Syntactic Pattern Recognition in Computer Vision

Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.

(Hyper)graph Kernels over Simplicial Complexes

Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered. In this paper, four (hyper)graph kernels are proposed and their efficiency and effectiveness are compared in a twofold fashion. First, by inferring the simplicial complexes on the top of underlying graphs and by performing a comparison among 18 benchmark datasets against state-of-the-art approaches; second, by facing a real-world case study (i.e., metabolic pathways classification) where input data are natively represented by hypergraphs. With this work, we aim at fostering the extension of graph kernels towards hypergraphs and, more in general, bridging the gap between structural pattern recognition and the domain of hypergraphs.

Patterns of Suffering in Palliative Care: A Descriptive Study

Suffering experiences are common phenomena in palliative care. In this study, we aim to explore the different patterns of suffering in palliative care. Adult palliative care patients were recruited from the University of Malaya Medical Centre. Suffering scores were charted 3 times a day for a week. The characteristics of the suffering charts were analyzed using SPSS. The patterns of suffering were analyzed using structural pattern recognition. A total of 53 patients participated. The overall trends of suffering were downward (64%), upward (19%), and stable (17%). Median minimum and maximum suffering scores were 2/10 and 6/10, with an average of 3.6/10. Nine patterns of suffering were recognized from categorizing two key characteristics of suffering (intensity and fluctuation)—named S1 to S9. Understanding the different patterns of suffering may lead to better suffering management.