Presentation of my senior Project about "A real time automatic eye tracking system for ophthalmology"
In the presentation, it briefly explains about conventional object tracking method "template matching" based on Sum-of-Square difference. Therefore we also present the powerful matching technique called Gradient Orientation Pattern Matching (GOPM) proposed by T.Kondo and we proposed an improved version of GOPM called time-vary GOPM to solve a illumination and noise problem.
Multi Image Deblurring using Complementary Sets of Fluttering Patterns by Mul...IRJET Journal
This document discusses a proposed method for multi-image deblurring using complementary sets of fluttering patterns and an alternating direction multiplier method. Existing methods for coded exposure and multi-image deblurring have limitations like generating complex fluttering patterns, low signal-to-noise ratio, and loss of spectral information. The proposed method uses a multiplier algorithm to optimize a latent image and generate simple binary fluttering patterns for single or multiple input images. This helps reduce spectral loss and recover spatially consistent deblurred images with minimum noise. The method involves preprocessing the input image, setting regularization parameters, performing deconvolution iteratively using matrices, and outputting a deblurred image with sharp details and low noise.
FPGA Implementation of Glaucoma Detection using Neural NetworksIRJET Journal
This document describes a study that implemented glaucoma detection using neural networks on an FPGA. The key steps were:
1. Features were extracted from retinal images including optic disk area, cup area, and neuro-retinal rim area. These features were used as inputs to the neural network.
2. A feedforward backpropagation neural network was trained to classify images as glaucoma or healthy based on the extracted features.
3. The neural network was implemented on a Spartan 3A FPGA to take advantage of its reconfigurability and parallel processing capabilities for neural networks.
4. Testing on sample images from a fundus image database achieved accurate classification of glaucoma and healthy
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Iaetsd a review on modified anti forensicIaetsd Iaetsd
This document proposes a new anti-forensic technique to remove detectable traces from digitally manipulated images. The technique aims to hide evidence of image compression and filtering by adding specially designed noise called "tailored noise" to the image after processing. Existing digital forensic techniques analyze images to determine processing history and detect tampering, but new anti-forensic methods are needed to understand weaknesses. The proposed technique could remove evidence of JPEG compression and filtering by distributing image coefficients and matching the distribution to estimation models using tailored noise. This would allow covering a history of processing and removing signature traces, hindering forensic analysis of image authenticity.
This document is the master's thesis of Jani Huhtanen submitted to the Department of Information Technology at Tampere University of Technology. The thesis compares several lossless image compression algorithms and develops a new algorithm for compressing multi- and hyperspectral imagery. The proposed algorithm uses wavelet transforms and novel inter-band adaptive prediction to achieve high compression ratios while maintaining low complexity and flexibility for hyperspectral data applications. Results show the proposed algorithm provides up to 20% higher compression ratios than JPEG2000 for some hyperspectral images due to its more sophisticated exploitation of inter-band dependencies. Overall, the algorithm proves competitive with other techniques in terms of compression performance and features such as resolution scalability and random access.
The document describes the CEDCT (Continuous Extension of the Discrete Orbit Function Transform) interpolation algorithm. CEDCT is based on Lie groups and can be applied for 2D and 3D image and data interpolation. It has advantages over other interpolation methods like bilinear and bicubic in terms of speed and reducing artifacts like aliasing, blurring, and edge halos through adaptive filtering. Examples show CEDCT better preserves details and textures compared to other methods for image and MRI data interpolation.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
Multi Image Deblurring using Complementary Sets of Fluttering Patterns by Mul...IRJET Journal
This document discusses a proposed method for multi-image deblurring using complementary sets of fluttering patterns and an alternating direction multiplier method. Existing methods for coded exposure and multi-image deblurring have limitations like generating complex fluttering patterns, low signal-to-noise ratio, and loss of spectral information. The proposed method uses a multiplier algorithm to optimize a latent image and generate simple binary fluttering patterns for single or multiple input images. This helps reduce spectral loss and recover spatially consistent deblurred images with minimum noise. The method involves preprocessing the input image, setting regularization parameters, performing deconvolution iteratively using matrices, and outputting a deblurred image with sharp details and low noise.
FPGA Implementation of Glaucoma Detection using Neural NetworksIRJET Journal
This document describes a study that implemented glaucoma detection using neural networks on an FPGA. The key steps were:
1. Features were extracted from retinal images including optic disk area, cup area, and neuro-retinal rim area. These features were used as inputs to the neural network.
2. A feedforward backpropagation neural network was trained to classify images as glaucoma or healthy based on the extracted features.
3. The neural network was implemented on a Spartan 3A FPGA to take advantage of its reconfigurability and parallel processing capabilities for neural networks.
4. Testing on sample images from a fundus image database achieved accurate classification of glaucoma and healthy
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Iaetsd a review on modified anti forensicIaetsd Iaetsd
This document proposes a new anti-forensic technique to remove detectable traces from digitally manipulated images. The technique aims to hide evidence of image compression and filtering by adding specially designed noise called "tailored noise" to the image after processing. Existing digital forensic techniques analyze images to determine processing history and detect tampering, but new anti-forensic methods are needed to understand weaknesses. The proposed technique could remove evidence of JPEG compression and filtering by distributing image coefficients and matching the distribution to estimation models using tailored noise. This would allow covering a history of processing and removing signature traces, hindering forensic analysis of image authenticity.
This document is the master's thesis of Jani Huhtanen submitted to the Department of Information Technology at Tampere University of Technology. The thesis compares several lossless image compression algorithms and develops a new algorithm for compressing multi- and hyperspectral imagery. The proposed algorithm uses wavelet transforms and novel inter-band adaptive prediction to achieve high compression ratios while maintaining low complexity and flexibility for hyperspectral data applications. Results show the proposed algorithm provides up to 20% higher compression ratios than JPEG2000 for some hyperspectral images due to its more sophisticated exploitation of inter-band dependencies. Overall, the algorithm proves competitive with other techniques in terms of compression performance and features such as resolution scalability and random access.
