LITERATURE REVIEW ON METHODS OF CLASSIFICATION IN PATTERN RECOGNITION

2019 ◽  
Vol 44 (1) ◽  
pp. 85-102
Author(s):  
KHIMYA S. TINANI ◽  
◽  
DEEPA H. KANDPAL ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 3993
Author(s):  
Andrew Pivovar ◽  
Patrick Oellers

Purpose: To review novel findings in research with ultra-widefield imaging for analysis of peripheral manifestations in macular degeneration (AMD). We introduce the evolving widefield imaging modalities while summarizing the analytical techniques used in data collection of peripheral retinal findings thus far. Our review provides a summary of advancements to date and a commentary on future direction for AMD research. Methods: This is a literature review of all significant publications focused on the relationship between AMD and the retinal periphery conducted within the last two decades. Results and Conclusion: Promising research has been undertaken to elucidate peripheral retinal manifestations in macular degeneration using novel methodology. Advancements in ultra-widefield imaging and fundus autofluorescence have allowed us to elucidate peripheral retinal pigmentary changes, drusen deposition, and much more. Novel grid overlay techniques have been introduced to aid in analyzing these changes for pattern recognition and grouping of findings. This review discusses these findings in detail, providing evidence for the pan-retinal manifestations of AMD. Inter-study discordance in analytical approach highlights a need for more systematic future study.


2021 ◽  
pp. 103985622110540
Author(s):  
Graeme C Smith

Objective: To explore the theme identified by Bagster et al.1 in their selective psychiatric literature review that formulation can appear daunting. Conclusion: Formulation is understandably daunting, even though it occurs in all human encounters. The plural nature of mental symptoms is such that anxiety-provoking intuitive judgement is required at all points in both the process and explication of formulation, a type of instinctive guessing. There are no rules for this, because the laws of vertical integration of systems are not established. Guidelines are more appropriate than ‘instructions’. Much of the wider mental health and clinical reasoning literature addresses intuitive judgement, but the current psychiatric literature tends to focus on pattern recognition as a deliberative cognitive act of Type 2 processes. Arguably this reductionism adds to the dauntingness. Anxiety detected about the intuitive judgement involved can be addressed in supervision, taking into account the psychological mindedness of the trainee.


2020 ◽  
Vol 1 (2) ◽  
pp. 22-36
Author(s):  
Tobias Ribeiro Sombra ◽  
Rose Marie Santini ◽  
Emerson Cordeiro Morais ◽  
Walmir Oliveira Couto ◽  
Alex de Jesus Zissou ◽  
...  

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Shekeeb S Mohammad ◽  
Rajeshwar Reddy Angiti ◽  
Andrew Biggin ◽  
Hugo Morales-Briceño ◽  
Robert Goetti ◽  
...  

Abstract Bilateral basal ganglia abnormalities on MRI are observed in a wide variety of childhood disorders. MRI pattern recognition can enable rationalization of investigations and also complement clinical and molecular findings, particularly confirming genomic findings and also enabling new gene discovery. A pattern recognition approach in children with bilateral basal ganglia abnormalities on brain MRI was undertaken in this international multicentre cohort study. Three hundred and five MRI scans belonging to 201 children with 34 different disorders were rated using a standard radiological scoring proforma. In addition, literature review on MRI patterns was undertaken in these 34 disorders and 59 additional disorders reported with bilateral basal ganglia MRI abnormalities. Cluster analysis on first MRI findings from the study cohort grouped them into four clusters: Cluster 1—T2-weighted hyperintensities in the putamen; Cluster 2—T2-weighted hyperintensities or increased MRI susceptibility in the globus pallidus; Cluster 3—T2-weighted hyperintensities in the globus pallidus, brainstem and cerebellum with diffusion restriction; Cluster 4—T1-weighted hyperintensities in the basal ganglia. The 34 diagnostic categories included in this study showed dominant clustering in one of the above four clusters. Inflammatory disorders grouped together in Cluster 1. Mitochondrial and other neurometabolic disorders were distributed across clusters 1, 2 and 3, according to lesions dominantly affecting the striatum (Cluster 1: glutaric aciduria type 1, propionic acidaemia, 3-methylglutaconic aciduria with deafness, encephalopathy and Leigh-like syndrome and thiamine responsive basal ganglia disease associated with SLC19A3), pallidum (Cluster 2: methylmalonic acidaemia, Kearns Sayre syndrome, pyruvate dehydrogenase complex deficiency and succinic semialdehyde dehydrogenase deficiency) or pallidum, brainstem and cerebellum (Cluster 3: vigabatrin toxicity, Krabbe disease). The Cluster 4 pattern was exemplified by distinct T1-weighted hyperintensities in the basal ganglia and other brain regions in genetically determined hypermanganesemia due to SLC39A14 and SLC30A10. Within the clusters, distinctive basal ganglia MRI patterns were noted in acquired disorders such as cerebral palsy due to hypoxic ischaemic encephalopathy in full-term babies, kernicterus and vigabatrin toxicity and in rare genetic disorders such as 3-methylglutaconic aciduria with deafness, encephalopathy and Leigh-like syndrome, thiamine responsive basal ganglia disease, pantothenate kinase-associated neurodegeneration, TUBB4A and hypermanganesemia. Integrated findings from the study cohort and literature review were used to propose a diagnostic algorithm to approach bilateral basal ganglia abnormalities on MRI. After integrating clinical summaries and MRI findings from the literature review, we developed a prototypic decision-making electronic tool to be tested using further cohorts and clinical practice.


2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Gilberto Astolfi ◽  
Fábio Prestes Cesar Rezende ◽  
João Vitor De Andrade Porto ◽  
Edson Takashi Matsubara ◽  
Hemerson Pistori

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.


2018 ◽  
Author(s):  
Sylvain Christin ◽  
Éric Hervet ◽  
Nicolas Lecomte

AbstractA lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.


Author(s):  
Poonam Bhanudas Abhale

Abstract: Character recognition is a process by which a computer recognizes letters, figures, or symbols and turns them into a digital form that a computer can use. In moment’s terrain character recognition has gained a lot of attention in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks, form recycling systems, and numerous further. Character recognition is one of the well- liked and grueling areas of exploration. In the unborn character recognition produce a paperless terrain. In this paper, we describe the detailed study of the being system for handwritten character recognition. We give a literature review on colorful ways used in offline English character recognition. Keywords: Character; Character recognition; Preprocessing; Segmentation; Point birth; Bracket; neural network; Convolution neural network.


Author(s):  
G.Y. Fan ◽  
J.M. Cowley

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.


Author(s):  
L. Fei ◽  
P. Fraundorf

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.


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