Arthritic Hand-Finger Movement Similarity Measurements: Tolerance Near Set Approach

The problem considered in this paper is how to measure the degree of resemblance between nonarthritic and arthritic hand movements during rehabilitation exercise. The solution to this problem stems from recent work on a tolerance space view of digital images and the introduction of image resemblance measures. The motivation for this work is both to quantify and to visualize differences between hand-finger movements in an effort to provide clinicians and physicians with indications of the efficacy of the prescribed rehabilitation exercise. The more recent introduction of tolerance near sets has led to a useful approach for measuring the similarity of sets of objects and their application to the problem of classifying image sequences extracted from videos showing finger-hand movement during rehabilitation exercise. The approach to measuring the resemblance between hand movement images introduced in this paper is based on an application of the well-known Hausdorff distance measure and a tolerance nearness measure. The contribution of this paper is an approach to measuring as well as visualizing the degree of separation between images in arthritic and nonarthritic hand-finger motion videos captured during rehabilitation exercise

Metric free nearness measure using description-based neighbourhoods

Preprint versionThe focus of this paper is on a metric free nearness measure for quantifying the descriptive nearness of digital images. Regions of Interest (ROI) play an important role in discerning perceptual similarity within a single image, or between a pair of images. In terms of pixels, closeness between ROIs can be assessed in light of the traditional closeness between points and sets and closeness between sets using topology or proximity theory. A metric free nearness measure is introduced in this paper by finding common patterns among disjoint description based neighbourhoods obtained from these spatially defined sets. The contribution of this article is a metric free nearness measure implemented within the Proximity System, an application used to demonstrate near set concepts using digital images.https://link.springer.com/article/10.1007/s11786-013-0141-

Descriptive Topological Spaces for Performing Visual Search

Accepted versionThis article presents an approach to performing the task of visual search in the context of descriptive topological spaces. The presented algorithm forms the basis of a descriptive visual search system (DVSS) that is based on the guided search model (GSM) that is motivated by human visual search. This model, in turn, consists of the bottom-up and top-down attention models and is implemented within the DVSS in three distinct stages. First, the bottom-up activation process is used to generate saliency maps and to identify salient objects. Second, perceptual objects, defined in the context of descriptive topological spaces, are identified and associated with feature vectors obtained from a VGG deep learning convolutional neural network. Lastly, the top-down activation process makes decisions on whether the object of interest is present in a given image through the use of descriptive patterns within the context of a descriptive topological space. The presented approach is tested with images from the ImageNet ILSVRC2012 and SIMPLIcity datasets. The contribution of this article is a descriptive pattern-based visual search algorithm."This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 418413, and the Faculty of Graduate Studies at the University of Winnipeg."https://link.springer.com/chapter/10.1007/978-3-662-58768-3_

Quantifying nearness in visual spaces

Preprint versionCybernetic vision systems can be deployed in problem domains where the goal is to achieve results similar to those produced by humans. Fundamentally, these problems consist of evaluation of image content between sets of images. This article contrasts two theoretical frameworks for image comparison, namely, the semantic similarity approach used in the earth mover's distance (EMD) and the integrated region matching (IRM) similarity measure, with the tolerance nearness measure (tNM) based on near set theory. The contribution of this article is a comparison of the image similarity measures EMD, IRM, and tNM, as well as a signature-based approach to calculating the tolerance nearness measure."This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant 194376, the University of Winnipeg Research Start-Up Grant, and the University of Winnipeg Major Research Grant."https://www.tandfonline.com/doi/abs/10.1080/01969722.2012.73279

Signature-based perceptual nearness: Application of near sets to image retrieval

Preprint versionThis paper presents a signature-based approach to quantifying perceptual nearness of images. A signature is defined as a set of descriptors, where each descriptor consists of a real-valued feature vector associated with a digital image region (set of pixels) combined with a region-based weight. Tolerance near sets provide a formal framework for our application of near sets to image retrieval. The tolerance nearness measure tNM was created to demonstrate application of near set theory to the problem of image correspondence. A new form of tNM has been introduced in this work, which takes into account the region size. Our method is compared to two other well-known image similarity measures: earth movers distance (EMD) and integrated region matching (IRM).https://link.springer.com/article/10.1007/s11786-013-0145-

A Descriptive Tolerance Nearness Measure for Performing Graph Comparison

Accepted versionThis article proposes the tolerance nearness measure (TNM) as a computationally reduced alternative to the graph edit distance (GED) for performing graph comparisons. The TNM is defined within the context of near set theory, where the central idea is that determining similarity between sets of disjoint objects is at once intuitive and practically applicable. The TNM between two graphs is produced using the Bron-Kerbosh maximal clique enumeration algorithm. The result is that the TNM approach is less computationally complex than the bipartite-based GED algorithm. The contribution of this paper is the application of TNM to the problem of quantifying the similarity of disjoint graphs and that the maximal clique enumeration-based TNM produces comparable results to the GED when applied to the problem of content-based image processing, which becomes important as the number of nodes in a graph increases."This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 418413."https://content.iospress.com/articles/fundamenta-informaticae/fi174

Arthritic hand-finger movement similarity measurements: Tolerance near set approach

The problem considered in this paper is how to measure the degree of resemblance between nonarthritic and arthritic hand movements during rehabilitation exercise. The solution to this problem stems from recent work on a tolerance space view of digital images and the introduction of image resemblance measures. The motivation for this work is both to quantify and to visualize differences between hand-finger movements in an effort to provide clinicians and physicians with indications of the efficacy of the prescribed rehabilitation exercise. The more recent introduction of tolerance near sets has led to a useful approach for measuring the similarity of sets of objects and their application to the problem of classifying image sequences extracted from videos showing finger-hand movement during rehabilitation exercise. The approach to measuring the resemblance between hand movement images introduced in this paper is based on an application of the well-known Hausdorff distancemeasure and a tolerance nearness measure. The contribution of this paper is an approach to measuring as well as visualizing the degree of separation between images in arthritic and nonarthritic hand-finger motion videos captured during rehabilitation exercise.https://www.hindawi.com/journals/cmmm/2011/569898

Felt Knowledge

Bodies, inclusive of flesh|bodies and object|bodies, show the wear brought upon them by physical labour; the particularity of their history engrained upon the skin|surface of both sentient and non-sentient entities. Although the discrete life experienced by the object|body cannot be recalled mnemonically, the developments on a surface can be partially decoded, revealing an archive of gestures brought about purposefully and incidentally. Without the capacity for verbal expression, the archive of marks left on the object|body’s surface cannot be relayed as a narrative of events. Instead, it is left to those with flesh|bodies to seek an understanding through touch. As touch binds two bodies together through gesture, they merge and dissolve the boundaries of their singularity, momentarily entering into shared understanding. In this way, the archive of trace left on surface is an ongoing experience of the labour that created it, perpetuating the act by grafting present and past together