The document describes the CEDCT (Continuous Extension of the Discrete Orbit Function Transform) interpolation algorithm. CEDCT is based on Lie groups and can be applied for 2D and 3D image and data interpolation. It has advantages over other interpolation methods like bilinear and bicubic in terms of speed and reducing artifacts like aliasing, blurring, and edge halos through adaptive filtering. Examples show CEDCT better preserves details and textures compared to other methods for image and MRI data interpolation.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
Survey on Image Integration of Misaligned ImagesIRJET Journal
The document discusses methods for integrating misaligned images to improve image quality under low lighting conditions. It reviews previous works that combine images like flash/no-flash pairs to transfer details and color, but have limitations when images are misaligned. The paper proposes a new method using a long-exposure image and flash image that introduces a local linear model to transfer color while maintaining natural colors and high contrast, without deteriorating contrast for misaligned pairs. It concludes that handling misaligned images remains a challenge with existing methods and further work is needed.
IRJET-Retina Image Decomposition using Variational Mode DecompositionIRJET Journal
This document describes research applying the variational mode decomposition (VMD) algorithm to decompose retina images. VMD is presented as an improvement over existing empirical mode decomposition methods as it is less sensitive to noise and frequencies. The researchers apply VMD to decompose a retina image into intrinsic mode functions (IMFs) representing different frequency bands. Texture features are extracted from the IMFs and used to classify retina images as healthy or unhealthy, achieving perfect detection. Hardware implementation of the VMD algorithm on an FPGA is also discussed to improve computational speed for potential medical applications in disease diagnosis.
The document describes a methodology for a-priori error estimation of Particle Image Velocimetry (PIV) measurements. It involves generating synthetic images with similar characteristics as experimental images such as particle image diameter and image density. The error estimated from synthetic images is then validated against experimental images. A-priori error estimation is then performed as a function of relevant experimental parameters like particle image diameter, seeding density, displacement gradient, and out-of-plane motion.
The document describes the OrtHOPHOs XG 3D, a new hybrid x-ray machine from Sirona that combines 2D and 3D imaging capabilities. It has a simple, intuitive interface and requires similar space as a traditional 2D machine. The OrtHOPHOs XG 3D increases diagnostic accuracy for clinical needs like implant planning and offers low radiation dose. It integrates with CEREC for comprehensive digital planning and workflow.
This document summarizes a research paper that proposes a method for detecting glaucoma and exudates in retinal images. The key steps are:
1. Extracting texture features from retinal images using discrete wavelet transforms with different wavelet filters. This decomposes images into approximation and detailed coefficients.
2. Calculating energy signatures from the wavelet coefficients as features.
3. Classifying images as normal or glaucomatous using a probabilistic neural network trained on the energy features.
4. Segmenting exudates from abnormal images using k-means clustering applied to the wavelet coefficients.
The goal is to develop an automated system to analyze retinal images, classify them,
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
IRJET- Retinal Health Diagnosis using Image ProcessingIRJET Journal
The document presents a method for detecting glaucoma from fundus images using image processing and machine learning techniques. Fundus images of normal and glaucomatic eyes are preprocessed and the region of interest containing the optic disc is extracted. The optic disc and cup are segmented using adaptive thresholding. The neuroretinal rim is obtained by subtracting the cup from the disc. Features like cup-to-disc ratio and rim-to-disc ratio are extracted and used to train a support vector machine (SVM) classifier to classify images as normal or glaucomatic. Testing on fundus images achieved an accuracy of 90%, sensitivity of 98% and specificity of 80%.
Tomosurgery is a new radiosurgery treatment planning approach that separates the tumor volume into planar slices and treats each slice with a continuous "moving shot" raster pattern to optimize dose distribution. The student aims to implement the Tomosurgery algorithm on a Gamma Knife platform by translating the MATLAB code to be compatible with Gamma Knife software. The goals are to successfully deliver a Tomosurgery plan to a phantom, compare the dose accuracy and delivery times to previous patient plans, and evaluate the utility of Tomosurgery for real-world use. Challenges include the Gamma Knife's inability to move the beam or create a disk-shaped shot.
This document proposes a technique called Automatic Dynamic Depth Focusing (ADDF) for phased array non-destructive testing. The technique estimates the geometry between the ultrasound array probe and test part using time-of-flight measurements from initial trigger shots. It then calculates a "virtual array" that operates as if in a homogeneous medium, avoiding complications from the real interface. Using the virtual array, it computes initial focusing parameters for real-time dynamic focusing during scanning. The overall procedure takes about 2 seconds, providing fully automated focusing faster than existing geometry-based calculators.
Image Compression based on DCT and BPSO for MRI and Standard ImagesIJERA Editor
Nowadays, digital image compression has become a crucial factor of modern telecommunication systems. Image compression is the process of reducing total bits required to represent an image by reducing redundancies while preserving the image quality as much as possible. Various applications including internet, multimedia, satellite imaging, medical imaging uses image compression in order to store and transmit images in an efficient manner. Selection of compression technique is an application-specific process. In this paper, an improved compression technique based on Butterfly-Particle Swarm Optimization (BPSO) is proposed. BPSO is an intelligence-based iterative algorithm utilized for finding optimal solution from a set of possible values. The dominant factors of BPSO over other optimization techniques are higher convergence rate, searching ability and overall performance. The proposed technique divides the input image into 88 blocks. Discrete Cosine Transform (DCT) is applied to each block to obtain the coefficients. Then, the threshold values are obtained from BPSO. Based on this threshold, values of the coefficients are modified. Finally, quantization followed by the Huffman encoding is used to encode the image. Experimental results show the effectiveness of the proposed method over the existing method.
Augmented Reality in Volumetric Medical Imaging Using Stereoscopic 3D Display ijcga
This paper is written about augmented reality in medicine. Medical imaging equipment (CT, PET, MRI) are produced 3D volumetric data, so using the stereoscopic 3D display, observer feels depth perception. The major factors about depth-Convergence, Accommodation, Relative size are tested. Convergence and Accommodation have affected depth perception but relative size is negligible.
IRJET - A Review on Gradient Histograms for Texture Enhancement and Objec...IRJET Journal
This document discusses image deblurring and object detection techniques. It first reviews existing methods for image deblurring that use priors like gradient priors and sparse priors. It then proposes a new deblurring algorithm called GHPD that combines gradient histogram preservation with non-local sparse representation to better enhance textures while reducing noise and artifacts. After deblurring, histogram of oriented gradients (HOG) and support vector machines (SVM) are used to extract features and detect objects in the deblurred images. The algorithm is able to better detect objects by preserving textures during the deblurring process.
