Novel interest point detector using bilateral-Harris corner method

Manoranjitham, R.; Deepa, P.

doi:10.1109/icaccs.2017.8014721

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The bilateral filter is similar to the Gaussian filter. But the bi-lateral filter also uses sigma color and sigma space parameters for better filtering the image and reducing noise much more efficiently [23].…”

Section: B) Bi-lateral Filtermentioning

confidence: 99%

FPGA based object parameter detection for Embedded Vision Application

Mo,

Singh

2023

IJCDS

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Real-time image/video processing contains complex algorithms, and the number of computation operations such as a number of additions and the number of multiplications, which are solved by CPU, results decrease in processing speed and consume more power. On the other hand, FPGA has high processing speed and low power consumption and it also has the capability to work with CPU. Hence, sharing the load and performing complex tasks, results increase the system performance. In this work, there is the development of IPs for single object detection and canny edge detection using different filters. These IPs can be used in FPGA for real-time image processing. The tool which is used is Vivado HLS, to generate the hardware accelerator and the library which is used is the xfOpenCV library. Both are developed by Xilinx. XfOpenCV library is the optimized version of OpenCV means to reduce the complexity of an algorithm for fast performance. Single object detection detects the object in real-time by using FPGA based system and xfOpenCV APIs. Similarly, edge detection using a different filter shows the effect of filters, which means removing noise in the image and making it smooth, for edge detection. Smoothing image also reduces the number of computations (Addition and Multiplication). Resource utilized by the generated IPs is very less which makes the system less heavy and more reliable.

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Section: B) Bi-lateral Filtermentioning

confidence: 99%

FPGA based object parameter detection for Embedded Vision Application

Mo,

Singh

2023

IJCDS

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“…Three major regions in any object of an image are "flat", "edge", and "corner". Harris corner detector [10,11] provides a mathematical approach for deciding which region holds. Harris and Stephens [10] have considered the differential of the corner score instead of shifted patches.…”

Section: Related Workmentioning

confidence: 99%

Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences

Ranganatha¹,

Gowramma²

2022

IJIGSP

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We are proposing a unique novel algorithm for tracking human face(s) in different background video sequences. In the beginning, Eigen features and corner points are extracted from the detected face(s). HOG (Histograms of Oriented Gradients) features are isolated from corner points. Eigen and HOG features are combined together. Using these combined features, point tracker keeps track of the face(s) in the frames of the video sequence. Proposed algorithm is being tested on challenging datasets video sequences with technical challenges such as partial occlusion (e.g. moustache, beard, spectacles, helmet, headscarf etc.), changes in expression, variations in illumination and pose; and measured for performance using standard metrics such as accuracy, precision, recall and specificity. Experimental results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.

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“…first detect candidates and then remove the incorrect points by using the symmetry of the point coordinates and the interactions of the grid lines. Manoranjitham and Deepa [16] proposed the bilateral scale space construction calculation and used the Harris filter to detect extremes. Zheng et al [17] used two convolutional templates and triangular part detectors that were combined to calculate point coordinates.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Calibration of Omnidirectional Camera Using an Iterative Method

Gong

et al. 2019

IEEE Access

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A distorted checkerboard image affects the precision of omnidirectional camera calibration because of the inaccurate localization of the feature points. To solve this problem, an iterative refinement method is presented. First, the initial parameters are obtained using the traditional calibration method, and the distorted checkerboard is transformed to a distortion-free plane by the projection model that is estimated by initial parameters. Then, the feature point coordinates of the corrected image are extracted. The calibration parameters are recomputed using the refinement of new point locations until the camera parameters converge. This iterative refinement method improves the localization accuracy of feature points and, consequently, of camera calibration. The correctness and effectiveness of the method are verified by a series of simulations and physical experiments. The experiments show that the reprojection errors of feature points are reduced by 39% compared to traditional methods.INDEX TERMS Omnidirectional camera calibration, feature point extraction.

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Novel interest point detector using bilateral-Harris corner method

Cited by 5 publications

References 13 publications

FPGA based object parameter detection for Embedded Vision Application

FPGA based object parameter detection for Embedded Vision Application

Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences

High-Precision Calibration of Omnidirectional Camera Using an Iterative Method

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