pwc-archive/papers-with-abstracts huggingface dataset:
Fristograms: Revealing and Exploiting Light Field Internals
Authors: Thorsten Herfet, Kelvin Chelli, Tobias Lange, Robin Kremer
Date: 2021-07-22 00:00:00
Tasks: 3D Reconstruction, Image Matting, Mixed Reality, object-detection, Object Detection, Saliency Detection

In recent years, light field (LF) capture and processing has become an integral part of media production. The richness of information available in LFs has enabled novel applications like post-capture depth-of-field editing, 3D reconstruction, segmentation and matting, saliency detection, object detection and recognition, and mixed reality. The efficacy of such applications depends on certain underlying requirements, which are often ignored. For example, some operations such as noise-reduction, or hyperfan-filtering are only possible if a scene point Lambertian radiator. Some other operations such as the removal of obstacles or looking behind objects are only possible if there is at least one ray capturing the required scene point. Consequently, the ray distribution representing a certain scene point is an important characteristic for evaluating processing possibilities. The primary idea in this paper is to establish a relation between the capturing setup and the rays of the LF. To this end, we discretize the view frustum. Traditionally, a uniform discretization of the view frustum results in voxels that represents a single sample on a regularly spaced, 3-D grid. Instead, we use frustum-shaped voxels (froxels), by using depth and capturing-setup dependent discretization of the view frustum. Based on such discretization, we count the number of rays mapping to the same pixel on the capturing device(s). By means of this count, we propose histograms of ray-counts over the froxels (fristograms). Fristograms can be used as a tool to analyze and reveal interesting aspects of the underlying LF, like the number of rays originating from a scene point and the color distribution of these rays. As an example, we show its ability by significantly reducing the number of rays which enables noise reduction while maintaining the realistic rendering of non-Lambertian or partially occluded regions.
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI
Authors: Alvaro Fernandez-Quilez, Trygve Eftestøl, Morten Goodwin, Svein Reidar Kjosavik, Ketil Oppedal
Date: 2021-07-22 00:00:00
Tasks: Diagnostic, Transfer Learning

Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to unnecessary treatment and further testing. Bi-parametric magnetic resonance imaging (bp-MRI) based on apparent diffusion coefficient maps (ADC) and T2-weighted (T2w) sequences has been proposed as a triage test to differentiate between clinically significant (cS) and non-clinically significant (ncS) prostate lesions. However, analysis of the sequences relies on expertise, requires specialized training, and suffers from inter-observer variability. Deep learning (DL) techniques hold promise in tasks such as classification and detection. Nevertheless, they rely on large amounts of annotated data which is not common in the medical field. In order to palliate such issues, existing works rely on transfer learning (TL) and ImageNet pre-training, which has been proven to be sub-optimal for the medical imaging domain. In this paper, we present a patch-based pre-training strategy to distinguish between cS and ncS lesions which exploit the region of interest (ROI) of the patched source domain to efficiently train a classifier in the full-slice target domain which does not require annotations by making use of transfer learning (TL). We provide a comprehensive comparison between several CNNs architectures and different settings which are presented as a baseline. Moreover, we explore cross-domain TL which exploits both MRI modalities and improves single modality results. Finally, we show how our approaches outperform the standard approaches by a considerable margin
Route-VPlat: Survey and Analysis of Routing Protocols for Communication in Multi-hop Vehicular Platooning
Authors: N. Jaswanth, Shuddhank Pushola, Aditya Mehra, Pruthvi Raj, N. Rajasree, Hrishikesh Venkataraman
Date: 2021-04-13 00:00:00
Proceeding: - 2021 4
Tasks: Clustering

Over the years, vehicular communication has become a significant and emerging research area. However, the vehicular platooning concept has gained importance only in the last couple of years or so. Typically, a platoon is a group of vehicles governed by the front vehicle, where all other vehicles follow the instruction of the lead vehicle. The vehicles in a platoon have the flexibility to leave the connected path when their destination has arrived. However, maintaining a continuous communication mechanism is a major challenge that still needs to be solved. In a high-speed dynamic setting of platooning, vehicles need not only to exactly follow the pattern of movement of the lead vehicle but also have seamless communication between the vehicles, even while entering and exiting the platoon. Particularly, multi-hop communication is a critical component for vehicular platooning wherein the lead vehicle could communicate with all vehicles in the platoon. Multi-hop communication increases the coverage range but also increases the overall complexity. Hence it is imperative to investigate and analyze different existing routing protocols, their functionality in multi-hop scenarios, and their feasibility for a multi-hop vehicular platooning environment. This article presents Route-VPlat—a systematic review and analysis of the various routing protocols that can be used for continuous communication in multi-hop vehicular platooning. Importantly, the article analyzes routing protocols from different categories such as Ad Hoc On-Demand Distance Vector Routing Protocol (AODV), Optimized Link State Routing (OLSR) protocol, Greedy Perimeter Stateless Routing (GPSR), Path-Aware GPSR (PA-GPSR), Edge-Aware Epidemic Protocol (EAEP), and clustering-based routing mechanism—Low-Energy Adaptive Clustering Hierarchy (LEACH)—and analyzes their performance for platooning. The OLSR protocol performs better as compared to others in terms of latency, throughput, and delivery ratio metrics. Finally, a brief summary of different open research challenges is provided.
Perceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning
Authors: Nidamanuri Jaswanth, Anjali Poornima Karri, Hrishikesh Venkataraman
Date: 2020-09-30 00:00:00
Proceeding: Conference 2020 9
Tasks: Autonomous Driving, Deep Learning, vehicle detection