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
1) The document discusses a technique for detecting bone fractures in x-ray images using edge detection methods like Gaussian and Canny edge detection.
2) It involves preprocessing the x-ray image, applying Gaussian filtering to remove noise, using Canny edge detection to identify edges, and inverting the image to make fractures more visible.
3) The method is implemented using the AForge library and is found to accurately detect bone fractures in x-ray images for use in medical applications like aiding doctors' diagnoses.
Implementation of Lower Leg Bone Fracture Detection from X Ray Imagesijtsrd
A methodology and various techniques are presented for development of fracture detection system using digital image processing. This paper presents an implementation of bone fracture detection using medical X ray image. The goal of this paper is to generate a quick and diagnosis can save time, effort and cost as well as reduce errors. The main objective of this research is to classify the lower leg bone fracture using X ray image because this type of bone are the most commonly occur in bone. This paper classifies the two types of fracture Non fracture and fracture or Transverse fracture by using SVM classifier. The proposed system has basically five steps, namely, image acquisition, image pre processing, image segmentation, feature extraction and image classification. San Myint | Khaing Thinzar "Implementation of Lower Leg Bone Fracture Detection from X-Ray Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27957.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/27957/implementation-of-lower-leg-bone-fracture-detection-from-x-ray-images/san-myint
Rician Noise Reduction with SVM and Iterative Bilateral Filter in Different T...IJMERJOURNAL
ABSTRACT: Parallel magnetic resonance imaging (pMRI) techniques can speed up MRI scan through a multi-channel coil array receiving signal simultaneously. Nevertheless, noise amplification and aliasing artifacts are serious in pMRI reconstructed images at high accelerations. Image Denoising is one of the most challenging task because image denoising techniques not only poised some technical difficulties, but also may result in the destruction of the image (i.e. making it blur) if not effectively and adequately applied to image. This study presents a patch-wise de-noising method for pMRI by exploiting the rank deficiency of multi-Channel coil images and sparsity of artifacts. For each processed patch, similar patches a researched in spatial domain and through-out all coil elements, and arranged in appropriate matrix forms. Then, noise and aliasing artifacts are removed from the structured Matrix by applying sparse and low rank matrix decomposition method. The proposed method has been validated using both phantom and in vivo brain data sets, producing encouraging results. Specifically, the method can effectively remove both noise and residual aliasing artifact from pMRI reconstructed noisy images, and produce higher peak signal noise rate (PSNR) and structural similarity index matrix (SSIM) than other state-of-the-art De-noising methods. We propose image de-noising using low rank matrix decomposition (LMRD) and Support vector machine (SVM). The aim of Low Rank Matrix approximation based image enhancement is that it removes the various types of noises in the contaminated image simultaneously. The main contribution is to explore the image denoising low-rank property and the applications of LRMD for enhanced image Denoising, Then support vector machine is applied over the result.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Purkinje imaging for crystalline lens density measurementPetteriTeikariPhD
Brief introduction for the non-invasive, inexpensive and fast Purkinje image -based method for measuring the spectral transmittance of the human crystalline lens density in vivo.
Alternative download link:
https://www.dropbox.com/s/588y7epy13n34xo/purkinje_imaging.pdf?dl=0
This document discusses using deep learning techniques like convolutional neural networks, autoencoders, and variational autoencoders to perform false coloring of satellite images. The techniques are implemented in Python using frameworks like Keras and TensorFlow. CNNs achieved 92% accuracy, autoencoders achieved 90% accuracy, and variational autoencoders achieved 92% accuracy in converting grayscale satellite images to color images. The codes for implementing each technique are also included.
The frame camera is used by users of digital single-lens reflex cameras (DSLRs) as a shorthand for an image sensor format which is the same size as 35mm format (36 mm × 24 mm) film.
Panoramic imagery is created either by digitally stitching together multiple images from the same position (left/right, up/down) or by rotating a camera with conventional optics, and an area or line sensor.
NEW IMPROVED 2D SVD BASED ALGORITHM FOR VIDEO CODINGcscpconf
Video compression is one of the most important blocks of an image acquisition system.
Compression of video results in reduction of transmission bandwidth. In real time video
compression the incoming video data is directly compressed without being stored first.
Therefore real time video compression system operates under stringent timing constraints.
Current video compression standards like MPEG, H.26x series, involve emotion estimation and
compensation blocks which are highly computationally expensive and hence they are not
suitable for real time applications on resource scarce systems. Current applications like video
calling, video conferencing require low complexity video compression algorithms so that they
can be implemented in environments that have scarce computational resources (like mobile
phones). A low complexity video compression algorithm based on 2D SVD exists. In this paper, a modification to that algorithm which provides higher PSNR at the same bit rate is presented.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
Survey on Image Integration of Misaligned ImagesIRJET Journal
The document discusses methods for integrating misaligned images to improve image quality under low lighting conditions. It reviews previous works that combine images like flash/no-flash pairs to transfer details and color, but have limitations when images are misaligned. The paper proposes a new method using a long-exposure image and flash image that introduces a local linear model to transfer color while maintaining natural colors and high contrast, without deteriorating contrast for misaligned pairs. It concludes that handling misaligned images remains a challenge with existing methods and further work is needed.
IRJET-Retina Image Decomposition using Variational Mode DecompositionIRJET Journal
This document describes research applying the variational mode decomposition (VMD) algorithm to decompose retina images. VMD is presented as an improvement over existing empirical mode decomposition methods as it is less sensitive to noise and frequencies. The researchers apply VMD to decompose a retina image into intrinsic mode functions (IMFs) representing different frequency bands. Texture features are extracted from the IMFs and used to classify retina images as healthy or unhealthy, achieving perfect detection. Hardware implementation of the VMD algorithm on an FPGA is also discussed to improve computational speed for potential medical applications in disease diagnosis.
The document describes a methodology for a-priori error estimation of Particle Image Velocimetry (PIV) measurements. It involves generating synthetic images with similar characteristics as experimental images such as particle image diameter and image density. The error estimated from synthetic images is then validated against experimental images. A-priori error estimation is then performed as a function of relevant experimental parameters like particle image diameter, seeding density, displacement gradient, and out-of-plane motion.
