an image file and performing Canny edge detection. Simulation and synthesis results are shown verifying the design works as intended. The paper concludes the Canny edge detection algorithm was successfully designed, simulated, tested and realized on an FPGA.
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
IMAGE DE-NOISING USING DEEP NEURAL NETWORKaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSINGPriyanka Rathore
Image processing techniques can involve converting images to digital form and applying transformations like the Laplace transform. The Laplace transform is useful for applications like image sharpening, edge detection, and blob detection. It involves calculating the second derivative of the image to help identify edges and other discontinuities. The zero crossings of the Laplace transform output are particularly useful for edge detection as they indicate where the slope of the image changes most rapidly. While the Laplace transform provides benefits like simpler implementation and reliable noise performance, it can also result in spaghetti-like edge effects with complex computations.
A Review on Image Compression using DCT and DWTIJSRD
This document reviews image compression techniques using discrete cosine transform (DCT) and discrete wavelet transform (DWT). It discusses how DCT transforms images from spatial to frequency domains, allowing for energy compaction and efficient encoding. DWT is a multi-resolution technique that represents images at different frequency bands. The document analyzes various studies that have used DCT and DWT for compression and compares their performance in terms of metrics like peak signal-to-noise ratio and compression ratio. It finds that DWT generally provides better compression performance than DCT, though DCT requires less computational resources. A hybrid DCT-DWT technique is also proposed to combine the advantages of both methods.
This document discusses image analysis using wavelet transformation. It provides an overview of digital image processing and compares Fourier transforms, short-term Fourier transforms, and wavelet transforms. Wavelet transforms provide better time-frequency localization than Fourier transforms. The document demonstrates Haar wavelets and how they can be used to decompose an image into different frequency subbands. It discusses applications of wavelet transforms such as image compression, denoising, and feature extraction. The document includes MATLAB code for performing wavelet decomposition on an image.
Similar to Fundamentals of Image processing.ppt (20)
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
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Why Computers in Microscopy?
This is the era of low-cost computer hardware
Allows diagnosis/analysis of images quantitatively
Compensate for defects in imaging process (restoration)
Certain techniques impossible any other way
Speed & reduced specimen irradiation
Avoidance of human error
Consistency and repeatability
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Digital Images
A natural image is a continuous, 2-dimensional
distribution of brightness (or some other physical
effect).
Conversion of natural images into digital form
involves two key processes, jointly referred to as
digitisation:
Sampling
Quantisation
Both involve loss of image fidelity i.e. approximations.
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Sampling
Sampling represents the image by measurements at
regularly spaced sample intervals. Two important
criteria:-
Sampling interval
•distance between sample points or pixels
Tessellation
•the pattern of sampling points
The number of pixels in the image is called the
resolution of the image. If the number of pixels is too
small, individual pixels can be seen and other
undesired effects (e.g. aliasing) may be evident.
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Quantisation
Quantisation uses an ADC (analogue to digital
converter) to transform brightness values into a
range of integer numbers, 0 to M, where M is limited
by the ADC and the computer.
where m is the number of bits used to represent the
value of each pixel. This determines the number of
grey levels.
Too few bits results in steps between grey levels
being apparent.
M m
2
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Example
For an image of 512 by 512 pixels, with 8 bits per
pixel:
Memory required = 0.25 megabytes
Images from video sources (e.g. video camera) arrive
at 25 images, or frames, per second:
Data rate = 6.55 million pixels per second
The capture of video images involves large amounts
of data occurring at high rates.
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The Grey-level Histogram
One of the simplest, yet most useful tools.
Can show up faulty settings in an image digitiser.
Almost impossible to achieve without digital
hardware.
Number of
pixels
Grey level
The GLH is a function
showing for each grey
level the number of pixels
that have that grey level.
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Image segmentation
The GLH can often be used to distinguish simple
objects from background and determine their area.
Dark objects, bright background
Area B Area A
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Grey Level Histogram Equalisation
In GLH equalisation, a non-linear grey scale
transformation redistributes the grey levels, producing
an image with a flattened histogram.
This can result in a striking contrast improvement.
