Fast and robust curve skeletonization for real-world elongated objects

We consider the problem of extracting curve skeletons of three-dimensional, elongated objects given a noisy surface, which has applications in agricultural contexts such as extracting the branching structure of plants. We describe an efficient and robust method based on breadth-first search that can determine curve skeletons in these contexts. Our approach is capable of automatically detecting junction points as well as spurious segments and loops. All of that is accomplished with only one user-adjustable parameter. The run time of our method ranges from hundreds of milliseconds to less than four seconds on large, challenging datasets, which makes it appropriate for situations where real-time decision making is needed. Experiments on synthetic models as well as on data from real world objects, some of which were collected in challenging field conditions, show that our approach compares favorably to classical thinning algorithms as well as to recent contributions to the field.Comment: 47 pages; IEEE WACV 2018, main paper and supplementary materia

Aspects of precommercial thinning in heterogeneous forests in southern Sweden

The overall objective of the work underlying this thesis was to suggest and evaluate possible strategies for the tending of young heterogeneous stands of Norway spruce, Scots pine and birch in southern Sweden. Heterogeneity was defined as variation in species composition, height distribution and spatial arrangement of the trees. The influence of stand density after precommercial thinning and timing of thinning on the diameter of the thickest branch was studied for naturally regenerated Scots pine. The branch diameter was found to decrease with increasing number of remaining stems after precommercial thinning. However, leaving very dense stands (> 3000 stems ha-1) resulted only in a minor reduction of the branch diameter. Late precommercial thinning, compared to early, reduced the branch diameter. The influence of the precommercial thinning regime on the crown ratio (living crown length/tree height) was also analysed. To be able to simulate the influence of different management options on the development of the young forest, single-tree growth models was developed for Scots pine, Norway spruce and birch. Height growth and diameter was estimated as a function of tree height, stand and site variables. Growth reduction due to competition was estimated using individual, distance independent indices as well as expressions of the overall stand density. In the third study the influence of stand structure after precommercial thinning on the development of mixtures between Norway spruce and silver birch was simulated. The aim was to identify mixtures that allowed both species to develop well until the first commercial thinning. By leaving birches with an average height slightly greater than spruce at precommercial thinning, a large proportion of competitive birches were available at first commercial thinning, at the same time as the relative diameter distribution of spruce in the mixture was equal to that of a pure spruce stand of the same density. The height difference between the species as well as the species proportion had a decisive impact on volume production. In the fourth study different precommercial thinning strategies were identified and applied to a heterogeneous stand including Scots pine, Norway spruce and birch. Stand development and economical returns over a rotation was estimated using a set of empirical models. The aim of the long-term strategies was: (i) a conifer dominated stand with focus on high production, (ii) a conifer dominated stand with focus on high timber quality, (iii) to preserve the heterogeneous stand structure, (iv) a mosaic pattern by tree species, (v) to reduce the precommercial thinning cost, without jeopardizing the future stand development. The difference in total volume production was found to be relatively small between the strategies. The lowest production was found for the strategies promoting species mixture at tree level (iii) and group level (iv). The net present value was highest for the strategy aiming at high production (ii) and lowest for the strategy aiming at preserved heterogeneity (iii). The minimal precommercial thinning (v) was a less profitable alternative, mainly because of an expensive first commercial thinning. Differences in timber quality were not considered in the simulations. The case study illustrates the possibilities for influencing the structure of a heterogeneous stand through precommercial thinning, as well as the limitations imposed by the initial stand structure

An improved rotation-invariant thinning algorithm

Ahmed & Ward have recently presented an elegant, rule-based rotation-invariant thinning algorithm to produce a single-pixel wide skeleton from a binary image. We show examples where this algorithm fails on two-pixel wide lines and propose a modified method which corrects this shortcoming based on graph connectivity

Improving the Performance of Thinning Algorithms with Directed Rooted Acyclic Graphs

In this paper we propose a strategy to optimize the performance of thinning algorithms. This solution is obtained by combining three proven strategies for binary images neighborhood exploration, namely modeling the problem with an optimal decision tree, reusing pixels from the previous step of the algorithm, and reducing the code footprint by means of Directed Rooted Acyclic Graphs. A complete and open-source benchmarking suite is also provided. Experimental results confirm that the proposed algorithms clearly outperform classical implementations

Statistical methods for tissue array images - algorithmic scoring and co-training

Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm - Tissue Array Co-Occurrence Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high-dimensional setting when there is "sufficient" redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists' performance in terms of accuracy and repeatability.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS543 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

One DAG to Rule Them All

In this paper, we present novel strategies for optimizing the performance of many binary image processing algorithms. These strategies are collected in an open-source framework, GRAPHGEN, that is able to automatically generate optimized C++ source code implementing the desired optimizations. Simply starting from a set of rules, the algorithms introduced with the GRAPHGEN framework can generate decision trees with minimum average path-length, possibly considering image pattern frequencies, apply state prediction and code compression by the use of Directed Rooted Acyclic Graphs (DRAGs). Moreover, the proposed algorithmic solutions allow to combine different optimization techniques and significantly improve performance. Our proposal is showcased on three classical and widely employed algorithms (namely Connected Components Labeling, Thinning, and Contour Tracing). When compared to existing approaches —in 2D and 3D—, implementations using the generated optimal DRAGs perform significantly better than previous state-of-the-art algorithms, both on CPU and GPU

Optimal Decision Trees for Local Image Processing Algorithms

In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connected components labeling and thinning, to show how these can be represented with decision tables, and the benets of their implementation as optimal decision trees in terms of reduced memory accesses. Experiments are reported, to show the computational time improvements over state of the art implementations

Aspects of automation of selective cleaning

Cleaning (pre-commercial thinning) is a silvicultural operation, primarily used to improve growing conditions of remaining trees in young stands (ca. 3 - 5 m of height). Cleaning costs are considered high in Sweden and the work is laborious. Selective cleaning with autonomous artificial agents (robots) may rationalise the work, but requires new knowledge. This thesis aims to analyse key issues regarding automation of cleaning; suggesting general solutions and focusing on automatic selection of main-stems. The essential requests put on cleaning robots are to render acceptable results and to be cost competitive. They must be safe and be able to operate independently and unattended for several hours in a dynamic and non-deterministic environment. Machine vision, radar, and laser scanners are promising techniques for obstacle avoidance, tree identification, and tool control. Horizontal laser scannings were made, demonstrating the possibility to find stems and make estimations regarding their height and diameter. Knowledge regarding stem selections was retrieved through qualitative interviews with persons performing cleaning. They consider similar attributes of trees, and these findings and current cleaning manuals were used in combination with a field inventory in the development of a decision support system (DSS). The DSS selects stems by the attributes species, position, diameter, and damage. It was used to run computer-based simulations in a variety of young forests. A general follow-up showed that the DSS produced acceptable results. The DSS was further evaluated by comparing its selections with those made by experienced cleaners, and by a test in which laymen performed cleanings following the system. The DSS seems to be useful and flexible, since it can be adjusted in accordance with the cleaners’ results. The laymen’s results implied that the DSS is robust and that it could be used as a training tool. Using the DSS in automatic, or semi-automatic, cleaning operations should be possible if and when selected attributes can be automatically perceived. A suitable base-machine and thorough research, regarding e.g. safety, obstacle avoidance, and target identification, is needed to develop competitive robots. However, using the DSS as a training-tool for inexperienced cleaners could be an interesting option as of today