The document describes the OrtHOPHOs XG 3D, a new hybrid x-ray machine from Sirona that combines 2D and 3D imaging capabilities. It has a simple, intuitive interface and requires similar space as a traditional 2D machine. The OrtHOPHOs XG 3D increases diagnostic accuracy for clinical needs like implant planning and offers low radiation dose. It integrates with CEREC for comprehensive digital planning and workflow.
This document summarizes a research paper that proposes a method for detecting glaucoma and exudates in retinal images. The key steps are:
1. Extracting texture features from retinal images using discrete wavelet transforms with different wavelet filters. This decomposes images into approximation and detailed coefficients.
2. Calculating energy signatures from the wavelet coefficients as features.
3. Classifying images as normal or glaucomatous using a probabilistic neural network trained on the energy features.
4. Segmenting exudates from abnormal images using k-means clustering applied to the wavelet coefficients.
The goal is to develop an automated system to analyze retinal images, classify them,
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
IRJET- Retinal Health Diagnosis using Image ProcessingIRJET Journal
The document presents a method for detecting glaucoma from fundus images using image processing and machine learning techniques. Fundus images of normal and glaucomatic eyes are preprocessed and the region of interest containing the optic disc is extracted. The optic disc and cup are segmented using adaptive thresholding. The neuroretinal rim is obtained by subtracting the cup from the disc. Features like cup-to-disc ratio and rim-to-disc ratio are extracted and used to train a support vector machine (SVM) classifier to classify images as normal or glaucomatic. Testing on fundus images achieved an accuracy of 90%, sensitivity of 98% and specificity of 80%.
Tomosurgery is a new radiosurgery treatment planning approach that separates the tumor volume into planar slices and treats each slice with a continuous "moving shot" raster pattern to optimize dose distribution. The student aims to implement the Tomosurgery algorithm on a Gamma Knife platform by translating the MATLAB code to be compatible with Gamma Knife software. The goals are to successfully deliver a Tomosurgery plan to a phantom, compare the dose accuracy and delivery times to previous patient plans, and evaluate the utility of Tomosurgery for real-world use. Challenges include the Gamma Knife's inability to move the beam or create a disk-shaped shot.
This document proposes a technique called Automatic Dynamic Depth Focusing (ADDF) for phased array non-destructive testing. The technique estimates the geometry between the ultrasound array probe and test part using time-of-flight measurements from initial trigger shots. It then calculates a "virtual array" that operates as if in a homogeneous medium, avoiding complications from the real interface. Using the virtual array, it computes initial focusing parameters for real-time dynamic focusing during scanning. The overall procedure takes about 2 seconds, providing fully automated focusing faster than existing geometry-based calculators.
Image Compression based on DCT and BPSO for MRI and Standard ImagesIJERA Editor
Nowadays, digital image compression has become a crucial factor of modern telecommunication systems. Image compression is the process of reducing total bits required to represent an image by reducing redundancies while preserving the image quality as much as possible. Various applications including internet, multimedia, satellite imaging, medical imaging uses image compression in order to store and transmit images in an efficient manner. Selection of compression technique is an application-specific process. In this paper, an improved compression technique based on Butterfly-Particle Swarm Optimization (BPSO) is proposed. BPSO is an intelligence-based iterative algorithm utilized for finding optimal solution from a set of possible values. The dominant factors of BPSO over other optimization techniques are higher convergence rate, searching ability and overall performance. The proposed technique divides the input image into 88 blocks. Discrete Cosine Transform (DCT) is applied to each block to obtain the coefficients. Then, the threshold values are obtained from BPSO. Based on this threshold, values of the coefficients are modified. Finally, quantization followed by the Huffman encoding is used to encode the image. Experimental results show the effectiveness of the proposed method over the existing method.
Augmented Reality in Volumetric Medical Imaging Using Stereoscopic 3D Display ijcga
This paper is written about augmented reality in medicine. Medical imaging equipment (CT, PET, MRI) are produced 3D volumetric data, so using the stereoscopic 3D display, observer feels depth perception. The major factors about depth-Convergence, Accommodation, Relative size are tested. Convergence and Accommodation have affected depth perception but relative size is negligible.
IRJET - A Review on Gradient Histograms for Texture Enhancement and Objec...IRJET Journal
This document discusses image deblurring and object detection techniques. It first reviews existing methods for image deblurring that use priors like gradient priors and sparse priors. It then proposes a new deblurring algorithm called GHPD that combines gradient histogram preservation with non-local sparse representation to better enhance textures while reducing noise and artifacts. After deblurring, histogram of oriented gradients (HOG) and support vector machines (SVM) are used to extract features and detect objects in the deblurred images. The algorithm is able to better detect objects by preserving textures during the deblurring process.
An Efficient Thresholding Neural Network Technique for High Noise Densities E...CSCJournals
Medical images when infected with high noise densities lose usefulness for diagnosis and early detection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinear function have been widely used to improve the efficiency of the denoising procedure. This paper introduces better solution for medical images in noisy environments which serves in early detection of breast cancer tumor. The proposed algorithm is based on two consecutive phases. Image denoising, where an adaptive learning TNN with remarkable time improvement and good image quality is introduced. A semi-automatic segmentation to extract suspicious regions or regions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of data is then applied to show algorithm superior image quality and complexity reduction especially in high noisy environments.
1) The document discusses a technique for detecting bone fractures in x-ray images using edge detection methods like Gaussian and Canny edge detection.
2) It involves preprocessing the x-ray image, applying Gaussian filtering to remove noise, using Canny edge detection to identify edges, and inverting the image to make fractures more visible.
3) The method is implemented using the AForge library and is found to accurately detect bone fractures in x-ray images for use in medical applications like aiding doctors' diagnoses.
