The Experts below are selected from a list of 30777 Experts worldwide ranked by ideXlab platform
Griff Bilbro - One of the best experts on this subject based on the ideXlab platform.
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Stacked Integral Image
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Amit Bhatia, Wesley E. Snyder, Griff BilbroAbstract:Filtering in digital Images via Integral Image yields fast computation times for uniform filtering. The extension by Heckbert to perform filtering via repeated integration provides a way for non-uniform filtering, but has its own limitations. A recent method by Hussein et al. provides non-uniform filtering by Euler expansion of filtering kernels, and is called kernel Integral method. We propose a simplification for non-uniform filtering by stacking of box filters from a single Integral Image. Results show speedups of as much as 40:1, similar to run time performance gains in kernel Integral method, when comparing to naïve nonuniform filtering, while at the same time reducing the setup time drastically.
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ICRA - Stacked Integral Image
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Amit Bhatia, Wesley E. Snyder, Griff BilbroAbstract:Filtering in digital Images via Integral Image yields fast computation times for uniform filtering. The extension by Heckbert [1] to perform filtering via repeated integration provides a way for non-uniform filtering, but has its own limitations. A recent method by Hussein et al. [2] provides non-uniform filtering by Euler expansion of filtering kernels, and is called kernel Integral method. We propose a simplification for non-uniform filtering by stacking of box filters from a single Integral Image.1 Results show speedups of as much as 40∶1, similar to run time performance gains in kernel Integral method, when comparing to naive nonuniform filtering, while at the same time reducing the setup time drastically.
Shoaib Ehsan - One of the best experts on this subject based on the ideXlab platform.
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Exploring Integral Image Word Length Reduction Techniques for SURF Detector
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Shoaib Ehsan, Klaus D. Mcdonald-maierAbstract:Speeded Up Robust Features (SURF) is a state of the art computer vision algorithm that relies on Integral Image representation for performing fast detection and description of Image features that are scale and rotation invariant. Integral Image representation, however, has major draw back of large binary word length that leads to substantial increase in memory size. When designing a dedicated hardware to achieve real-time performance for the SURF algorithm, it is imperative to consider the adverse effects of Integral Image on memory size, bus width and computational resources. With the objective of minimizing hardware resources, this paper presents a novel implementation concept of a reduced word length Integral Image based SURF detector. It evaluates two existing word length reduction techniques for the particular case of SURF detector and extends one of these to achieve more reduction in word length. This paper also introduces a novel method to achieve Integral Image word length reduction for SURF detector.
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novel hardware algorithms for row parallel Integral Image calculation
Digital Image Computing: Techniques and Applications, 2009Co-Authors: Shoaib Ehsan, Adrian F Clark, Klaus D McdonaldmaierAbstract:The Integral Image is an intermediate Image representation that allows rapid calculation of rectangular features at constant speed, irrespective of filter size, and is particularly useful for multi-scale computer vision algorithms like Speeded-Up Robust Features (SURF). Although calculation of the Integral Image involves simple addition operations, the total number of operations is significant due to the generally large size of Image data. Recursive equations allow considerable reduction in the required number of addition operations but require calculation of the Integral Image in a serial fashion. This is generally not desirable for real-time embedded vision systems with strict time limitations and low-powered but parallel hardware resources. With the objective of minimizing the hardware resources involved, this paper proposes two novel hardware algorithms based on decomposition of these recursive equations, allowing calculation of up to four Integral Image values in a row-parallel way with out significantly increasing the number of addition operations.
Amit Bhatia - One of the best experts on this subject based on the ideXlab platform.
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Stacked Integral Image
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Amit Bhatia, Wesley E. Snyder, Griff BilbroAbstract:Filtering in digital Images via Integral Image yields fast computation times for uniform filtering. The extension by Heckbert to perform filtering via repeated integration provides a way for non-uniform filtering, but has its own limitations. A recent method by Hussein et al. provides non-uniform filtering by Euler expansion of filtering kernels, and is called kernel Integral method. We propose a simplification for non-uniform filtering by stacking of box filters from a single Integral Image. Results show speedups of as much as 40:1, similar to run time performance gains in kernel Integral method, when comparing to naïve nonuniform filtering, while at the same time reducing the setup time drastically.
