The Experts below are selected from a list of 22854 Experts worldwide ranked by ideXlab platform
Rudy Lauwereins - One of the best experts on this subject based on the ideXlab platform.
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derivative based scale invariant image Feature Detector with error resilience
IEEE Transactions on Image Processing, 2014Co-Authors: Pradip Mainali, Gauthier Lafruit, Klaas Tack, Luc Van Gool, Rudy LauwereinsAbstract:We present a novel scale-invariant image Feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image Feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant Feature transform (SIFT) and speeded up robust Features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality Feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.
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SIFER: Scale-Invariant Feature Detector with Error Resilience
International Journal of Computer Vision, 2013Co-Authors: Pradip Mainali, Gauthier Lafruit, Qiong Yang, Bert Geelen, Luc Van Gool, Rudy LauwereinsAbstract:We present a new method to extract scale-invariant Features from an image by using a Cosine Modulated Gaussian (CM-Gaussian) filter. Its balanced scale-space atom with minimal spread in scale and space leads to an outstanding scale-invariant Feature detection quality, albeit at reduced planar rotational invariance. Both sharp and distributed Features like corners and blobs are reliably detected, irrespective of various image artifacts and camera parameter variations, except for planar rotation. The CM-Gaussian filters are approximated with the sum of exponentials as a single, fixed-length filter and equal approximation error over all scales, providing constant-time, low-cost image filtering implementations. The approximation error of the corresponding digital signal processing is below the noise threshold. It is scalable with the filter order, providing many quality-complexity trade-off working points. We validate the efficiency of the proposed Feature detection algorithm on image registration applications over a wide range of testbench conditions.
Tim Wark - One of the best experts on this subject based on the ideXlab platform.
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an exploration of Feature Detector performance in the thermal infrared modality
Digital Image Computing: Techniques and Applications, 2011Co-Authors: Stephen Vidas, Ruan Lakemond, Simon Denman, Clinton Fookes, Sridha Sridharan, Tim WarkAbstract:Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of Feature Detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on Feature Detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on Detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of Feature counts was found to improve the repeatability performance of several Detectors. Most other image transformations had predictable effects on Feature stability. The best-performing Detector varied considerably depending on the nature of the scene and the test.
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DICTA - An Exploration of Feature Detector Performance in the Thermal-Infrared Modality
2011 International Conference on Digital Image Computing: Techniques and Applications, 2011Co-Authors: Stephen Vidas, Ruan Lakemond, Simon Denman, Clinton Fookes, Sridha Sridharan, Tim WarkAbstract:Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of Feature Detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on Feature Detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on Detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of Feature counts was found to improve the repeatability performance of several Detectors. Most other image transformations had predictable effects on Feature stability. The best-performing Detector varied considerably depending on the nature of the scene and the test.
Pradip Mainali - One of the best experts on this subject based on the ideXlab platform.
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derivative based scale invariant image Feature Detector with error resilience
IEEE Transactions on Image Processing, 2014Co-Authors: Pradip Mainali, Gauthier Lafruit, Klaas Tack, Luc Van Gool, Rudy LauwereinsAbstract:We present a novel scale-invariant image Feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image Feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant Feature transform (SIFT) and speeded up robust Features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality Feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.
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SIFER: Scale-Invariant Feature Detector with Error Resilience
International Journal of Computer Vision, 2013Co-Authors: Pradip Mainali, Gauthier Lafruit, Qiong Yang, Bert Geelen, Luc Van Gool, Rudy LauwereinsAbstract:We present a new method to extract scale-invariant Features from an image by using a Cosine Modulated Gaussian (CM-Gaussian) filter. Its balanced scale-space atom with minimal spread in scale and space leads to an outstanding scale-invariant Feature detection quality, albeit at reduced planar rotational invariance. Both sharp and distributed Features like corners and blobs are reliably detected, irrespective of various image artifacts and camera parameter variations, except for planar rotation. The CM-Gaussian filters are approximated with the sum of exponentials as a single, fixed-length filter and equal approximation error over all scales, providing constant-time, low-cost image filtering implementations. The approximation error of the corresponding digital signal processing is below the noise threshold. It is scalable with the filter order, providing many quality-complexity trade-off working points. We validate the efficiency of the proposed Feature detection algorithm on image registration applications over a wide range of testbench conditions.
Stephen Vidas - One of the best experts on this subject based on the ideXlab platform.
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an exploration of Feature Detector performance in the thermal infrared modality
Digital Image Computing: Techniques and Applications, 2011Co-Authors: Stephen Vidas, Ruan Lakemond, Simon Denman, Clinton Fookes, Sridha Sridharan, Tim WarkAbstract:Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of Feature Detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on Feature Detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on Detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of Feature counts was found to improve the repeatability performance of several Detectors. Most other image transformations had predictable effects on Feature stability. The best-performing Detector varied considerably depending on the nature of the scene and the test.
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DICTA - An Exploration of Feature Detector Performance in the Thermal-Infrared Modality
2011 International Conference on Digital Image Computing: Techniques and Applications, 2011Co-Authors: Stephen Vidas, Ruan Lakemond, Simon Denman, Clinton Fookes, Sridha Sridharan, Tim WarkAbstract:Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of Feature Detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on Feature Detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on Detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of Feature counts was found to improve the repeatability performance of several Detectors. Most other image transformations had predictable effects on Feature stability. The best-performing Detector varied considerably depending on the nature of the scene and the test.
Tieniu Tan - One of the best experts on this subject based on the ideXlab platform.
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ACCV (2) - A harris-like scale invariant Feature Detector
Computer Vision – ACCV 2009, 2010Co-Authors: Kaiqi Huang, Tieniu TanAbstract:Image Feature detection is a fundamental issue in computer vision. SIFT[1] and SURF[2] are very effective in scale-space Feature detection, but their stabilities are not good enough because unstable Features such as edges are often detected even if they use edge suppression as a post-treatment. Inspired by Harris function[3], we extend Harris to scale-space and propose a novel method - Harris-like Scale Invariant Feature Detector (HLSIFD). Different to Harris-Laplace which is a hybrid method of Harris and Laplace, HLSIFD uses Hessian Matrix which is proved to be more stable in scale-space than Harris matrix. Unlike other methods suppressing edges in a sudden way(SIFT) or ignoring it(SURF), HLSIFD suppresses edges smoothly and uniformly, so fewer fake points are detected by HLSIFD. The approach is evaluated on public databases and in real scenes. Compared to the state of arts Feature Detectors: SIFT and SURF, HLSIFD shows high performance of HLSIFD.
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a harris like scale invariant Feature Detector
Asian Conference on Computer Vision, 2009Co-Authors: Kaiqi Huang, Tieniu TanAbstract:Image Feature detection is a fundamental issue in computer vision. SIFT[1] and SURF[2] are very effective in scale-space Feature detection, but their stabilities are not good enough because unstable Features such as edges are often detected even if they use edge suppression as a post-treatment. Inspired by Harris function[3], we extend Harris to scale-space and propose a novel method - Harris-like Scale Invariant Feature Detector (HLSIFD). Different to Harris-Laplace which is a hybrid method of Harris and Laplace, HLSIFD uses Hessian Matrix which is proved to be more stable in scale-space than Harris matrix. Unlike other methods suppressing edges in a sudden way(SIFT) or ignoring it(SURF), HLSIFD suppresses edges smoothly and uniformly, so fewer fake points are detected by HLSIFD. The approach is evaluated on public databases and in real scenes. Compared to the state of arts Feature Detectors: SIFT and SURF, HLSIFD shows high performance of HLSIFD.