Implementation of Lower Leg Bone Fracture Detection from X Ray Imagesijtsrd
A methodology and various techniques are presented for development of fracture detection system using digital image processing. This paper presents an implementation of bone fracture detection using medical X ray image. The goal of this paper is to generate a quick and diagnosis can save time, effort and cost as well as reduce errors. The main objective of this research is to classify the lower leg bone fracture using X ray image because this type of bone are the most commonly occur in bone. This paper classifies the two types of fracture Non fracture and fracture or Transverse fracture by using SVM classifier. The proposed system has basically five steps, namely, image acquisition, image pre processing, image segmentation, feature extraction and image classification. San Myint | Khaing Thinzar "Implementation of Lower Leg Bone Fracture Detection from X-Ray Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27957.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/27957/implementation-of-lower-leg-bone-fracture-detection-from-x-ray-images/san-myint
Rician Noise Reduction with SVM and Iterative Bilateral Filter in Different T...IJMERJOURNAL
ABSTRACT: Parallel magnetic resonance imaging (pMRI) techniques can speed up MRI scan through a multi-channel coil array receiving signal simultaneously. Nevertheless, noise amplification and aliasing artifacts are serious in pMRI reconstructed images at high accelerations. Image Denoising is one of the most challenging task because image denoising techniques not only poised some technical difficulties, but also may result in the destruction of the image (i.e. making it blur) if not effectively and adequately applied to image. This study presents a patch-wise de-noising method for pMRI by exploiting the rank deficiency of multi-Channel coil images and sparsity of artifacts. For each processed patch, similar patches a researched in spatial domain and through-out all coil elements, and arranged in appropriate matrix forms. Then, noise and aliasing artifacts are removed from the structured Matrix by applying sparse and low rank matrix decomposition method. The proposed method has been validated using both phantom and in vivo brain data sets, producing encouraging results. Specifically, the method can effectively remove both noise and residual aliasing artifact from pMRI reconstructed noisy images, and produce higher peak signal noise rate (PSNR) and structural similarity index matrix (SSIM) than other state-of-the-art De-noising methods. We propose image de-noising using low rank matrix decomposition (LMRD) and Support vector machine (SVM). The aim of Low Rank Matrix approximation based image enhancement is that it removes the various types of noises in the contaminated image simultaneously. The main contribution is to explore the image denoising low-rank property and the applications of LRMD for enhanced image Denoising, Then support vector machine is applied over the result.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Purkinje imaging for crystalline lens density measurementPetteriTeikariPhD
Brief introduction for the non-invasive, inexpensive and fast Purkinje image -based method for measuring the spectral transmittance of the human crystalline lens density in vivo.
Alternative download link:
https://www.dropbox.com/s/588y7epy13n34xo/purkinje_imaging.pdf?dl=0
This document discusses using deep learning techniques like convolutional neural networks, autoencoders, and variational autoencoders to perform false coloring of satellite images. The techniques are implemented in Python using frameworks like Keras and TensorFlow. CNNs achieved 92% accuracy, autoencoders achieved 90% accuracy, and variational autoencoders achieved 92% accuracy in converting grayscale satellite images to color images. The codes for implementing each technique are also included.
The frame camera is used by users of digital single-lens reflex cameras (DSLRs) as a shorthand for an image sensor format which is the same size as 35mm format (36 mm × 24 mm) film.
Panoramic imagery is created either by digitally stitching together multiple images from the same position (left/right, up/down) or by rotating a camera with conventional optics, and an area or line sensor.
NEW IMPROVED 2D SVD BASED ALGORITHM FOR VIDEO CODINGcscpconf
Video compression is one of the most important blocks of an image acquisition system.
Compression of video results in reduction of transmission bandwidth. In real time video
compression the incoming video data is directly compressed without being stored first.
Therefore real time video compression system operates under stringent timing constraints.
Current video compression standards like MPEG, H.26x series, involve emotion estimation and
compensation blocks which are highly computationally expensive and hence they are not
suitable for real time applications on resource scarce systems. Current applications like video
calling, video conferencing require low complexity video compression algorithms so that they
can be implemented in environments that have scarce computational resources (like mobile
phones). A low complexity video compression algorithm based on 2D SVD exists. In this paper, a modification to that algorithm which provides higher PSNR at the same bit rate is presented.
1. The document discusses using deep learning techniques for surface defect detection, focusing on strategies for dealing with imbalanced training data.
2. It proposes using generative adversarial networks (GANs) to generate synthetic defect samples in order to address the class imbalance problem. Convolutional neural networks (CNNs) are then used for classification.
3. Autoencoding models like convolutional autoencoders (CAE) and variational autoencoders (VAE) can also be used for unsupervised defect detection based on image reconstruction.
Realtime pothole detection system using improved CNN Modelsnithinsai2992
The document summarizes work on a real-time pothole detection system using improved CNN models. It discusses using the YOLOv5 model for pothole detection and training YOLOv5m6, YOLOv5s6, and YOLOv5n6 models on a dataset, achieving mAP scores of 80.8%, 82.2%, and 82.5% respectively. It also proposes further improving the system through techniques like better image processing during nighttime and enhancing detection of distant objects.
Machine learning approaches are being explored for video compression. Conservative approaches replace individual MPEG blocks with deep learning blocks, while disruptive end-to-end approaches aim to replace the entire MPEG chain. Optical flow networks can exploit temporal redundancy by estimating motion between frames. Fully neural network-based video compression models jointly learn motion estimation, motion compression, and residual compression in an end-to-end optimized framework. However, performance gains must be balanced against increased complexity, and neural network approaches are not yet mature enough to be included in video compression standards.
Iec 62676 5 standardized spezification of image qualityHenry Rutjes
This document discusses IEC 62676-5, a standard for specifying image quality performance metrics for camera devices used in security applications. It outlines several methods defined in the standard for evaluating key image quality parameters such as resolution, optical/electronic characteristic function (OECF), dynamic range, minimum illumination, visible dynamic range, infrared illumination operating distance, image distortion, and image flare. The intention of the standard is to provide standardized procedures and definitions for measuring and reporting these parameters in a meaningful way to help users select the right camera for their application.
The document discusses the implementation of stereoscopic and dual view images on micro-display HDTVs. It explains that DMD technology uses a spatial light modulator with mirrors arranged diagonally to achieve high resolution. The smooth picture technology displaces the mirror array by half a pixel to write each input pixel across two subframes at 120Hz for 1080p video. This allows the left and right images for 3D to be shown sequentially using shutter glasses synchronized to the timing signal. The technology enables 3D and dual view modes for applications like gaming, chemistry, motography, x-rays, and ultrasound imaging.
A Novel Approach for Tracking with Implicit Video Shot DetectionIOSR Journals
1) The document presents a novel approach that combines video shot detection and object tracking using a particle filter to create an efficient tracking algorithm with implicit shot detection.
2) It uses a robust pixel difference method for shot detection that is resistant to sudden illumination changes. It then applies a particle filter for tracking that uses color histograms and Bhattacharyya distance to track objects across frames.
