The work in this paper is focused around visual ADAS (Advanced Driver Assistance Systems). We introduce forward rectification – a technique for making computer vision algorithms more robust against camera mount point and mount angles. Using the technique can increase the quality of recognition as well as lower the dimensionality for algorithm invariance, making it possible to apply simpler affine-invariant algorithms for applications that require projective invariance. Providing useful results this rectification requires thorough calibration of the camera, which can be done automatically or semi-automatically. The technique is of general nature and can be applied to different algorithms, such as pattern matching detectors, convolutional neural networks. The applicability of the technique is demonstrated on HOG-based car detector detection rate.
We study the issue of performance improvement of classification-based object detectors by including certain geometric-oriented filters. Configurations of the observed 3D scene may be used as a priori or a posteriori information for object filtration. A priori information is used to select only those object parameters (size and position on image plane) that are in accordance with the scene, restricting implausible combinations of parameters. On the other hand the detection robustness can be enhanced by rejecting detection results using a posteriori information about 3D scene. For example, relative location of detected objects can be used as criteria for filtration. We have included proposed filters in object detection modules of two different industrial vision-based recognition systems and compared the resulting detection quality before detectors improving and after. Filtering with a priori information leads to significant decrease of detector's running time per frame and increase of number of correctly detected objects. Including filter based on a posteriori information leads to decrease of object detection false positive rate.
The artifacts (known as metal-like artifacts) arising from incorrect reconstruction may obscure or simulate pathology in medical applications, hide or mimic cracks and cavities in the scanned objects in industrial tomographic scans. One of the main reasons caused such artifacts is photon starvation on the rays which go through highly absorbing regions. We indroduce a way to suppress such artifacts in the reconstructions using soft penalty mimicing linear inequalities on the photon starved rays. An efficient algorithm to use such information is provided and the effect of those inequalities on the reconstruction quality is studied.
A method of determining of the road shape and direction is proposed. The road can potentially have curved shape as well as be seen unclearly due to weather effects or relief features. The proposed method uses video taken from frontal camera that is rigidly placed in car as an input data. The method is based on self-similarity of typical road image, i.e. the
smaller image inside the road is close to downscaled initial image.