Computation of Orientational Filters for Real-Time Computer Vision Problems I: Implementation and Methodology

Kubota, Toshiro; Alford, Cecil O.

doi:10.1006/rtim.1995.1026

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…This decomposition can be obtained by applying SVD to the matrix g f [29], [16]. For each filter, we chose r so that |ĝ T f g f | is larger than 0.95.…”

Section: A Dictionary Of Featuresmentioning

confidence: 99%

Sharing Visual Features for Multiclass and Multiview Object Detection

Torralba

Murphy

Freeman

2007

IEEE Trans. Pattern Anal. Mach. Intell.

587

450

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“…This decomposition can be obtained by applying SVD to the matrix g f [29], [16]. For each filter, we chose r so that |ĝ T f g f | is larger than 0.95.…”

Section: A Dictionary Of Featuresmentioning

confidence: 99%

Sharing Visual Features for Multiclass and Multiview Object Detection

Torralba

Murphy

Freeman

2007

IEEE Trans. Pattern Anal. Mach. Intell.

587

450

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show abstract

“…The capturing hardware used in the commercially available systems are very expensive and complex. Tracking features (in our case anatomical land marks) in real-time is very dicult [32,33]. Additionally the tracking is highly dependent of the quality of the captured gaits.…”

Section: System Descriptionmentioning

confidence: 99%

Development of an Automated Image Processing System for Kinematic Analysis of Human Gait

Yeasin¹,

Chaudhuri²

2000

Real-Time Imaging

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“…There are different types of Orientational filters which can be used to extract local oriented energy features [16]. Gabor filters and second derivative of Gaussian are the most popular ones [7].…”

Section: Gabor Filtersmentioning

confidence: 99%

Object tracking on FPGA-based smart cameras using local oriented energy and phase features

Norouznezhad

Bigdeli

Postula

et al. 2010

Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras

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This paper presents the use of local oriented energy and phase features for real-time object tracking on smart cameras. In our proposed system, local energy features are used as spatial feature set for representing the target region while the local phase information are used for estimating the motion pattern of the target region. The motion pattern information of the target region is used for displacement of search area. Local energy and phase features are extracted by filtering the incoming images with a bank of complex Gabor filters. The effectiveness of the chosen feature set is tested using a mean-shift tracker. Our experiments show that the proposed system can significantly enhance the performance of the tracker in presence of photometric variations and geometric transformation. The real-time implementation of the system is also described in this paper. To achieve the desired performance, a hardware/software co-design approach is pursued. Apart from mean-shift vector calculation, the other blocks are implemented on hardware resources. The system was synthesized onto a Xilinx Virtex-5 XC5VSX50T using Xilinx ML506 development board and the implementation results are presented.

show abstract

Computation of Orientational Filters for Real-Time Computer Vision Problems I: Implementation and Methodology

Cited by 10 publications

References 27 publications

Sharing Visual Features for Multiclass and Multiview Object Detection

Sharing Visual Features for Multiclass and Multiview Object Detection

Development of an Automated Image Processing System for Kinematic Analysis of Human Gait

Object tracking on FPGA-based smart cameras using local oriented energy and phase features

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