A Computational Model of the Short-Cut Rule for 2D Shape Decomposition

We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition. The model we proposed generates a set of cut hypotheses passing through the points on the silhouette which represent the negative minima of curvature. We then show that most part-cut hypotheses can be eliminated by analysis of local properties of each. Finally, the remaining hypotheses are evaluated in ascending length order, which guarantees that of any pair of conflicting cuts only the shortest will be accepted. We demonstrate that, compared with state-of-the-art shape decomposition methods, the proposed approach achieves decomposition results which better correspond to human intuition as revealed in psychological experiments.Comment: 11 page

Investigations on skeleton completeness for skeleton-based shape matching

Skeleton is an important shape descriptor for deformable shape matching, because it integrates both geometrical and topological features of a shape. As the skeletonisation process often generates redundant skeleton branches that may seriously disturb the skeleton matching and cause high computational complexity, skeleton pruning is required to remove the inaccurate or redundant branches while preserving the essential topology of the original skeleton. However, pruning approaches normally require manual intervention to produce visually complete skeletons. As different people may have different perceptions for identifying visually complete skeletons, it is unclear how much the accuracy of skeleton-based shape matching is influenced by human selection. Moreover, it is also unclear how skeleton completeness impacts the accuracy of skeleton-based shapematching. We investigate here these two questions in a structured way. In addition, we present experimental evidence to show that it is possible to do automatic skeleton pruning while maintaining the matching accuracy by estimating the approximate pruning power of each shape

Applying spatial reasoning to topographical data with a grounded geographical ontology

Grounding an ontology upon geographical data has been pro- posed as a method of handling the vagueness in the domain more effectively. In order to do this, we require methods of reasoning about the spatial relations between the regions within the data. This stage can be computationally expensive, as we require information on the location of points in relation to each other. This paper illustrates how using knowledge about regions allows us to reduce the computation required in an efficient and easy to understand manner. Further, we show how this system can be implemented in co-ordination with segmented data to reason abou

Image Processing Techniques for Detecting Chromosome Abnormalities

With the increasing use of Fluorescence In Situ Hybridization (FISH) probes as markers for certain genetic sequences, the requirement of a proper image processing framework is becoming a necessity to accurately detect these probe signal locations in relation to the centerline of the chromosome. Automated detection and length measurements based on the centerline relative to the centromere and the telomere coordinates would highly assist in clinical diagnosis of genetic disorders and thus improve its efficiency significantly. Although many image processing techniques have been developed for chromosomal analysis such as ’’karyotype analysis” to assist in laboratory diagnosis, they fail to provide reliable results in segmenting and extracting the centerline of chromosomes due to the high variability in shape of chromosomes on microscope slides. In this thesis we propose a hybrid algorithm that utilizes Gradient Vector Flow active contours, Discrete Curve Evolution based skeleton pruning and morphological thinning to provide a robust and accurate centerline of the chromosome, which is then used for the measurement of the FISH probe signals. Then this centerline information is used to detect the centromere location of the chromosome and the probe signal location distances were measured with respective to these landmarks. The ability to accurately detect FISH probe locations with respective to its centerline and other landmarks can provide the cytogeneticists with detailed information that could lead to a faster diagnosis

Human Metaphase Chromosome Analysis using Image Processing

Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A metric termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4\u27,6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%

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