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This paper presents a series of experiments on classification of remotely sensed images, to investigate the effectiveness of various combinations of different types of feature sets, including spectral features, variance features and wavelet-based features. All the experiments use the identical study area, training data, reference data, testing data, and classification algorithm while varying the feature sets. The classification accuracy from different feature sets is evaluated using the traditional accuracy assessment from reference data. The experimental results show that the spectral-based feature set has the basic discrimination power to distinguish classes with middle and high homogeneity value. However, it has little success in correctly classifying classes with low homogeneity value, such as the residential class. Compared with spectral features, the multi-scale wavelet-based feature set can improve the discrimination power for classes with both low and high homogeneity value. The variance-based feature set alone has little discrimination power, no matter what homogeneity level the class has. However, adding the variance features into the spectral feature or wavelet-based feature set can dramatically increase the classification accuracy for classes with low homogeneity value. [ABSTRACT FROM AUTHOR]

In image analysis, recognition of the primitives plays an important role. Subsequent analysis is used to interpret the arrangement of primitives. This subsequent analysis must make allowance for errors or ambiguities in the recognition of primitives. In this paper, we assume that the primitive recognizer produces a set of possible interpretations for each primitive. To reduce this primitive-recognition ambiguity, we use contextual information in the image, and apply constraints from the image domain. This process is variously termed constraint satisfaction, labeling or discrete relaxation. Existing methods for discrete relaxation are limited in that they assume a priori knowledge of the neighborhood model: before relaxation begins, the system is told (or can determine) which sets of primitives are related by constraints. These methods do not apply to image domains in which complex analysis is necessary to determine which primitives are related by constraints. For example, in music notation, we must recognize which notes belong to one measure, before it is possible to apply the constraint that the number of beats in the measure should match the time signature. Such constraints can be handled by our graph-rewriting paradigm for discrete relaxation: here neighborhood-construction is interleaved with constraint-application. In applying this approach to the recognition of simple music notation, we use approximately 180 graph-rewriting rules to express notational constraints and semantic-interpretation rules for music notation. The graph rewriting rules express both binary and higher-order notational constraints. As image-interpretation proceeds, increasingly abstract levels of interpretation are assigned to (groups of) primitives. This allows application of higher-level constraints, which can be formulated only after partial interpretation of the image. [ABSTRACT FROM AUTHOR]

Our aim is to describe a possible class of nonpathological spontaneous movements that has so far received little attention in the scientific literature. These movements arise spontaneously without an underlying pathology such as Huntington's, Parkinson's, cerebral palsy or spinal cord injury. The movements arise in many different contexts including therapeutic, social, religious, and solitary settings. Anecdotal evidence suggests that the movements are related to development and maintenance of form, being part of inherited autoregulatory behaviors and hence bringing an overlooked therapeutic potential. We describe contexts in which they occur, illustrate with case reports, and characterize the movements in terms of their various triggers, movement phenotypes, and conscious and subconscious influences that can occur at both the individual level as well as during collaborative movement relationships between patient and therapist. This description is intended to create a more widespread awareness of the movements, and provide a foundation for future research as to their healing potential., Competing Interests: CONFLICT OF INTEREST NOTIFICATION The authors declare there are no conflicts of interest., (Copyright© The Author(s) 2022. Published by the Massage Therapy Foundation.)

Helmets are widely used as protection against sports-related concussions. The degree of concussion protection offered by a helmet may be related to the fit between the helmet and head. This paper presents the design of a prototype helmet fitting recommendation system using shape-based helmet fitting. The shape-based helmet fitting system uses a Kinect sensor to scan a client's head and then compares the head shape to helmet shapes from a database of off-the-shelf helmets. A slice extraction method is used to compare a standard reference slice extracted from the head to a corresponding slice from the helmet. The degree to which the helmet fits the client's head is calculated and displayed to the user. The prototype system could potentially help a concussion expert make recommendations about helmet fit to clients, if more research about the effects of helmet fitting on concussion protection becomes available.

Vector Symbolic Architectures (VSAs) such as Holographic Reduced Representations (HRRs) are computational associative memories used by cognitive psychologists to model behavioural and neurological aspects of human memory. We present a novel analysis of the mathematics of VSAs and a novel technique for representing data in HRRs. Encoding and decoding in VSAs can be characterised by Latin squares. Successful encoding requires the structure of the data to be orthogonal to the structure of the Latin squares. However, HRRs can successfully encode vectors of locally structured data if vectors are shuffled. Shuffling results are illustrated using images but are applicable to any nonrandom data. The ability to use locally structured vectors provides a technique for detailed modelling of stimuli in HRR models.

Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classification, and provided training and test data sets. So far, these efforts focused on analyzing only the text content of documents. However, as was noted in the KDD'02 text mining contest-where figure-captions proved to be an invaluable feature for identifying documents of interest-images often provide curators with critical information. We examine the possibility of using information derived directly from image data, and of integrating it with text-based classification, for biomedical document categorization. We present a method for obtaining features from images and for using them-both alone and in combination with text-to perform the triage task introduced in the TREC Genomics track 2004. The task was to determine which documents are relevant to a given annotation task performed by the Mouse Genome Database curators. We show preliminary results, demonstrating that the method has a strong potential to enhance and complement traditional text-based categorization methods.