3D far-field Lidar sensing and computational modeling for human identification.

3D sensors offer depth sensing that may be used for task-specific data processing and computational modeling. Many existing methods for human identification using 3D depth sensors primarily focus on Kinect data, where the range is very limited. This work considers a 3D long-range Lidar sensor for far-field imaging of human subjects in 3D Lidar full motion video (FMV) of "walking" action. 3D Lidar FMV data for human subjects are used to develop computational modeling for automated human silhouette and skeleton extraction followed by subject identification. We propose a matrix completion algorithm to handle missing data in 3D FMV due to self-occlusion and occlusion from other subjects for 3D skeleton extraction. We further study the effect of noise in the 3D low resolution far-field Lidar data in human silhouette extraction performance of the model. Moreover, this work addresses challenges associated with far-field 3D Lidar including learning with a limited amount of data and low resolution. Moreover, we evaluate the proposed computational algorithm using a gallery of 10 subjects for human identification and show that our method is competitive with the state-of-the-art OpenPose and V2VPose skeleton extraction models using the same dataset for human identification.

Survey on Deep Neural Networks in Speech and Vision Systems.

This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in vision and speech applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent vision and speech systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent vision and speech systems to date. An overview of large-scale industrial research and development efforts is provided to emphasize future trends and prospects of intelligent vision and speech systems. Robust and efficient intelligent systems demand low-latency and high fidelity in resource-constrained hardware platforms such as mobile devices, robots, and automobiles. Therefore, this survey also provides a summary of key challenges and recent successes in running deep neural networks on hardware-restricted platforms, i.e. within limited memory, battery life, and processing capabilities. Finally, emerging applications of vision and speech across disciplines such as affective computing, intelligent transportation, and precision medicine are discussed. To our knowledge, this paper provides one of the most comprehensive surveys on the latest developments in intelligent vision and speech applications from the perspectives of both software and hardware systems. Many of these emerging technologies using deep neural networks show tremendous promise to revolutionize research and development for future vision and speech systems.

Novel deep generative simultaneous recurrent model for efficient representation learning.

Representation learning plays an important role for building effective deep neural network models. Deep generative probabilistic models have shown to be efficient in the data representation learning task which is usually carried out in an unsupervised fashion. Throughout the past decade, there has been almost exclusive focus on the learning algorithms to improve representation capability of the generative models. However, effective data representation requires improvement in both learning algorithm and architecture of the generative models. Therefore, improvement to the neural architecture is critical for improved data representation capability of deep generative models. Furthermore, the prevailing class of deep generative models such as deep belief network (DBN), deep Boltzman machine (DBM) and deep sigmoid belief network (DSBN) are inherently unidirectional and lack recurrent connections ubiquitous in the biological neuronal structures. Introduction of recurrent connections may offer further improvement in data representation learning performance to the deep generative models. Consequently, for the first time in literature, this work proposes a deep recurrent generative model known as deep simultaneous recurrent belief network (D-SRBN) to efficiently learn representations from unlabeled data. Experimentation on four benchmark datasets: MNIST, Caltech 101 Silhouettes, OCR letters and Omniglot show that the proposed D-SRBN model achieves superior representation learning performance while utilizing less computing resources when compared to the four state-of-the-art generative models such as deep belief network (DBN), DBM, DSBN and VAE (variational auto-encoder).

Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.