scholarly journals Shape Completion Using Deep Boltzmann Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Zheng Wang ◽  
Qingbiao Wu
Keyword(s):  
Image Processing ◽  
Regression Model ◽  
Prior Information ◽  
Shape Feature ◽  
Boltzmann Machine ◽  
Shape Features ◽  
Shape Information ◽  
Object Shape ◽  
Deep Boltzmann Machine ◽  
Shape Completion

Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with the distribution of the shapes, it is quite easy to acquire the result by sampling from the model. In this paper, we make use of the hidden activation of the DBM and incorporate it with the convolutional shape features to fit a regression model. We compare the output of the regression model with the incomplete shape feature in order to set a proper and compact mask for sampling from the DBM. The experiment shows that our method can obtain realistic results without any prior information about the incomplete object shape.

scholarly journals Microfilariae Classification Using Multiple Classifiers for Color and Shape Features

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Faroq AL-Tam ◽  
António dos Anjos ◽  
Sébastien Pion ◽  
Michel Boussinesq ◽  
Hamid Reza Shahbazkia
Keyword(s):  
Feature Vector ◽  
Shape Descriptor ◽  
Experimental Results ◽  
Shape Feature ◽  
Shape Features ◽  
Posterior Probabilities ◽  
Shape Information ◽  
Color Feature ◽  
Feature Vectors ◽  
Multiple Classifiers

Abstract This paper presents a multi-classifier approach for classifying microfilariae in 2-D images. A shape descriptor based on the quench function is described. This descriptor is represented as a feature vector that encodes the shape information. The color feature vector is calculated as a histogram. Two classifiers were used to train both color and shape feature vectors, one for each vector. The posterior probabilities calculated from the scores of each classifier are then used to calculate the final classification decision. The experimental results show that, although the proposed approach is simple, it is efficient when compared to various approaches.

Contractive Slab and Spike Convolutional Deep Boltzmann Machine

2018 ◽  
Vol 290 ◽  
pp. 208-228 ◽  
Author(s):  
Bi Xiaojun ◽  
Wang Haibo
Keyword(s):  
Boltzmann Machine ◽  
Deep Boltzmann Machine

scholarly journals The representation of colored objects in macaque color patches

2017 ◽  
Author(s):  
Le Chang ◽  
Pinglei Bao ◽  
Doris Y. Tsao
Keyword(s):  
Object Recognition ◽  
Color Vision ◽  
The Other ◽  
Color Patch ◽  
Shape Information ◽  
Object Shape ◽  
Shape Invariant ◽  
High Concentrations ◽  
The Brain

AbstractAn important question about color vision is: how does the brain represent the color of an object? The recent discovery of “color patches” in macaque inferotemporal (IT) cortex, the part of brain responsible for object recognition, makes this problem experimentally tractable. Here we record neurons in three color patches, middle color patch CLC (central lateral color patch), and two anterior color patches ALC (anterior lateral color patch) and AMC (anterior medial color patch), while presenting images of objects systematically varied in hue. We found that all three patches contain high concentrations of hue-selective cells, and the three patches use distinct computational strategies to represent colored objects: while all three patches multiplex hue and shape information, shape-invariant hue information is much stronger in anterior color patches ALC/AMC than CLC; furthermore, hue and object shape specifically for primate faces/bodies are over-represented in AMC but not in the other two patches.

Identifying Genetic related Neuroimaging Biomarkers of Alzheimer's Disease via Diagnosis-Guided Group Sparse Multitask Learning Method

2021 ◽  
Vol 16 ◽  
Author(s):  
Anshi Lin ◽  
Wei Kong ◽  
Shuaiqun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regression Model ◽  
Multivariate Regression ◽  
Prior Information ◽  
Diagnostic Information ◽  
Multitask Learning ◽  
Learning Method ◽  
Pathological Characteristics ◽  
Different Types

Background: Advances in brain imaging and high-throughput genotyping techniques have provided new methods for studying the effects of genetic variation on brain structure and function. Traditionally, a variety of prior information has been added into the multivariate regression method for single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) to improve the accuracy of prediction. In previous studies, brain regions of interest (ROIs) with different types of pathological characteristics (Alzheimer's Disease/Mild Cognitive Impairment/healthy control) can only be randomly dispersed in test cases, greatly limiting the prediction ability of the regression model and failing to obtain optimal global results. Objective: This study proposes a multivariate regression model informed by prior diagnostic information to overcome this limitation. Method: In the prediction model, we first consider traditional prior information and then design a new regularization form to integrate the diagnostic information of different sample ROIs into the model. Results: Experiments demonstrated that this method greatly improves the prediction accuracy of the model compared to other methods and selects a batch of promising pathogenic SNP loci. Conclusion: Taking into account that ROIs with different types of pathological characteristics can be employed as prior information, we propose a new method (Diagnosis-Guided Group Sparse Multitask Learning Method) that improves the ability to predict disease-related quantitative feature sites and select genetic feature factors, applying this model to research on the pathogenesis of Alzheimer's disease.

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