A technique for obtaining subpixel resolution when tracking through cross-correlation consists of interpolating the obtained function and then refine the peak location. Although the technique provides accurate location results, the peak is always biased towards the closest integer coordinate. This effect is known as peak-locking error and is a major limit to the experimental accuracy of this calculation technique. This error may be different depending on the algorithm used to fit and interpolate the correlation peak but no systematic analysis was found in the literature. In our study we explore the three most common interpolation methods: thin-plate splines, second-order polynomial fit and Gaussian fit together with the influence of the extent of local interpolation area around the peak. Additionally, we have checked the influence of the image blurring on the results, since it is reported as one effective method to reduce the peak-locking error. Finally, the optimal adjustment found is the Gaussian fit with no blur and a neighborhood around the correlation peak of 11x11 pixels.
Some materials undergo an hygric expansion when they are soaked. In porous rocks, this effect is enhanced by the pore space that allows the water to reach every part of its volume and to hydrate the most of their swelling parts. This enlargement has negative structural consequences in the vicinity since adjacent elements will support some compressions or displacements. Recently image-based methods have arisen in this field due to their advantages versus traditional methods. Among all image processing methods, digital image correlation (DIC) is one of the most used in all areas. In this work, we propose a new methodology based on DIC for the calculation of the hygric expansion of materials. We use porous sandstone, with dimensions 14x14x30 mm to measure its hygric swelling using an industrial digital camera and a telecentric objective. We took one image every 5 minutes to characterize the whole swelling process. Due the large magnification, the whole 14 mm length of one contour was not in the image and therefore we lost the image scale reference. To solve this, a 1951 USAF test was used to calibrate the imagen. The telecentric objective and a narrow deep of field allowed to have the specimen surface exactly on the same plane that the USAF test was during the calibration. The image was pointed to one corner of the specimen, to obtain information not only of its vertical displacement due to its expansion but also of its horizontal movement. Preliminary results show that the proposed methodology provides reliable information of the hygric swelling using a non-contact methodology, with an accuracy of 1 micron.
Object tracking with subpixel accuracy often needs targets with special shapes, which include random dot patterns, circular objects or targets with irregular contours. Unfortunately, in majority of real applications no such objects are found or cannot be attached to the region of interest and non-optimal objects must be used. Among those, we will analyze here the performance of rectangular objects that may be common in natural or artificial targets. Object tracking has been performed through the two most common methods that appear in the literature: centroid calculation and cross-correlation with peak interpolation. Numerical simulations show that tracking results for such objects are highly dependent of the object orientation with respect to the direction of movement. This is due to the interference of the object borders and the sensor and how is the subpixel information obtained. Best results are obtained for object orientations from 5 to 30 deg with respect to the normal to the displacement directions. Experimental results confirm the simulations and allow us to establish that, although object alignment provides a better image on the sensor with sharper borders, this situation is not desirable for accurate subpixel tracking, with accuracies varying from accuracies of 0.08 px (RMS error) with aligned objects to 0.02px with a misaligned target.
The behaviour of a construction safety net and its supporting structure was monitored with a high speed camera and
image processing techniques. A 75 kg cylinder was used to simulate a falling human body from a higher location in a
sloped surface of a building under construction. The cylinder rolled down over a ramp until it reaches the net. The
behaviour of the net and its supporting structure was analysed through the movement of the cylinder once it reaches the
net. The impact was captured from a lateral side with a high speed camera working at 512 frames per second. In order to
obtain the cylinder position each frame of the sequence was binarized. Through morphological image processing the
contour of the cylinder was isolated from the background and with a Hough transform the presence of the circle was
detected. With this, forces and accelerations applying on the net and the supporting structure have been described,
together with the trajectory of the cylinder. All the experiment has been done in a real structure in outdoors location.
Difficulties found in the preparation on the experiment and in extracting the final cylinder contour are described and
some recommendations are giving for future implementations.
Analysis of vibrations and displacements is a hot topic in structural engineering. Video cameras can provide good
accuracy at reasonable cost. Proper system configuration and adequate image processing algorithms provide a reliable
method for measuring vibrations and displacements in structures. In this communication we propose using a pocket
camera (Casio) for measuring small vibrations and displacements. Low end cameras can acquire high speed video
sequences at very low resolutions. Nevertheless, many applications do not need precise replication of the scene, but
detecting its relative position. By using targets with known geometrical shapes we are able to mathematically obtain
subpixel information about its position and thus increase the system resolution. The proposal is demonstrated by using
circular and elliptic targets on moving bodies The used method combines image processing and least squares fitting and
the obtained accuracy multiplies by 10 the original resolution. Results form the low-end camera (400 euros) working at
224×168 px are compared with those obtained with a high-end camera (10000 euros) with a spatial resolution of 800×560 px.
Although the low-end camera introduces a lot of noise in the detected trajectory, we obtained that results are comparable.
Thus for particular applications, low-end pocket cameras can be a real alternative to more sophisticated and expensive