Original image Cumulative GL histogram Flattened histogram
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Point Operations
Point operations affect the way images occupy greyscale.
A point operation transforms an input image, producing
an output image in which each pixel grey level is related in
a systematic way to that of the corresponding input pixel.
Unchanged Stretch
Compress
Output
grey level
Input
grey level
A point operation will
never alter the spatial
relationships within an
image.
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Examples of Point Operations
Input
Output
t Input
Output
t t
1 2
(a) Threshold (b) Window Threshold
Input
Output
(c) Contrast Stretch
Input
Output
(d) Contrast Compression
Input
Output
(e) Combination
Input
Output
(f) Contouring
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– Addition:
– Subtraction
Algebraic operations
A point form of operation (with >1 input image)
Grey level of each output pixel depends only on grey
levels of corresponding pixels in input images
Four major operations:
C A B
– Multiplication:
– Division:
C A B
C A B
C A B
Other operations can be defined that involve more than 2 input
images, or using (for example) boolean or logic operators.
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Applications of algebraic operations
Addition
Ensemble averaging to reduce noise
Superimposing one image upon another
Subtraction
Removal of unwanted additive interference
(background suppression)
Motion detection
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Applications (continued)
Multiplication
Removal of unwanted multiplicative interference
(background suppression)
Masking prior to combination by addition
Windowing prior to Fourier transformation
Division
Background suppression (as multiplication)
Special imaging signals (multi-spectral work)
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Local Operations
In a local operation, the value of a pixel in the output
image is a function of the corresponding pixel in the
input image and its neighbouring pixels. Local
operations may be used for:-
image smoothing
noise cleaning
edge enhancement
boundary detection
assessment of texture
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Local operations for image smoothing
Image averaging can be described as follows:-
0 1 0
1/4 1 0 1
0 1 0
h x y
( , )
The mask shows
graphically the disposition
and weights of the pixels
involved in the operation.
Total weight =
Image averaging is an example of low-pass filtering.
h x y
n
( , )
4
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Low Pass and Median Filters
The low-pass filter can provide image smoothing and
noise reduction, but subdues and blurs sharp edges.
Median filters can provide noise filtering without
blurring.
Original image Averaging filter Median filter
Edge
Noise pulse
1 1 1
1 1 1
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High Pass Filters
Subtracting contributions from neighbouring pixels
resembles differentiation, and can emphasise or sharpen
variations in contrast. This technique is known as High
Pass Filtering.
-1 1 -1
1
The simplest high-pass filter
simulates the mathematical
gradient operator:
G
df dy
df dx
h1 h2
h1 gives the vertical, and h2 the horizontal component. The
two parts are then summed (ignoring sign) to give the result.
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Further examples of filters
These masks contain 9 elements organised as 3 x 3.
Calculation of one output pixel requires 9
multiplications & 9 additions. Larger masks may
involve long computing times unless special
hardware (a convolver) is available.
1 1 1 -1 0 1 0 1 0 -1 -1 -1
1/4 1 1 1 -2 0 2 1 -4 1 -1 9 -1
1 1 1 -1 0 1 0 1 0 -1 -1 -1
(a) Averaging (b) Sobel (c) Laplacian (d) High Pass
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Frequency Domain
Frequency refers to the rate of repetition of some
periodic event. In imaging, Spatial Frequency refers
to the variations of image brightness with position in
space.
A varying signal can be transformed into a series of
simple periodic variations. The Fourier Transform is
a well known example and decomposes the signal
into a set of sine waves of different characteristics
(frequency and phase).
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Amplitude and Phase
The spectrum is the set of waves representing a
signal as frequency components. It specifies for each
frequency:
The amplitude (related to the energy)
The phase (its ‘position’ relative to other
frequencies)
Amplitude Phase
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Fourier Filtering
The Fourier Transform of an image can be carried out
using:
Software (time-consuming)
Special-purpose hardware (much faster)
using the Discrete Fourier Transform (DFT) method.
The DFT also allows spectral data (i.e. a transformed
image) to be inverse transformed, producing an
image once again.
