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GPU-based fast scale invariant interest point detector
2010
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
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To take full advantage of the powerful computing capability of graphics processing units (GPU) to speed up local feature detection, we present a novel GPU-based scale invariant interest point detector, ...
H-H detects Harris points in low scale and refines their location and scale in higher scale-space with the determinant of Hessian matrix. ...
GPU-BASED SCALE INVARIANT INTEREST POINT DETECTOR To obtain scale invariant detector, Mikolajczyk [5] combined the Harris detector with the Laplacian-based (LoG) scale selection. ...
doi:10.1109/icassp.2010.5494898
dblp:conf/icassp/XieGZLL10
fatcat:qcttztzf4vabpkc6hwtiu42ek4
A fast and efficient sift detector using the mobile GPU
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. ...
of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. ...
Scale-invariant interest points, or features, are essential to many computer vision tasks, such as object recognition and tracking, and will continue to gain relevance in the realm of mobile computing ...
doi:10.1109/icassp.2013.6638141
dblp:conf/icassp/RisterWWC13
fatcat:hdcmjzumibgevcauvjr55pchuy
GPGPU Acceleration of the KAZE Image Feature Extraction Algorithm
[article]
2017
arXiv
pre-print
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Additionally, the strategies described here can prove useful for the GPU implementation of other nonlinear scale space based methods. ...
The CUDA based parallel implementation is described in detail with fine-grained comparison between the GPU and CPU implementations. ...
Turk, “Evaluation of interest point
Equations (PDE) [33] more efficiently. ...
arXiv:1706.06750v1
fatcat:gw7x3ctqrbhn7h24dylkmtlotu
FPGA-based module for SURF extraction
2014
Machine Vision and Applications
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The module's overall performance is evaluated and compared to CPU and GPU based solutions. ...
We describe the module hardware as well as the custom FPGA image processing cores that implement the algorithm's most computationally expensive process, the interest point detection. ...
Then, points of interest are identified by means of "Fast Hessian" blob detector, which pinpoints local brightness extrema. ...
doi:10.1007/s00138-014-0599-0
fatcat:dj3ddob3xjeuroamg67olae46q
Evaluation of feature-based methods for automated network orientation
2014
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The performed tests – based on the analysis of the SIFT algorithm and its most used variants – processed some datasets and analysed various interesting parameters and outcomes (e.g. number ...
In this paper we evaluate some feature-based methods used to automatically extract the tie points necessary for calibration and orientation procedures, in order to better understand their performances ...
Interest point or corner detectors (MORAVEC: Moravec, 1979; FOERSTNER: Foerstner & Guelch, 1987; HARRIS: Harris & Stephens, 1988; SUSAN: Smith & Brady, 1997; AGAST: Mair et al., 2010; FAST: Rosten et ...
doi:10.5194/isprsarchives-xl-5-47-2014
fatcat:2uxt2gp5cbfonccmm4763zrjsu
Fast scale invariant feature detection and matching on programmable graphics hardware
2008
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
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The remaining time can then be spent on fast matching against large feature databases on the GPU while the CPU can be used for other tasks. ...
This paper presents methods that take full advantage of modern graphics card hardware for real-time scale invariant feature detection and matching. ...
Search for candidate feature points by the creation of a Hessian based scale-space pyramid (SURF detector). Fast filtering is performed by approximating the Hessian as a combination of boxfilters. 3. ...
doi:10.1109/cvprw.2008.4563087
dblp:conf/cvpr/CornelisG08
fatcat:gcdwlywpf5hdpjhjkvmfvho2xi
Feature Descriptors for Tracking by Detection: a Benchmark
[article]
2016
arXiv
pre-print
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More recently, due to fast key-point detectors, local image features can be used in online tracking frameworks. ...
Our results show that binary descriptors like ORB or BRISK have comparable results to SIFT or AKAZE due to a higher number of key-points. ...
As a consequence, there is a growing interest within the computer vision community it fast key-point detectors and binary descriptors that can dramatically decrease the computational cost of detecting ...
arXiv:1607.06178v1
fatcat:wgabokpzzzhfhayjzggeujoibe
Fast cortical keypoints for real-time object recognition
2013
2013 IEEE International Conference on Image Processing
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In this paper we present a fast cortical keypoint detector for extracting meaningful points from images. ...