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ICRA - Stacked Integral Image
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Amit Bhatia, Wesley E. Snyder, Griff BilbroAbstract:Filtering in digital Images via Integral Image yields fast computation times for uniform filtering. The extension by Heckbert [1] to perform filtering via repeated integration provides a way for non-uniform filtering, but has its own limitations. A recent method by Hussein et al. [2] provides non-uniform filtering by Euler expansion of filtering kernels, and is called kernel Integral method. We propose a simplification for non-uniform filtering by stacking of box filters from a single Integral Image.1 Results show speedups of as much as 40∶1, similar to run time performance gains in kernel Integral method, when comparing to naive nonuniform filtering, while at the same time reducing the setup time drastically.
H.j.w. Belt - One of the best experts on this subject based on the ideXlab platform.
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word length reduction for the Integral Image
International Conference on Image Processing, 2008Co-Authors: H.j.w. BeltAbstract:The Integral Image is an Image containing accumulated sums of pixel values taken from an input Image. It is an important concept for multi-scale Image processing algorithms, for it provides a very economic way to compute the sum of pixel values in any rectangular input Image region. Unfortunately, the Integral Image requires a large binary word length to represent the accumulated sums. This is an issue for platforms having limited memory, power, and bandwidth like in mobile devices. Our paper deals with two methods for word length reduction, involving computation through the overflow and rounding with error diffusion. We show by experiment that, based on a word length reduced Integral Image, the Viola and Jones face detector for a VGA resolution can work on a 16-bit CPU (i.s.o. 27 bits, which becomes 32 bits on byte-oriented CPUs), enabling face detection on a wider range of platforms.
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ICIP - Word length reduction for the Integral Image
2008 15th IEEE International Conference on Image Processing, 2008Co-Authors: H.j.w. BeltAbstract:The Integral Image is an Image containing accumulated sums of pixel values taken from an input Image. It is an important concept for multi-scale Image processing algorithms, for it provides a very economic way to compute the sum of pixel values in any rectangular input Image region. Unfortunately, the Integral Image requires a large binary word length to represent the accumulated sums. This is an issue for platforms having limited memory, power, and bandwidth like in mobile devices. Our paper deals with two methods for word length reduction, involving computation through the overflow and rounding with error diffusion. We show by experiment that, based on a word length reduced Integral Image, the Viola and Jones face detector for a VGA resolution can work on a 16-bit CPU (i.s.o. 27 bits, which becomes 32 bits on byte-oriented CPUs), enabling face detection on a wider range of platforms.
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Storage Size Reduction for the Integral Image
2007Co-Authors: H.j.w. BeltAbstract:The Integral Image is an Image containing accumulated sums of pixel values taken from an input Image. It is an important concept for the computationally efficient implementation of so-called box filters, where the sums of the values of pixel in a rectangular Image region are calculated. An important example of an algorithm using the Integral Image, treated in this report, is the well-known Viola and Jones object detector. Though computationally efficient for its low number of operations, unaddressed issues with the Integral Image are its large storage size and word length. This report deals with methods to reduce both considerably. As a result the Integral Image becomes an even more attractive concept for applications having memory and power constraints, like portable multi-media devices. One algorithm is taken as example for an experiment, namely a Viola and Jones face detector. It is shown that with the proposed word length reduction technique itis possible to apply Viola and Jones face detection on VGA Image using only a 16-bit media processing architecture.
Wesley E. Snyder - One of the best experts on this subject based on the ideXlab platform.
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Stacked Integral Image
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Amit Bhatia, Wesley E. Snyder, Griff BilbroAbstract:Filtering in digital Images via Integral Image yields fast computation times for uniform filtering. The extension by Heckbert to perform filtering via repeated integration provides a way for non-uniform filtering, but has its own limitations. A recent method by Hussein et al. provides non-uniform filtering by Euler expansion of filtering kernels, and is called kernel Integral method. We propose a simplification for non-uniform filtering by stacking of box filters from a single Integral Image. Results show speedups of as much as 40:1, similar to run time performance gains in kernel Integral method, when comparing to naïve nonuniform filtering, while at the same time reducing the setup time drastically.
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ICRA - Stacked Integral Image
2010 IEEE International Conference on Robotics and Automation, 2010Co-Authors: Amit Bhatia, Wesley E. Snyder, Griff BilbroAbstract:Filtering in digital Images via Integral Image yields fast computation times for uniform filtering. The extension by Heckbert [1] to perform filtering via repeated integration provides a way for non-uniform filtering, but has its own limitations. A recent method by Hussein et al. [2] provides non-uniform filtering by Euler expansion of filtering kernels, and is called kernel Integral method. We propose a simplification for non-uniform filtering by stacking of box filters from a single Integral Image.1 Results show speedups of as much as 40∶1, similar to run time performance gains in kernel Integral method, when comparing to naive nonuniform filtering, while at the same time reducing the setup time drastically.