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Fourier Filtering (continued)
If we compute the DFT of an image, then
immediately inverse transform the result, we expect
to regain the same image.
If we multiply each element of the DFT of an image
by a suitably chosen weighting function we can
accentuate certain frequency components and
attenuate others. The corresponding changes in the
spatial form can be seen after the inverse DFT has
been computed.
The selective enhancement/suppression of frequency
components like this is known as Fourier Filtering.
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Uses of Fourier Filtering
Convolution with large masks (Convolution
Theorem)
Compensate for known image defects (restoration)
Reduction of image noise
Suppression of ‘hum’ or other periodic interference
Reconstruction of 3D data from 2D sections
Many others . . .
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Transforms & Image Compression
Image transforms convert the spatial information of
the image into a different form e.g. fast Fourier
transform (F.F.T.) and discrete cosine transform
(D.C.T.). A value in the output image is dependent
on all pixels of the input image. The calculation of
transforms is very computationally intensive.
Image compression techniques reduce the amount of
data required to store a particular image. Many of the
image compression algorithms rely on the fact the
eye is unable to perceive small changes in an image.
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Other Applications
Image restoration (compensate instrumental aberrations)
Lattice averaging & structure determination (esp. TEM)
Automatic focussing & astigmatism correction
Analysis of diffraction (and other related) patterns
3D measurements, visualisation & reconstruction
Analysis of sections (stereology)
Image data compression, transmission & access
Desktop publishing & multimedia
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Segmentation
The operation of distinguishing important objects
from the background (or from unimportant objects).
Point-dependent methods
Thresholding and semi-thresholding
Adaptive thresholding
Neighbourhood-dependent
Edge enhancement & edge detectors
Boundary tracking
Template matching
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Point-dependent methods
Operate by locating groups of pixels with similar properties.
Thresholding
Assign a threshold grey level which discriminates
between objects and background. This is straightforward
if the image has a bimodal grey-level histogram.
Threshold
Object
Background
ft
f t
f t
255
0
(thresholding)
ft
f f t
f t
0
(semi-thresholding)
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Adaptive thresholding
In practice the GLH is rarely bimodal, owing to:-
Random noise - use LP/median or temporal filtering
Varying illumination
Complex images - objects of different sizes/properties
Grey level profile
Object 1
Object 2
Sloping background
?
?
t2
t1
Adaptive threshold
Background correction
(subtract or divide) may be
applied if an image of the
background alone is available.
Otherwise an adaptive strategy
can be used.
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Neighbourhood-dependent operations
Edge detectors
Highlight region boundaries.
Template matching
Locate groups of pixels in a particular group or
configuration (pattern matching)
Boundary tracking
Locate all pixels lying on an object boundary
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Edge detectors
Most edge enhancement techniques based on HP
filters can be used to highlight region boundaries -
e.g. Gradient, Laplacian. Several masks have been
devised specifically for this purpose, e.g. Roberts and
Sobel operators.
Must consider directional characteristics of mask
Effects of noise may be amplified
Certain edges (e.g. texture edge) not affected
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Template matching
A template is an array of numbers used to detect the
presence of a particular configuration of pixels. They
are applied to images in the same way as convolution
masks.
-1 -1 -1
-1 8 -1
-1 -1 -1
This 3x3 template will
identify isolated objects
consisting of a single pixel
differing in grey-level from
the background.
Other templates can be devised to identify lines or edges in
chosen orientations.
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Boundary tracking
Boundary tracking can be applied to any image
containing only boundary information. Once a single
boundary point is found, the operation seeks to find
all other pixels on that boundary. One approach is
shown:-
1
2
• Find first boundary pixel (1);
• Search 8 neighbours to find (2);
• Search in same direction (allow
deviation of 1 pixel either side);
• Repeat step 3 till end of boundary.
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Connectivity and connected objects
Rules are needed to decide to which object a pixel belongs.
Some situations easily handled, others less straightforward.
It is customary to assume either:
4-connectivity
•a pixel is regarded as connected to its four nearest neighbours
8-connectivity.