3) The key innovation is that the tracking algorithm is only initiated after a shot change is detected, reducing computational costs by discarding unneeded frames and triggering tracking only when needed. This provides a more efficient solution for tracking large video datasets with minimal preprocessing.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development published a paper in 2013 comparing various template matching techniques for face recognition. The paper describes 10 different template matching algorithms: convolution, normalized cross correlation (NCC), zero mean normalized cross correlation (ZNCC), sum of absolute difference (SAD), optimized sum of absolute difference (OSAD), optimized sum of squared difference (OSSD), sum of hamming distance (SHD), and asymmetric correlation. NCC provides good accuracy but is affected by illumination changes. ZNCC and OSAD provide better robustness than NCC. SAD and OSAD are accurate and not affected by illumination, but can be impacted by background clutter.
Disparity Estimation by a Real Time Approximation AlgorithmCSCJournals
This document summarizes an approximation algorithm for real-time disparity estimation of stereo images. The algorithm shrinks the left and right images 3 times to reduce computational time and search area. Disparity is estimated from the shrunk images and then extrapolated to reconstruct the original disparity image. Experimental results on standard stereo images show the algorithm reduces computational time by 76.34% compared to traditional window-based methods, with acceptable accuracy. Some accuracy is lost due to pixel quantization during shrinking and extrapolation, but the fast estimation of dense disparity makes the algorithm useful for applications requiring real-time performance.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
1) The document proposes a method for tracking moving objects in videos captured using a moving camera in complex scenes. It involves video stabilization, key frame extraction, object detection/tracking using Gaussian mixture models and Kalman filters, and object recognition using bag of features.
2) Key frame extraction identifies important frames for processing by computing edge differences between frames and selecting frames above a threshold.
3) Moving objects are detected using background subtraction and Gaussian mixture models, and then tracked across frames using Kalman filters.
4) Object recognition is performed using bag of features, which represents objects as histograms of visual word frequencies to classify objects based on characteristic visual parts.
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
Abstract. We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse contents. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...CSCJournals
Augmented reality has been a topic of intense research for several years for many applications. It consists of inserting a virtual object into a real scene. The virtual object must be accurately positioned in a desired place. Some measurements (calibration) are thus required and a set of correspondences between points on the calibration target and the camera images must be found. In this paper, we present a tracking technique based on both detection of Chessboard corners and a least squares method; the objective is to estimate the perspective transformation matrix for the current view of the camera. This technique does not require any information or computation of the camera parameters; it can used in real time without any initialization and the user can change the camera focal without any fear of losing alignment between real and virtual object.
This document proposes a multi-view object tracking system using deep learning to track objects from multiple camera views. It uses the YOLO v3 algorithm to map segmented object groups between camera views to share knowledge. A two-pass regression framework is also presented for multi-view object tracking. Key steps include preprocessing images, extracting features, detecting and tracking objects between views using blob matching, and counting objects over time by maintaining tracks. The approach aims to improve object counting accuracy by exploiting information from multiple camera views.
The document describes a system developed by Allergan and ADCIS to convert 35mm ophthalmic slides to high-quality digital images in a fast and cost-effective manner. The system uses a 10.1 megapixel digital SLR camera, copy stand, and light box controlled by a computer to capture slide images and patient data. It includes software modules to capture images and data (Slide-Snapper) and query/display images and data (Slide-Display). Testing showed the system could digitize slides over 10 times faster than film scanners while providing superior image quality and color rendering with minimal noise.
Color Detection & Segmentation based Invisible CloakAviral Chaurasia
This document summarizes a student project that aims to create an invisible cloak using color detection and segmentation techniques in image processing. The objectives are to capture background images, detect colored objects in the frame using HSV color space, generate a mask to segment out the colored object, and combine the processed frames to create the effect of invisibility. The methodology involves capturing images using a webcam, processing them with OpenCV libraries, and testing the cloak in real-time. So far the team has completed capturing background images and detecting and segmenting colored objects, and is working on combining the frames. Accuracy and precision metrics will be used to evaluate the model's performance.
This document discusses optimizing 360-degree video streaming to head-mounted virtual reality. It covers challenges like existing codecs only supporting 2D videos and 360 videos having wider views than conventional videos. Approaches proposed include fixation prediction to avoid streaming unwatched parts, QoE modeling designed for 360 videos to improve user experience, and an adaptive streaming platform to select and transmit tiles based on fixation prediction while allocating bitrates based on the QoE model. Part I discusses fixation prediction including using neural networks trained on viewing features. Part II covers QoE modeling, noting limitations of existing metrics and factors that affect QoE like content and bitrates. It constructs a logarithmic linear QoE model. Part III outlines an
Image super resolution using Generative Adversarial Network.IRJET Journal
This document discusses using a generative adversarial network (GAN) for image super resolution. It begins with an abstract that explains super resolution aims to increase image resolution by adding sub-pixel detail. Convolutional neural networks are well-suited for this task. Recent years have seen interest in reconstructing super resolution video sequences from low resolution images. The document then reviews literature on image super resolution techniques including deep learning methods. It describes the methodology which uses a CNN to compare input images to a trained dataset to predict if high-resolution images can be generated from low-resolution images.
Paper 58 disparity-of_stereo_images_by_self_adaptive_algorithmMDABDULMANNANMONDAL
This document summarizes a research paper that proposes a new stereo matching algorithm called Self Adaptive Algorithm (SAA) to efficiently compute stereo correspondence or disparity maps from stereo images. SAA aims to improve matching speed by reducing the search zone and avoiding false matches through an adaptive search approach. It dynamically selects the search range based on previous matching results, reducing the range by 50% with each iteration. Experimental results on standard stereo datasets show that SAA outperforms other methods in terms of speed while maintaining accuracy, with processing speeds of 535 fps and 377 fps for different image pairs. SAA reduces computational time by 70.53-99.93% compared to other state-of-the-art methods.
Design and Analysis of Quantization Based Low Bit Rate Encoding Systemijtsrd
This document summarizes research on developing a low bit rate encoding system for video compression using vector quantization. It first discusses how vector quantization can achieve high compression ratios and has been used widely in image and speech coding. It then describes the methodology used, which involves taking video frames as input, downsampling the frames to extract pixels, applying vector quantization, and detecting edges on the compressed frames to check compression quality. Finally, it discusses the results of testing the approach on MATLAB and presents conclusions on the advantages of the proposed algorithm for very low bit rate video coding applications.
