1,340 research outputs found
Automatic Detection of Calibration Grids in Time-of-Flight Images
It is convenient to calibrate time-of-flight cameras by established methods,
using images of a chequerboard pattern. The low resolution of the amplitude
image, however, makes it difficult to detect the board reliably. Heuristic
detection methods, based on connected image-components, perform very poorly on
this data. An alternative, geometrically-principled method is introduced here,
based on the Hough transform. The projection of a chequerboard is represented
by two pencils of lines, which are identified as oriented clusters in the
gradient-data of the image. A projective Hough transform is applied to each of
the two clusters, in axis-aligned coordinates. The range of each transform is
properly bounded, because the corresponding gradient vectors are approximately
parallel. Each of the two transforms contains a series of collinear peaks; one
for every line in the given pencil. This pattern is easily detected, by
sweeping a dual line through the transform. The proposed Hough-based method is
compared to the standard OpenCV detection routine, by application to several
hundred time-of-flight images. It is shown that the new method detects
significantly more calibration boards, over a greater variety of poses, without
any overall loss of accuracy. This conclusion is based on an analysis of both
geometric and photometric error.Comment: 11 pages, 11 figures, 1 tabl
Structured Light-Based 3D Reconstruction System for Plants.
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance
Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network
In this paper, we suggest a new neural network architecture for vanishing
point detection in images. The key element is the use of the direct and
transposed Fast Hough Transforms separated by convolutional layer blocks with
standard activation functions. It allows us to get the answer in the
coordinates of the input image at the output of the network and thus to
calculate the coordinates of the vanishing point by simply selecting the
maximum. Besides, it was proved that calculation of the transposed Fast Hough
Transform can be performed using the direct one. The use of integral operators
enables the neural network to rely on global rectilinear features in the image,
and so it is ideal for detecting vanishing points. To demonstrate the
effectiveness of the proposed architecture, we use a set of images from a DVR
and show its superiority over existing methods. Note, in addition, that the
proposed neural network architecture essentially repeats the process of direct
and back projection used, for example, in computed tomography.Comment: 9 pages, 9 figures, submitted to "Computer Optics"; extra experiment
added, new theorem proof added, references added; typos correcte
Lane Departure and Front Collision Warning System Using Monocular and Stereo Vision
Driving Assistance Systems such as lane departure and front collision warning has caught great attention for its promising usage on road driving. This, this research focus on implementing lane departure and front collision warning at same time. In order to make the system really useful for real situation, it is critical that the whole process could be near real-time. Thus we chose Hough Transform as the main algorithm for detecting lane on the road. Hough Transform is used for that it is a very fast and robust algorithm, which makes it possible to execute as many frames as possible per frames. Hough Transform is used to get boundary information, so that we could decide if the car is doing lane departure based on the car\u27s position in lane. Later, we move on to use front car\u27s symmetry character to do front car detection, and combine it with Camshift tracking algorithm to fill the gap for failure of detection. Later we introduce camera calibration, stereo calibration, and how to calculate real distance from depth map
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