•a pixel is regarded as connected to all eight nearest neighbours
1 3 2 1
2 X 0 4 X 0
3 5 6 7
4-connected pixels 8-connected pixels
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Connected components
Results of analysis under 4- or 8- connectivity
A hidden paradox affects object and background pixels
Object - Shaded
Background - Blank
Connectivity A. Objects found B. Objects found C. Objects found
4 1 2 2
8 1 1 2
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Line segment encoding
Objects are represented as collections of chords
A line-by-line technique
Requires access to just two lines at a time
Data compression may also be applied
Feature measurement may be carried out simultaneously
200 1- 1 1-1 is the first segment found.
Obj label Segments Segment labels...
201 1- 2 2- 1 1-2 & 2-1 appear to be separate
objects.
19 11 1-1 1-2 ..
202 1- 3 2- 2
203 1- 4 1-4, 1-5 underlie 1-3, 2-2;
204 1- 5 Hence,1- & 2- are the same
205 1- 6 1- 7 .. 202 311 8 ..
206 1- 8 1- 9 1-6 to 1-9 belong to same object. Row Col Pixels
207
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Representation of objects
Object membership map (OMM)
An image the same size as the original image
Each pixel encodes the corresponding object number,
e.g. all pixels of object 9 are encoded as value 9
Zero represents background pixels
requires an extra, full-size digital image
requires further manipulation to yield feature information
1 1 1 1 2 2 2 2
1 1 1 2 2 2
2
3 3
3 3 3 3 3
4 4 3 3 3 3
4 4 4 3 3 3 5 5
4 4 3 5 5 5
4
Example OMM
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Representation of objects
A compact format for storing object information about an object
Defines only the position of the object boundary
Takes advantage of connected nature of boundaries.
Economical representation; 3 bits/boundary point
Yields some feature information directly
Choose a starting point on the boundary (arbitrary)
One or more nearest neighbours must also be a boundary point
Record the direction codes that specify the path around the boundary
3 2 1
4 0
5 6 7
Start point (204, 463)
Boundary Chain code:
204 463
5660702122435
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Size measurements
Area
A simple, convenient measurement, can be determined during extraction.
The object pixel count, multiplied by the area of a single pixel.
Determined directly from the segment-encoded representation
Additional computation needed for boundary chain code.
Simplified C code example
a = 0; // Initialise area to 0
x = n; y = n; // Arbitrary start coordinates
for (i=0; i<n; i++)
switch (c[i]) // Inspect each element
{ // 0246 are parallel to the axes
case 0: a -= y; x++; break;
case 2: y++; break;
case 4: a += y; x--; break;
case 6: y--; break;
}
printf ("Area is %10.4fn",a);
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Integrated optical density (IOD)
Determined from the original grey scale image.
IOD is rigorously defined for photographic imaging
In digital imaging, taken as sum of all pixel grey levels over the object:
where:
may be derived from the OMM, LSE, or from the BCC.
IOD reflects the mass or weight of the object.
Numerically equal to area multiplied by mean object grey level.
IOD f x y o x y
x
x x
y
y y
, . ,
max
max
0
0
o x y
x y
,
,
1
0
lies within the object
elsewhere
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Length and width
Straightforwardly computed during encoding or tracking.
Record coordinates:
•minimum x
•maximum x
•minimum y
•maximum y
Take differences to give:
•horizontal extent
•vertical extent
•minimum boundary rectangle.
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Perimeter
May be computed crudely from the BCC simply by counting pixels
More accurately, take centre-to-centre distance of boundary pixels
For the BCC, perimeter, P, may be written
where:-
NE is the number of even steps
NO is the number of odd steps
taken in navigating the boundary.
Dependence on magnification is a difficult problem
Consider area and perimeter measurements at two magnifications:
•Area will remain constant
•Perimeter invariably increases with magnification
Presence of holes can also affect the measured perimeter
P N N
E O
2
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Number of holes
Hole count may be of great value in classification.
A fundamental relationship exists between:-
•the number of connected components C (i.e. objects)
•the number of holes H in a figure
•and the Euler number:-
E = C - H
A number of approaches exist for determining H.
Count special motifs (known as bit quads) in objects.
