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Tool condition monitoring is a key component of predictive maintenance in smart manufacturing. Predicting excessive tool wear in machining processes becomes increasingly difficult if different materials need to be processed. We propose a novel computer vision-based system for saw blade condition monitoring that is independent of the processed materials and combines deep learning with classic computer vision. Our approach allows for accurate condition monitoring of blade wear which can further be used for predictive maintenance. Additionally, the system can classify different defect types such as missing blade teeth, thus preventing the production of scrap parts.

Image downscaling is arguably the most frequently used image processing tool. We present an algorithm based on convolutional filters where input pixels contribute more to the output image the more their color deviates from their local neighborhood, which preserves visually important details. In a user study we verify that users prefer our results over related work. Our efficient GPU implementation works in real-time when downscaling images from 24 M to 70 k pixels. Further, we demonstrate empirically that our method can be successfully applied to videos.

We propose a unified rendering approach that jointly handles motion and defocus blur for transparent and opaque objects at interactive frame rates. Our key idea is to create a sampled representation of all parts of the scene geometry that are potentially visible at any point in time for the duration of a frame in an initial rasterization step. We store the resulting temporally-varying fragments (t-fragments) in a bounding volume hierarchy which is rebuild every frame using a fast spatial median construction algorithm. This makes our approach suitable for interactive applications with dynamic scenes and animations. Next, we perform spatial sampling to determine all t-fragments that intersect with a specific viewing ray at any point in time. Viewing rays are sampled according to the lens uv-sampling for depth-of-field effects. In a final temporal sampling step, we evaluate the pre-determined viewing ray/t-fragment intersections for one or multiple points in time. This allows us to incorporate all standard shading effects including transparency. We describe the overall framework, present our GPU implementation, and evaluate our rendering approach with respect to scalability, quality, and performance.

When rendering complex scenes using path-tracing methods, long processing times are required to calculate a sufficient number of samples for high quality results. In this paper, we propose a new method for priority sampling in path-tracing that exploits restrictions of the human visual system by recognizing whether an error is perceivable or not. We use the stationary wavelet transformation to efficiently calculate noise-contrasts in the image based on the standard error of the mean. We then use the Contrast Sensitivity Function and Contrast Masking of the Human Visual System to detect if an error is perceivable for any given pixel in the output image. Errors that can not be detected by a human observer are then ignored in further sampling steps, reducing the amount of samples calculated while producing the same perceived quality. This approach leads to a drastic reduction in the total number of samples required and therefore in total rendering time.

Film sequences generated using image-based rendering techniques are commonly used in broadcasting, especially for sporting events. In many cases, however, image-based rending sequences contain artifacts, and these must be manually located. Here, we propose an algorithm to automatically detect not only the presence of the two most disturbing classes of artifact (popping and ghosting), but also the strength of each instance of an artifact. A simple perceptual evaluation of the technique shows that it performs well.

In this article we use an ElectroEncephaloGraph (EEG) to explore the perception of artifacts that typically appear during rendering and determine the perceptual quality of a sequence of images. Although there is an emerging interest in using an EEG for image quality assessment, one of the main impediments to the use of an EEG is the very low Signal-to-Noise Ratio (SNR) which makes it exceedingly difficult to distinguish neural responses from noise. Traditionally, event-related potentials have been used for analysis of EEG data. However, they rely on averaging and so require a large number of participants and trials to get meaningful data. Also, due the the low SNR ERP's are not suited for single-trial classification. We propose a novel wavelet-based approach for evaluating EEG signals which allows us to predict the perceived image quality from only a single trial. Our wavelet-based algorithm is able to filter the EEG data and remove noise, eliminating the need for many participants or many trials. With this approach it is possible to use data from only 10 electrode channels for single-trial classification and predict the presence of an artifact with an accuracy of 85%. We also show that it is possible to differentiate and classify a trial based on the exact type of artifact viewed. Our work is particularly useful for understanding how the human visual system responds to different types of degradations in images and videos. An understanding of the perception of typical image-based rendering artifacts forms the basis for the optimization of rendering and masking algorithms.

The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae. Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry.

We present several improvements for compression-based multi-resolution rendering of very large volume data sets at interactive to real-time frame rates on standard PC hardware. The algorithm accepts scalar or multi-variant data sampled on a regular grid as input. The input data are converted into a compressed hierarchical wavelet representation in a pre-processing step. During rendering, the wavelet representation is decompressed on-the-fly and rendered using hardware texture mapping. The level-of-detail used for rendering is adapted to the estimated screen-space error. To increase the rendering performance additional visibility tests, such as empty space skipping and occlusion culling, are applied. Furthermore, we discuss how to render the remaining multi-resolution blocks efficiently using modern graphics hardware. Using a prototype implementation of this algorithm we are able to perform a high-quality interactive rendering of large data sets on a single off-the-shelf PC.

The use of multiple Microsoft Kinect has become prominent in the last 2 years and enjoyed widespread acceptance. While several work has been published to mitigate quality degradations in the precomputed depth image, this work focuses on employing an optical flow suitable for dot patterns as employed in the Kinect to retrieve subtle scene data alterations for reconstruction. The method is employed in a multiple Kinect vision architecture to detect the interface of propane flow around occluding objects in air.

We introduce the Kinect as a tool for capturing gas flows around occluders using objects of different aerodynamic properties. Previous approaches have been invasive or require elaborate setups including large printed sheets of complex noise patterns and neat lighting. Our method is easier to set up while still producing good results. We show that three Kinects are sufficient to qualitatively reconstruct nonstationary time varying gas flows in the presence of occluders.