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Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
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We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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20240609 QFM020 Irresponsible AI Reading List May 2024
A real time automatic eye tracking system for ophthalmology
1. A Real-Time Automatic Eye Tracking
System for Ophthalmology
Mr. Wattanit Hotrakool
Mr. Prarinya Siritanawan
Supervised by Dr.Toshiaki Kondo
Sirindhorn International Institute of Technology
1
2. Outline
Project Background
Project Objective
A eye-tracking technique using Traditional Template
Matching
A eye-tracking technique using Gradient Orientation
Pattern Matching
A eye-tracking technique using Time-varying GOPM
Conclusion
Question and Answer
2
3. Project Background
The conventional eye-surgery cameras are manual-
controlled and they reduce the efficiency of surgery.
In order to reduce the burden of oculist, the automated
camera control is required. The image processing is used
to locate and track the eye’s centroid.
3
4. Project Background
Many real-time eye tracking techniques used intensity
data as an input; they are very sensitive to changing
lighting condition and result as miss-matching.
This project proposes new template matching based
technique which robust to changing lighting condition by
using Time-varying Gradient Orientation Pattern
Matching.
4
5. Project Objectives
To implement matching-based techniques in real-time.
To verify the robustness to changing lighting condition of
gradient orientation pattern matching.
To develop new eye-tracking technique; a time-varying
gradient orientation pattern matching.
5
6. Eye-tracking method using template
matching technique
Template matching is the intensity-based technique for
measuring the similarity between template and
corresponding block of image.
Template
Match
Sample frame
6
7. Simulation Specification
Simulation System using the following hardware and
software specifications:
Software Specification
Operating System Windows/Linux
Programming Language C/C++
Primary library OpenCV 2.0
Hardware Specification
Processor Intel Core2 Duo
Processor Speed 1.66 GHz
Memory 4GB
7
8. Video sequences used for simulation
Video sequences used in this simulation can be
categorized into 3 categories:
1. Test video in normal lighting condition
2. Test video in changing lighting condition
3. Actual surgery video from real camera
8
9. Template Initialization
Template initialization is required before the eye-tracking
method.
The pupil is specified as a template in order to track the
eye.
The example of video and corresponding template for
each categories is shown in next page
9
10. Test video in normal lighting condition
Video sequence
Template
10
11. Test video in changing lighting condition
Video sequence
Template
11
14. A eye-tracking method using Traditional
template matching
There are many traditional techniques of template
matching such as sum-of-absolute difference (SAD), sum-
of-squared difference (SSD), or cross-correlation (CC)
technique.
In this step, we implement the sum-of-squared difference
technique (SSD) to be eye-tracking method.
14
15. Sum-of-squared Difference technique
SSD is the template matching method done by finding the
lowest difference value between input and template. The
differences are squared in order to remove the sign.
where I1 is the intensity of input image and
I2 is the intensity of template
N is the size of the template
15
16. Procedure
1. Convert template to gray scale image.
2. Convert input frame to gray scale image.
3. For every pixel, compute the SSD between input and
template.
4. Find the minimum difference pixel, which is the best
matching location.
5. For every frame, repeat step 2-4.
16
18. Result
Input Average computation time Precision error
(ms) (%)
Normal light video 78.35 1.33
(resolution: 320x240 px)
Changing light video 78.63 40.47
(resolution: 320x240 px)
Actual surgery video 103.33 0
(resolution: 384x288 px)
Average computation time mostly depends on video
resolution
However, this method currently can process at 10 -12.5 frame/sec
18
19. Result
Input Average computation time Precision error
(ms) (%)
Normal light video 78.35 1.33
(resolution: 320x240)
Changing light video 78.63 40.47
(resolution: 320x240)
Actual surgery video 103.33 0
(resolution: 384x288)
SSD technique can work very well in normal light video.
However, this technique give high error in changing light video
because it uses intensity data which are sensitive to light.
Therefore SSD cannot work in changing light condition
19
20. Result
Properties of eye-tracking using SSD technique
Obstacle robustness Yes
Blur robustness Yes
Light condition robustness No
Scaling robustness No
Average computation time About 50-350 ms
20
21. A eye-tracking method using Gradient
Orientation Pattern Matching
Presented at ICESIT2010, Chiang Mai, Thailand
21
22. A eye-tracking technique using Gradient
Orientation Pattern Matching
In order to develop a method that can provide the
robustness to light condition, the new template matching
technique is used.
The gradient orientation pattern matching (GOPM) is a
new template matching technique proposed by Dr.
Toshiaki Kondo.
22
23. Gradient Orientation Pattern Matching
GOPM is a template matching method which use the
normalized gradient of the image in place of intensity
data. Thus, it only consider about the shape of the
pattern but not light.
Gradient vector is the first derivative of intensity. The
gradient in x and y direction are defined as:
where I is a intensity of an image
23
24. Gradient Orientation Pattern Matching
The gradient in x and y will then be normalized. This
step provides the robustness to light condition. The
normalized gradient in x and y direction are defined
as:
where
And is a small constant used to prevent zero-division.
24
25. Gradient Orientation Pattern Matching
The normalized gradient in x and y direction of input
frame and template will be match by using SSD
where N1 is the normalized gradient of input image and
N2 is the normalized gradient of template
25
26. Procedure
For every frame, we can divide the procedure into 2 main
steps;
1. Gradient orientation information (GOI) extraction
2. Gradient orientation pattern matching (GOPM)
26
27. Gradient Orientation Information (GOI)
Extraction
Extract the gradient Images
(Template and sample frame) in
x and y direction.
27
28. Gradient Orientation Pattern Matching
Apply template matching in x and y
direction. Then add the result of x and
y direction
28
29. Result
Input SSD technique GOPM technique
Average Precision Average Precision
computation error (%) computation error (%)
time (ms) time (ms)
Normal lighting condition 78.35 1.33 62.48 0
(resolution: 320x240px)
Change lighting condition 78.63 40.47 63.09 12.87
(resolution: 320x240px)
Actual surgery camera 103.33 0 77.7 0
(resolution: 384x288px)
Average computation time of GOPM slightly inprove from
SSD
Even though the method is more complex, but the computation time
is decrease due to the variable type and internal structure of OpenCV
However, this method still can process at only <15 frame/sec
29
30. Result
Input SSD technique GOPM technique
Average Precision Average Precision
computation error (%) computation error (%)
time (ms) time (ms)
Normal lighting condition 78.35 1.33 62.48 0
(resolution: 320x240px)
Change lighting condition 78.63 40.47 63.09 12.87
(resolution: 320x240px)
Actual surgery camera 103.33 0 77.7 0
(resolution: 384x288px)
In changing light condition, GOPM error is dramatically decrease due to
the normalized process.