These can give information about:-
•Area
•Perimeter
•Euler number
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Bit-quad codes
Q0 0 0 Q4 1 1 QD 1 0 0 1
0 0 1 1 0 1 1 0
Q1 1 0 0 1 0 0 0 0
0 0 0 0 0 1 1 0
Q2 1 1 0 1 0 0 1 0
0 0 0 1 1 1 1 0
Q3 1 1 0 1 1 0 1 1
0 1 1 1 1 1 1 0
A n Q n Q n Q n Q n QD
1
4 1
1
2 2
7
8 3 4
3
4
P n Q n Q n Q n QD
2
1
2 1 3 2
E n Q n Q n QD
1
4 1 3 2
For 1 object alone, H = 1 - E
Disposition of the 16 bit-quad motifs Equations for A, P and E
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Derived features
For example, shape features
Rectangularity
Ratio of object area A to area AE of minimum enclosing rectangle
Expresses how efficiently the object fills the MER
Value must be between 0 and 1.
For circular objects it is
Becomes small for curved, thin objects.
Aspect ratio
The width/length ratio of the minimum enclosing rectangle
Can distinguish slim objects from square/circular objects
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Derived features (cont)
Circularity
Assume a minimum value for circular shape
High values tend to reflect complex boundaries.
One common measure is:
C = P2/A
(ratio of perimeter squared to area)
takes a minimum value of 4p for a circular shape.
Warning: value may vary with magnification
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Derived measurements (cont)
Boundary energy is derived from the curvature of the boundary.
Let the instantaneous radius of curvature be r(p),p along the boundary.
The curvature function K(p) is defined:
This is periodic with period P, the boundary perimeter.
The average energy for the boundary can be written:
A circular boundary has minimum boundary energy given by:
where R is the radius of the circle.
K p
r p
1
E
P
K p
P
1 2
0
dp
E
P R
0
2 2
2 1
p
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Texture analysis
Repetitive structure cf. tiled floor, fabric
How can this be analysed quantitatively?
One possible solution based on edge detectors:-
determine orientation of gradient vector at each pixel
quantise to (say) 1 degree intervals
count the number of occurrences of each angle
plot as a polar histogram:
radius vector a number of occurrences
angle corresponds to gradient orientation
Amorphous images give roughly circular plots
Directional, patterned images may give elliptical plots
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Texture analysis (cont)
Simple expression for angle gives noisy histograms
Extended expression for q gives greater accuracy
Resultant histogram has smoother outline
Requires larger neighbourhood, and longer computing time
Extended approximation
V
R
W S P Q U
T
Z
q
arctan
8
8
f f f f
f f f f
R T V Z
Q S U W
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3D Measurements
Most imaging systems are 2D; many specimens are 3D.
How can we extract the information?
Photogrammetry - standard technique for cartography
TILT
PARALLAX
(X,Y,Z coords)
R
L
Either the specimen, or the electron beam can be tilted.
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Visualisation of height & depth
Seeing 3D images requires the following:-
Stereo pair images
Shift the specimen (low mag. only)
Tilt specimen (or beam) through angle a
Viewing system
lens/prism viewers
mirror-based stereoscope
twin projectors
anaglyph presentation (red & green/cyan)
LCD polarising shutter, polarised filters
Stereopsis - ability to fuse stereo-pair images
3D reconstruction (using projection, Fourier, or other methods)
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Measurement of height & depth
Measurement by processing of parallax measurements
Three cases:-
Low magnification, shift only parallax
Low magnification, tilt only
High magnification, tilt only (simple case)
requires xL, xR, tilt change a and magnification M
L
L R
R
L R
Z
x x
Z
x x
tan sin
sin tan
a a
a a
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Computer-based system for SEM
Acquisition of stereo-pair images
Recording of operating parameters
Correction for distortion
Computation of 3D values from parallax
Gun
Detector
Obj Lens
Scan Coils
SEM and
Lens control
Image Capture
Framestore
Host
Computer
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3D by Automatic Focussing
Gun
Detector
Obj Lens
Scan Coils
Lens control
16-bit DAC
8-bit ADC
Host
Computer
Scan waveforms
Twin 12-bit DAC
Computer SEM
w1 w2 Final Aperture
Specimen
Deflection coils
Electron beam
Objective lens
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Combined Stereo/Autofocus
Electron beam
Sample tilting Analogous beam tilting
Sample tilting is simple but awkward to implement
Beam tilting allows real time viewing but requires
extra stereo tilt deflection coils in the SEM column
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Novel Beam Tilt method
Uses Gun Alignment coils
No extra deflection coils
required
