Abstract: In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object’s motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values.
Abstract: This paper discusses a corner detection algorithm
for camera calibration. Calibration is a necessary step in many
computer vision and image processing applications. Robust
corner detection for an image of a checkerboard is required
to determine intrinsic and extrinsic parameters. In this paper,
an algorithm for fully automatic and robust X-corner detection
is presented. Checkerboard corner points are automatically
found in each image without user interaction or any prior
information regarding the number of rows or columns. The
approach represents each X-corner with a quadratic fitting
function. Using the fact that the X-corners are saddle points,
the coefficients in the fitting function are used to identify each
corner location. The automation of this process greatly simplifies
calibration. Our method is robust against noise and different
camera orientations. Experimental analysis shows the accuracy
of our method using actual images acquired at different camera
locations and orientations.
Abstract: In this paper, a novel corner detection method is
presented to stably extract geometrically important corners.
Intensity-based corner detectors such as the Harris corner can detect
corners in noisy environments but has inaccurate corner position and
misses the corners of obtuse angles. Edge-based corner detectors such
as Curvature Scale Space can detect structural corners but show
unstable corner detection due to incomplete edge detection in noisy
environments. The proposed image-based direct curvature estimation
can overcome limitations in both inaccurate structural corner detection
of the Harris corner detector (intensity-based) and the unstable corner
detection of Curvature Scale Space caused by incomplete edge
detection. Various experimental results validate the robustness of the
proposed method.