66 research outputs found
A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses
We propose a novel multi-view camera model for line-scan cameras with telecentric lenses. The camera model supports an arbitrary number of cameras and assumes a linear relative motion with constant velocity between the cameras and the object. We distinguish two motion configurations. In the first configuration, all cameras move with independent motion vectors. In the second configuration, the cameras are mounted rigidly with respect to each other and therefore share a common motion vector. The camera model can model arbitrary lens distortions by supporting arbitrary positions of the line sensor with respect to the optical axis. We propose an algorithm to calibrate a multi-view telecentric line-scan camera setup. To facilitate a 3D reconstruction, we prove that an image pair acquired with two telecentric line-scan cameras can always be rectified to the epipolar standard configuration, in contrast to line-scan cameras with entocentric lenses, for which this is possible only under very restricted conditions. The rectification allows an arbitrary stereo algorithm to be used to calculate disparity images. We propose an efficient algorithm to compute 3D coordinates from these disparities. Experiments on real images show the validity of the proposed multi-view telecentric line-scan camera model
A camera model for cameras with hypercentric lenses and some example applications
We propose a camera model for cameras with hypercentric lenses. Because of their geometry, hypercentric lenses allow to image the top and the sides of an object simultaneously. This makes them useful for certain inspections tasks, for which otherwise multiple images would have to be acquired and stitched together. After describing the projection geometry of hypercentric lenses, we derive a camera model for hypercentric lenses that is intuitive for the user. Furthermore, we describe how to determine the parameter values of the model by calibrating the camera with a planar calibration object. We also apply our camera model to two example applications: in the first application, we show how two cameras with hypercentric lenses can be used for dense 3D reconstruction. For an efficient reconstruction, the images are rectified such that corresponding points occur in the same image row. Standard rectification methods would result in perspective distortions in the images that would prevent stereo matching algorithms from robustly establishing correspondences. Therefore, we propose a new rectification method for objects that are approximately cylindrical in shape, which enables a robust and efficient reconstruction. In the second application, we show how to unwrap cylindrical objects to simplify further inspection tasks. For the unwrapping, the pose of the cylinder must be known. We show how to determine the pose of the cylinder based on a single camera image and based on two images of a stereo camera setup
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses slight localization inaccuracies around edges. It also fails to
reveal defective regions that have been visually altered when intensity values
stay roughly consistent. We show that these problems prevent these approaches
from being applied to complex real-world scenarios and that it cannot be easily
avoided by employing more elaborate architectures such as variational or
feature matching autoencoders. We propose to use a perceptual loss function
based on structural similarity which examines inter-dependencies between local
image regions, taking into account luminance, contrast and structural
information, instead of simply comparing single pixel values. It achieves
significant performance gains on a challenging real-world dataset of
nanofibrous materials and a novel dataset of two woven fabrics over the state
of the art approaches for unsupervised defect segmentation that use pixel-wise
reconstruction error metrics
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
We introduce a powerful student-teacher framework for the challenging problem
of unsupervised anomaly detection and pixel-precise anomaly segmentation in
high-resolution images. Student networks are trained to regress the output of a
descriptive teacher network that was pretrained on a large dataset of patches
from natural images. This circumvents the need for prior data annotation.
Anomalies are detected when the outputs of the student networks differ from
that of the teacher network. This happens when they fail to generalize outside
the manifold of anomaly-free training data. The intrinsic uncertainty in the
student networks is used as an additional scoring function that indicates
anomalies. We compare our method to a large number of existing deep learning
based methods for unsupervised anomaly detection. Our experiments demonstrate
improvements over state-of-the-art methods on a number of real-world datasets,
including the recently introduced MVTec Anomaly Detection dataset that was
specifically designed to benchmark anomaly segmentation algorithms.Comment: Accepted to CVPR 202
Subpixel-Precise Extraction of Watersheds
An approach to extract watersheds and watercourses, as well as their corresponding valleys and hills, from images with subpixel precision is proposed. The critical points of the terrain are essential as the starting points for the construction of these separatrices. They are extracted efficiently with subpixel precision using an approach based on derivatives of Gaussian filters. The separatrices are extracted by integrating their defining differential equation. Finally, the hills and valleys are constructed by an efficient graph search algorithm. Examples show the quality of the results that can be achieved with the proposed approach. 1 Introduction Watersheds and watercourses are important geomorphological features, which play an important role in hydrological GIS applications. Intuitively, watersheds can be regarded as the lines that separate the area where water drains to different locations. The areas that are enclosed by the watersheds are precisely the regions where water drain..
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