It is competitive with state-of-the-art detectors and particularly well-suited for tasks such as object recognition. ...
Detectors prefixed with "G" are GPU-based implementations. ...
doi:10.1109/icip.2013.6738695
dblp:conf/icip/TerzicRB13
fatcat:ipard7aq25dyhe5fhp3izrgj4y
Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU
2016
Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
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GPUs in embedded platforms are reaching performance levels comparable to desktop hardware, thus it becomes interesting to apply Computer Vision techniques. ...
We propose, implement, and evaluate a novel feature detector and descriptor combination, i.e., we combine the Harris-Hessian detector with the FREAK binary descriptor. ...
The detector is based on calculating a Difference of Gaussians (DoG) with several scale spaces. ...
doi:10.5220/0005662005170525
dblp:conf/icpram/DanielssonSGR16
fatcat:6ik4gjnljbhxjigax5xuai4z4m
FPGA based Speeded Up Robust Features
2009
2009 IEEE International Conference on Technologies for Practical Robot Applications
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The SURF algorithm extracts salient points from image and computes descriptors of their surroundings that are invariant to scale, rotation and illumination changes. ...
The interest point detection and feature descriptor extraction algorithm is often used as the first stage in autonomous robot navigation, object recognition and tracking etc. ...
To achieve scale invariance, algorithm searches for significant points on multiple image scale levels. ...
doi:10.1109/tepra.2009.5339646
fatcat:kv2tchxw35dtbp7qpzf5qldrzu
BIMP: A real-time biological model of multi-scale keypoint detection in V1
2015
Neurocomputing
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This makes them interesting both in biological models and as a useful detector in practice. ...
Models of single-and double-stopped hypercomplex cells in area V1 of the mammalian visual cortex are used to detect stable points of high complexity at multiple scales. ...
Meaningful blobs have been detected using region-based methods [8, 9] and by other affine-invariant region detectors. Additional interest point and region detectors are described in [10] . ...
doi:10.1016/j.neucom.2014.09.054
fatcat:ydk4pqqznjaihm7pubvuegfpha
On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods
2019
2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)
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2020 pre-print arXiv:2007.10000v1 fatcat:2yj352mxwvfzzdvtgqqumtkc2eOther Versions
The purpose of this study is to give a performance comparison between several classic hand-crafted and deep key-point detector and descriptor methods. ...
into detector-descriptor pipelines. ...
As a detector, SIFT convolves the image with Gaussian filters at various scales and detector finds scale invariant keypoints by selecting the local extrema in both scale and space. ...
doi:10.1109/ispa.2019.8868792
dblp:conf/imspa/BojanicBPPDS19
fatcat:rmqtwqtf6nco3phzto5eosfv5a
Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review
2020
Applied Sciences
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Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented ...
state-of-art object detectors. ...
Real-time object detection using different GPU-based embedded platforms should be robust with respect to invariant occlusions, scales, illumination, intra-class variations, and deformations. ...
doi:10.3390/app10093280
fatcat:e6jrltv6lrhxjntlhq7d34247e
Video Extruder: a semi-dense point tracker for extracting beams of trajectories in real time
2014
Journal of Real-Time Image Processing
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Its density and reliability in mobile video scenarios are compared with those of the FAST detector. ...
Then, a multi-scale matching strategy is presented, based on hybrid regional coarse-to-fine and temporal prediction, which provides robustness to large camera and object accelerations. ...
Besides, high invariance implies a computationally Fig. 5 Profiling the FAST detectors. ...
doi:10.1007/s11554-014-0415-0
fatcat:fzmvu372u5azpi7wsrfsbdld7m
Localization accuracy of interest point detectors with different scale space representations
2014
2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
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Recently, the alternative interest point (ALP) detector demonstrated its strength in fast computation on highly parallel architectures like the GPU. ...
The most popular detector for scale invariant features is the SIFT detector which uses the Difference of Gaussians (DoG) pyramid as an approximation of the LoG. ...
An alternative to SIFT, called alternative interest point (ALP) detector [6] , is designed for the usage in MPEG. ...
doi:10.1109/avss.2014.6918676
dblp:conf/avss/CordesRO14
fatcat:qimk47v6srddjcrkhu3rb4fysq
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