Tilt axis follows focal plane
of final lens with changes in
working distance
No restriction on working
distance
Specimen
Final Lens
Microshift coils
Condenser Lens 2
Condenser Lens 1
Gun Alignment coils
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Measurement Technique
In situ measurement technique
Beam tilt axis lies in focal plane of final lens
Features above/below focal plane are laterally displaced
Features are made to coincide
By changing excitation/focus of lens
Change in excitation gives measure of relative vertical
displacements between image features
Can readily be automated
by use of a computer to control lenses and determine feature
coincidence
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Automated height measurement
System determines:-
spot heights
line profiles
area topography map
contour map
Display shows a line profile taken across a 1 mm polysilicon track
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Remote Microscopy
Modern SEMs are fully computer-controlled
instruments
Networking to share resources - information,
hardware, software
The Internet explosion & related tools
Don’t Commute --- Communicate!
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Automated Diagnosis for SEM
Fault diagnosis of SEM
Too much expertise required
Hard to retain expertise
Verbal descriptions of symptoms often ambiguous
Geographical dispersion increases costs.
Amenable to the Expert System approach.
A computer program demonstrating expert
performance on a well-defined task
Should explain its answers, reason judgementally
and allow its knowledge to be examined and
modified
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Remote Diagnosis
Stages in development
Knowledge acquisition from experts, manuals and
service reports
Knowledge representation --- translation into a
formal notation
Implementation as custom expert system
Integration of ES with the Internet and RM
Conclusions
RM offers accurate information and SEM control
ES provides engineer with valuable knowledge
ES + RM = Effective Remote Diagnosis
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Image Processing Platforms
Low cost memory has resulted in computer
workstations having large amounts of memory and
being capable of storing images.
Graphics screens now have high resolutions and
many colours, and many are of sufficient quality to
display images.
However, two problems still remain for image
processing:
Getting images into the system.
Processing power.
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Parallel Processing
Many image processing operations involve repeating
the same calculation repeatedly on different parts of
the image. This makes these operations suitable for a
parallel processing implementation.
The most well known example of parallel computing
platforms is the transputer. The transputer is a
microprocessor which is able to communicate with
other transputers via communications links.
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Parallel Processing contd.
The speed increase is not linear as the number of
processing elements increases, due to a
communications overhead.
Number of Processors
Performance
Actual
Linear
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Windowed Video Displays
Windowed video hardware allows live video
pictures to be displayed within a window on the
computer display.
This is achieved by superimposing the live video
signal on the computer display output.
The video must be first rescaled, cropped and
repositioned so that it appears in the correct window
in the display. Rescaling is most easily performed by
missing out lines or pixels according to the direction.
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Windowed Video Displays contd.
MEMORY
COMPUTER
INTERFACE
FRAME
CONTROLLER
COMPUTER
DISPLAY
DRIVER
CONTROL
CONTROL
OUTPUT
DAC
SWITCH
OUTPUT TO
COMPUTER
DISPLAY
SCALE
ADC
VIDEO
IN
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Framestores - conclusion
The framestore is an important part of any image
processing system, allowing images to be captured
and stored for access by a computer. A framestore
and computer combination provides a very flexible
image processing system.
Real time image processing operations such as
recursive averaging and background correction
require a processing facility to be integrated into the
framestore.
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Digital Imaging
ComputersinImage Processing and Analysis
SEM and X-ray Microanalysis, 8-11 September 1997
David Holburn University Engineering Department, Cambridge
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Image Processing & Restoration
IEE Image Processing Conference, July 1995
David Holburn University Engineering Department, Cambridge
Owen Saxton University Dept of Materials Science & Metallurgy, Cambridge