3D Object Categorization and Recognition based on Deep Belief Networks and Point Clouds

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

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Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

CNS & Neurological Disorders - Drug Targets ◽

10.2174/1871527316666170113101559 ◽

2017 ◽

Vol 16 (2) ◽

pp. 129-136 ◽

Cited By ~ 1

Author(s):

Tianming Zhan ◽

Yi Chen ◽

Xunning Hong ◽

Zhenyu Lu ◽

Yunjie Chen

Keyword(s):

Brain Tumor ◽

Tumor Segmentation ◽

Belief Networks ◽

Deep Belief Networks ◽

Brain Tumor Segmentation

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Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽

10.3390/electronics10101205 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1205

Author(s):

Zhiyu Wang ◽

Li Wang ◽

Bin Dai

Keyword(s):

Object Detection ◽

Point Clouds ◽

Autonomous Driving ◽

Feature Representation ◽

Alignment Algorithm ◽

3D Object ◽

3D Point Clouds ◽

Object Feature ◽

3D Object Detection ◽

Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

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