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Brief notes: Abs-Laplacian series kernels as
"... a promising edge detection tool for real time imaging ..."
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a promising edge detection tool for real time imaging
Fusing Electro-Optic and Infrared Signals for High Resolution Night Images
- Proc. SPIE 2012
"... ABSTRACT Electro-optic (EO) images exhibit the properties of high resolution and low noise level, while it is a challenge to distinguish objects at night through infrared (IR) images, especially for objects with a similar temperature. Therefore, we will propose a novel framework of IR image enhance ..."
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ABSTRACT Electro-optic (EO) images exhibit the properties of high resolution and low noise level, while it is a challenge to distinguish objects at night through infrared (IR) images, especially for objects with a similar temperature. Therefore, we will propose a novel framework of IR image enhancement based on the information (e.g., edge) from EO images, which will result in high resolution IR images and help us distinguish objects at night. Superimposing the detected edge of the EO image onto the corresponding transformed IR image is our principal idea for the proposed framework. In this framework, we will adopt the theoretical point spread function (PSF) proposed by Russell C. Hardie et al. for our IR image system, which is contributed by the modulation transfer function (MTF) of a uniform detector array and the incoherent optical transfer function (OTF) of diffraction-limited optics. In addition, we will design an inverse filter in terms of the proposed PSF to conduct the IR image transformation. The framework requires four main steps, which are inverse filter-based IR image transformation, EO image edge detection, registration and superimposing of the obtained image pair. Simulation results will show the superimposed IR images.
Multi-Sensor Fusion of Infrared and Electro-Optic Signals for High Resolution Night Images
, 2012
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A Fuzzy Set Approach for Edge Detection
"... Image segmentation is one of the most studied problems in image analysis, computer vision, pattern recognition etc. Edge detection is a discontinuity based approach used for image segmentation. An edge detection using fuzzy set is proposed here, where an image is considered as a fuzzy set and pixels ..."
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Image segmentation is one of the most studied problems in image analysis, computer vision, pattern recognition etc. Edge detection is a discontinuity based approach used for image segmentation. An edge detection using fuzzy set is proposed here, where an image is considered as a fuzzy set and pixels are taken as elements of fuzzy set. The proposed approach converts the color image to a partially segmented image; finally an edge detector is convolved over the partially segmented image to obtain an edged image. The approach is implemented using MATLAB 7.11. (R2010b). In this paper, an attempt is made to evaluate edge detection using ground truth for quantitative and qualitative comparison. 30 BSD (Berkeley Segmentation Database) images and respective ground truths are used for experimentation. Performance parameters used are PSNR (dB) and Performance ratio (PR) of true to false edges. Experimental results shows that the proposed approach gives higher PSNR and PR values when compared with Canny’s edge detection algorithm under almost all scenarios. The proposed approach reduces false edge detection and identification of double edges are minimum.
REVIEW ON CANNY EDGE DETECTION
"... Abstract — Edge detection is one of the key stages in image processing.In this paper using canny edge detection algorithm, Edges will detect the efficiently with reduction in the processing speed and reduced the memory requirement. Canny edge detection algorithm reduces the latency and increase the ..."
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Abstract — Edge detection is one of the key stages in image processing.In this paper using canny edge detection algorithm, Edges will detect the efficiently with reduction in the processing speed and reduced the memory requirement. Canny edge detection algorithm reduces the latency and increase the throughput with no loss in edge detection performance and algorithm which has the ability to compute edge of multiple blocks at the same time. Canny developed an approach to derive an optimal edge detector for clean and noisy images the canny edge detection algorithm yield better edge detection result.
AUTOMATIC URBAN 3D BUILDING RECONSTRUCTION FROM MULTI-RAY PHOTOGRAMMETRY
"... Over the last 20 years the use of, and demand for, three dimensional (3D) building models has meant there has been a vast amount of research conducted in automating the extraction and reconstruction of these models from airborne sensors. Whilst many different approaches have been suggested, full aut ..."
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Over the last 20 years the use of, and demand for, three dimensional (3D) building models has meant there has been a vast amount of research conducted in automating the extraction and reconstruction of these models from airborne sensors. Whilst many different approaches have been suggested, full automation is yet to be achieved and research has suggested that the combination of data from multiple sources is required in order to achieve this. Developments in digital photogrammetry have delivered improvements in spatial resolution whilst higher image overlap to increase the number of pixel correspondents between images, giving the name multi-ray photogrammetry, has improved the resolution and quality of its by-products. In this paper the extraction of roof geometry from multi-ray photogrammetry will be covered, which underpins 3D building reconstruction. Using orthophotos, roof vertices are extracted using the Canny edge detector. Roof planes are detected from digital surface models (DSM) by extracting information from 2D cross sections and measuring height differences. To eliminate overhanging vegetation, the segmentation of trees is investigated by calculating the characteristics of a point within a local neighbourhood of the photogrammetric point cloud. The results highlight the complementary nature of these information sources, and a methodology for integration and reconstruction of roof geometry is proposed. 1.
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, 2015
"... niques for Hand Shape Recognition ” is my own work, that it has not been submitted before for any degree or assessment at any other university, and that all the sources I have used or quoted have been indicated and acknowledged by means of complete ref-erences. ..."
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niques for Hand Shape Recognition ” is my own work, that it has not been submitted before for any degree or assessment at any other university, and that all the sources I have used or quoted have been indicated and acknowledged by means of complete ref-erences.
Analysis of Vascular Pattern Recognition Using Neural Network
"... Biometric identification using vein patterns is a recent technique. The vein patterns are unique to each individual even in twins and they don’t change over age except their size. As veins are beneath the skin it is difficult to forge. BOSPHOROUS hand vein database is used in this work. Hand vein im ..."
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Biometric identification using vein patterns is a recent technique. The vein patterns are unique to each individual even in twins and they don’t change over age except their size. As veins are beneath the skin it is difficult to forge. BOSPHOROUS hand vein database is used in this work. Hand vein images are uploaded first and key points using Scale Invariant Feature Transform (SIFT) are extracted. Then the neural network is used for training these images. Finally neural network is used for testing these images to check whether the image used for testing matches with the existing database or not. Results are computed like False Acceptation Rate (FAR), False Rejection Rate (FRR), accuracy and error per bit stream.
Particle Swarm based Unsharp Masking
"... Edge enhancement is a predominant process in vision based applications. The performance of the image analysis and interpretation tasks depends on the quality of the image features. It insists that an image should be pre-processed to enhance the fine details like edges. Linear Unsharp Masking (UM) is ..."
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Edge enhancement is a predominant process in vision based applications. The performance of the image analysis and interpretation tasks depends on the quality of the image features. It insists that an image should be pre-processed to enhance the fine details like edges. Linear Unsharp Masking (UM) is a conventional method to enhance the edges in the image. The effect of Unsharp Masking depends on the scal-ing factor provided by the user. In this paper, a novel refer-ence free edge enhancement method called, Particle Swarm based Unsharp Masking (PSUM) is introduced where the scaling factor is optimized through Particle Swarm Opti-mization by minimizing the blur function without a priori information about the content of the image. The proposed work has been tested over various types of images and low resolution videos and proved to enhance the edges.
