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Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Ehsan S, Clark AF - Sensors (Basel) (2015)

Bottom Line: The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size.An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video).Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK. sehsan@essex.ac.uk.

ABSTRACT
The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

No MeSH data available.


Internal memory requirements for the integral image computation engine for some common image sizes.
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sensors-15-16804-f007: Internal memory requirements for the integral image computation engine for some common image sizes.

Mentions: Both the recursion-based serial [2] and parallel methods (in Section 3 and Section 4) require one complete row of integral image values to be stored in an internal memory so that it can be utilized for the calculation of the very next row. The width of the required internal memory is(number of rows × number of columns × maximum image pixel value) rounded to the upper integer whereas the depth is equal to the total number of columns in one row of the image. Figure 7 highlights the internal memory requirements for an integral image computation engine implemented in hardware for some common images sizes. It is evident that with the increasing image size, the design of the integral image computation engine becomes inefficient in terms of hardware resources due to the large internal memory. It is desirable to achieve a design which is memory-efficient and provides high throughput.


Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Ehsan S, Clark AF - Sensors (Basel) (2015)

Internal memory requirements for the integral image computation engine for some common image sizes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541907&req=5

sensors-15-16804-f007: Internal memory requirements for the integral image computation engine for some common image sizes.
Mentions: Both the recursion-based serial [2] and parallel methods (in Section 3 and Section 4) require one complete row of integral image values to be stored in an internal memory so that it can be utilized for the calculation of the very next row. The width of the required internal memory is(number of rows × number of columns × maximum image pixel value) rounded to the upper integer whereas the depth is equal to the total number of columns in one row of the image. Figure 7 highlights the internal memory requirements for an integral image computation engine implemented in hardware for some common images sizes. It is evident that with the increasing image size, the design of the integral image computation engine becomes inefficient in terms of hardware resources due to the large internal memory. It is desirable to achieve a design which is memory-efficient and provides high throughput.

Bottom Line: The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size.An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video).Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK. sehsan@essex.ac.uk.

ABSTRACT
The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

No MeSH data available.