Therefore GOPM can provide the robustness to changing light
condition
30
31. Result
Properties of eye-tracking using SSD and GOPM technique
SSD GOPM
Obstacle robustness Yes Yes
Blur robustness Yes Yes
Light condition robustness No Yes
Scaling robustness No No
Average computation time About 50-350 ms About 50-350 ms
31
32. A eye-tracking method using a
Time-varying GOPM
Accepted by ECTI-CON 2010, Chiang Mai, Thailand
32
33. Time-varying GOPM
Even though GOPM provides robustness to changing light
condition, however the static template will not
guaranteed that it yields the good result for all condition.
There are many uncontrolled factors such as skin and
noise.
Time-varying GOPM uses the dynamic template which
update itself automatically in place of static template. It
reduce the difference of template environment in various
period of time.
33
34. Template Update Algorithm
Step 1 : Perform GOPM, get best matching coordinate
current Template
BEST MATCH
Sample frame
34
35. Template Update Algorithm
Step 2 : Crop region with the same size of old template
for creating new template
new Template
Sample frame
35
36. Correct-matching criterion
1st Criterion Equation
where Nxn+1 and Nyn+1 are the normalized gradient of the newly created
template, Nxn and Nyn are the normalized gradient of the current
template, i and j are the size of the template.
is a threshold value defined as
1st Criterion is used to check the correctness of the updated
template and prevent the jumping coordinate.
36
37. Correct-matching criterion
2nd Criterion Equation
where Xn and Yn are the location of current matching result,
Xc and Yc are the location of the last known correct result.
T is a threshold value define as
2nd Criterion is used to double check the jumping real
coordinate.
37
38. 1st Criterion
STEP 1 : Find the difference b/w gradient component of
old template and new template in X and Y direction
x
- =
y
- =
Old Template New Template Template diff.
38
39. 1st Criterion
STEP 2 : Combine the difference of x and y
+ =
Diff x Diff y Total Diff
STEP 3 : Sum all elements and then thresholding
If summation is less than the threshold function, update
template.
If summation is more than the threshold function, discard the
new template.
39
40. 31
2nd Criterion
Using the fact that It is impossible that the eye would
change its position suddenly in next frame.
(400,100)
(300,500)
Frame N Frame N+1
40
41. 2nd Criterion
STEP 1 : Find the best matching of the frame N.
(x1,y1)
Frame N
41
42. 2nd Criterion
STEP 2 : If location of N passed the criteria, the location
is used as a latest known correct position C.
Frame N
42
43. 2nd Criterion
STEP 3 : Find best matching of the frame N+1.
(x2,y2)
Frame N+1
43
44. 2nd Criterion
STEP 4 : Find Euclidean distance between position C and
position in frame N+1.
Frame N+1
44
45. 2nd Criterion
STEP 5.1 : If distance more than threshold function,
discard the current location.
Threshold fcn
Frame N+1
45
46. 2nd Criterion
STEP 5.2 : If distance less than threshold function, mask
the location as new corrected position C.
Threshold fcn
Frame N+1
46
49. Downsampling
In here, we resizes input video sequence and template to
50 percent of the height and width. Hence the
downsampled image is reduced to ¼ of the original size.
Thus, computation time is 4 times faster.
No effect to the matching result since both video and
template are downsampled with the same ratio
49
50. Result
Input GOPM technique Time-varying GOPM
Average Precision Average Precision
computation error (%) computation error (%)
time (ms) time (ms)
Normal lighting condition 62.48 0 13.81 0
(resolution: 320x240 px)
Change lighting condition 63.09 12.87 12.92 0
(resolution: 320x240 px)
Actual surgery camera 77.7 0 17.12 0
(resolution: 384x288 px)
Average computation time is decreased by downsampling
Currently this method can process at > 50 frame/sec which is enough
for most of video capture device that run at 25 frame/secs.
50
51. Result
Input GOPM technique Time-varying GOPM
Average Precision Average Precision
computation error (%) computation error (%)
time (ms) time (ms)
Normal lighting condition 62.48 0 13.81 0
(resolution: 320x240 px)
Change lighting condition 63.09 12.87 12.92 0
(resolution: 320x240 px)
Actual surgery camera 77.7 0 17.12 0
(resolution: 384x288 px)
In all cases, time-varying GOPM provide the better result than normal
GOPM. Especially for the case of changing light condition, which error is
decrease to 0%
Therefore time-varying GOPM can provide the robustness to
changing light condition with more precision than normal GOPM
51
52. Result
Properties of eye-tracking using SSD, GOPM , and time-varying GOPM
technique
SSD GOPM Time-varying
Obstacle robustness Yes Yes Yes
Blur robustness Yes Yes Yes
Lighting condition robustness No Yes Yes
Scaling robustness No No No
Average computation time About 50-350 About 50-350 About 10-90
ms ms ms
52
53. Drawback of time-varying GOPM
In rarely case, when the 2nd criteria drop the frame
repeatedly, it causes the template slightly shifts from the
eye’s centroid.
Fail update
However for real implementation prospective, if not be
too much, can be tolerated by the surgeon
53
55. Conclusion
This work verify that the speed of template matching
technique with downsampling is able to implement in
real-time. (speed > 50 frame/secs).
In the changing light condition, the result clearly shows
that GOPM is more robust than SSD.
A time-varying GOPM reduce the difference of
template environment in various time and provides the
higher precision of tracking than normal GOPM.
55
56. Future Work
Optimize the utilization of the threshold function in
corrected-matching criterion.
Due to difference in camera specification such as
resolution or sensitivity, it required other advance
method to supervised the threshold function such as
machine learning or neuron network.
56
57. Acknowledgement
Assist. Prof. Dr. Toshiaki Kondo
Assoc.Prof. Dr. Waree Kongprawechnon
Dr. Itthisek Nilkhamhang
All faculty members and our beloved friends
57