Anders Landström

Morphology-Based Crack Detection for Steel Slabs

Continuous casting is a highly efficient process used to produce most of the world steel production tonnage, but can cause cracks in the semi-finished steel product output. These cracks may cause problems further down the production chain, and detecting them early in the process would avoid unnecessary and costly processing of the defective goods. In order for a crack detection system to be accepted in industry, however, false detection of cracks in non-defective goods must be avoided. This is further complicated by the presence of scales; a brittle, often cracked, top layer originating from the casting process. We present an approach for an automated on-line crack detection system, based on 3D profile data of steel slab surfaces, utilizing morphological image processing and statistical classification by logistic regression. The initial segmentation successfully extracts 80% of the crack length present in the data, while discarding most potential pseudo-defects (non-defect surface features similar to defects). The subsequent statistical classification individually has a crack detection accuracy of over 80% (with respect to total segmented crack length), while discarding all remaining manually identified pseudo-defects. Taking more ambiguous regions into account gives a worst-case false classification of 131 mm within the 30 600 mm long sequence of 150 mm wide regions used as validation data. The combined system successfully identifies over 70% of the manually identified (unambiguous) crack length, while missing only a few crack regions containing short crack segments. The results provide proof-of-concept for a fully automated crack detection system based on the presented method.

Adaptive morphology using tensor-based elliptical structuring elements

Mathematical Morphology is a common strategy for non-linear filtering of image data. In its traditional form the filters used, known as structuring elements, have constant shape once set. Such rigid structuring elements are excellent for detecting patterns of a specific shape, but risk destroying valuable information in the data as they do not adapt in any way to its structure. We present a novel method for adaptive morphological filtering where the local structure tensor, a well-known method for estimation of structure within image data, is used to construct adaptive elliptical structuring elements which vary from pixel to pixel depending on the local image structure. More specifically, their shape varies from lines in regions of strong single-directional characteristics to disks at locations where the data has no prevalent direction.

Adaptive mathematical morphology - A survey of the field

We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences between a few selected methods for adaptive structuring elements are considered, providing perspective on the consequences of different types of adaptivity. We also provide a brief analysis of perspectives and trends within the field, discussing possible directions for future studies.

Voronoi-Based ISD and Site Density Characteristics for Mobile Networks

Inter-Site Distance (ISD) is a common measure for characterizing the site density in a mobile network. However, obtaining a good estimation of the ISD for a real world network is not trivial since the physical layout is usually quite more complex than a perfect theoretical hexagonal grid, due to a number of unavoidable factors such as site availability and traffic density. Voronoi diagrams have been suggested for approximating cells from network layouts, providing a method for partitioning the covered area into cells defined by the proximity to the given set of sites. This yields a framework for site coverage approximation based on the actual site distribution, rather than an underlying theoretical model. We present a novel measure, based on Voronoi diagrams, for characterizing the site density of a cellular network and provide a comparison to the more traditional ISD measure. This measure improves capacity assessments and modeling of real networks.

Sub-Millimeter Crack Detection in Casted Steel Using Color Photometric Stereo

A novel method for automated inspection of small corner cracks in casted steel is presented, using a photometric stereo setup consisting of two light sources of different colors in conjunction with a line-scan camera. The resulting image is separated into two different reflection patterns which are used to cancel shadow effects and estimate the surface gradient. Statistical methods are used to first segment the image and then provide an estimated crack probability for each segmented region. Results show that true cracks are successfully assigned a high crack probability, while only a minor proportion of other regions cause similar probability values. About 80% of the cracks present in the segmented regions are given a crack probability higher than 70%, wile the corresponding number for other non-crack regions is only 5%. The segmented regions contain over 70% of the manually identified crack pixels. We thereby provide proof-of-concept for the presented method.

Transmitter localization for 5G mmWave REMs by stochastic generalized triangulation

Future mobile networks will need new tools to deal with the challenges of emerging technologies. In particular, more flexible networks will require localization of transmitters in the networks. In this work we present a novel method for transmitter localization, suitable for rich multipath mmWave 5G scenarios such as dense urban environments. Our work combines stochastic estimation of Radio Environmental Maps (REMs) with the well known concept of triangulation, generalizing the latter into a method for localization in anisotropic propagation environments. It can be considered a conceptual bridge from classical distance-based triangulation into a generalized version where the propagation environment is taken into account. The result is a highly flexible tool for network planning in general and transmitter localization in particular.

Image reconstruction by prioritized incremental normalized convolution

A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution. The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.

Measurement-Based Stochastic mmWave Channel Modeling

Emerging mmWave technology will require new channel models. Compared to the lower frequency bands, mmWaves will be more reflected and absorbed but less diffracted. Hence, placement of individual physical structures in the environment will affect the propagation much more than before, providing a challenge for channel modeling. At the same time, however, an increasing amount of information about the topology of the physical environment, in particular for buildings, is made available through better measurement equipment and services for obtaining 3D data. We propose a Monte-Carlo approach for channel modeling where interactions between mmWaves and the surrounding small-scale environment can be included, given a stochastic representation. This method is not only suitable for assessment of basic effects such as material reflection and absorption, but can also in the future be extended to various additional effects such as weather, traffic, foliage, etc. The framework is verified against 15 GHz measurements from an urban environment, demonstrating how major reflection paths can be replicated by modeling the closest buildings.

An Approach to Adaptive Quadratic Structuring Functions Based on the Local Structure Tensor

Classical morphological image processing, where the same structuring element is used to process the whole image, has its limitations. Consequently, adaptive mathematical morphology is attracting more and more attention. So far, however, the use of non-flat adaptive structuring functions is very limited. This work presents a method for defining quadratic structuring functions from the well known local structure tensor, building on previous work for flat adaptive morphology. The result is a novel approach to adaptive mathematical morphology, suitable for enhancement and linking of directional features in images. Moreover, the presented strategy can be quite efficiently implemented and is easy to use as it relies on just two user-set parameters which are directly related to image measures.

Adaptive Morphological Filtering of Incomplete Data

We demonstrate how known convolution techniques for uncertain data can be used to set the shapes of structuring elements in adaptive mathematical morphology, enabling robust morphological processing of partially occluded or otherwise incomplete data. Results are presented for filtering of both gray-scale images containing missing data and 3D profile data where information is missing due to occlusion effects. The latter demonstrates the intended use of the method: enhancement of crack signatures in a surface inspection system for casted steel. The presented method is able to disregard unreliable data in a systematic and robust way, enabling adaptive morphological processing of the available information while avoiding any false edges or other unwanted features introduced by the values of faulty pixels.