In recent times, advanced driver assistance system (ADAS) and autonomous driving have received significant interest in the automotive community. In this regard, lane assistance system and vehicle detection are considered as core modules of ADAS. However, the drawback is that the conventional image processing and vision-based techniques are quite slow and computationally expensive. The proposed work circumvents this limitation with the practical use of deep learning (CNN) based detection architecture. This paper proposes the use of CNN inspired detection methods such as Faster RCNN and YOLO for visual perception of road traffic. Notably, they were applied for effective vehicle detection in non-disciplined, heterogeneous Indian road traffic. This involved collecting own dataset for Indian urban heterogeneous traffic in both day and night time. An application of YOLO with VGG network resulted in a mAP score of 78.57%. On the other hand, Faster RCNN with Inception v2 and ResNet networks resulted in mAP score of 88% and 89.44%, on Indian road traffic datasets. This is a significant result; that shows the use of Deep Learning techniques for an efficient visual modelling of unconstrained road traffic scenarios.
Lane Change For System-Driven Vehicles Using Dynamic Information
Authors: Nidamanuri Jaswanth, Hrishikesh Venkataraman
Date: 2020-10-01 00:00:00
Proceeding: Conference 2018 10
Tasks: Autonomous Driving, vehicle detection

In recent times, advanced driver assistance system (ADAS) and autonomous driving have received significant interest in the automotive community. In this regard, lane assistance system and vehicle detection are considered as core modules of ADAS. However, the drawback is that the conventional image processing and vision-based techniques are quite slow and computationally expensive. The proposed work circumvents this limitation with the practical use of deep learning (CNN) based detection architecture. This paper proposes the use of CNN inspired detection methods such as Faster RCNN and YOLO for visual perception of road traffic. Notably, they were applied for effective vehicle detection in non-disciplined, heterogeneous Indian road traffic. This involved collecting own dataset for Indian urban heterogeneous traffic in both day and night time. An application of YOLO with VGG network resulted in a mAP score of 78.57%. On the other hand, Faster RCNN with Inception v2 and ResNet networks resulted in mAP score of 88% and 89.44%, on Indian road traffic datasets. This is a significant result; that shows the use of Deep Learning techniques for an efficient visual modelling of unconstrained road traffic scenarios.
Route-VPlat: Survey and Analysis of Routing Protocols for Communication in Multi-hop Vehicular Platooning
Authors: Jaswanth, N et al
Date: 2021-04-13 00:00:00
Proceeding: journal 2021 4
Tasks: Clustering

Over the years, vehicular communication has become a significant and emerging research area. However, the vehicular platooning concept has gained importance only in the last couple of years or so. Typically, a platoon is a group of vehicles governed by the front vehicle, where all other vehicles follow the instruction of the lead vehicle. The vehicles in a platoon have the flexibility to leave the connected path when their destination has arrived. However, maintaining a continuous communication mechanism is a major challenge that still needs to be solved. In a high-speed dynamic setting of platooning, vehicles need not only to exactly follow the pattern of movement of the lead vehicle but also have seamless communication between the vehicles, even while entering and exiting the platoon. Particularly, multi-hop communication is a critical component for vehicular platooning wherein the lead vehicle could communicate with all vehicles in the platoon. Multi-hop communication increases the coverage range but also increases the overall complexity. Hence it is imperative to investigate and analyze different existing routing protocols, their functionality in multi-hop scenarios, and their feasibility for a multi-hop vehicular platooning environment. This article presents Route-VPlat—a systematic review and analysis of the various routing protocols that can be used for continuous communication in multi-hop vehicular platooning. Importantly, the article analyzes routing protocols from different categories such as Ad Hoc On-Demand Distance Vector Routing Protocol (AODV), Optimized Link State Routing (OLSR) protocol, Greedy Perimeter Stateless Routing (GPSR), Path-Aware GPSR (PA-GPSR), Edge-Aware Epidemic Protocol (EAEP), and clustering-based routing mechanism—Low-Energy Adaptive Clustering Hierarchy (LEACH)—and analyzes their performance for platooning. The OLSR protocol performs better as compared to others in terms of latency, throughput, and delivery ratio metrics. Finally, a brief summary of different open research challenges is provided.
Lane Change For System-Driven Vehicles Using Dynamic Information
Authors: Jaswanth N, Hrishikesh Venkataraman
Date: 2018-10-01 00:00:00

Over the years, the advanced driver assistance system (ADAS) has evolved into a mature system-controlled driving. This includes automatic parking, obstacle detection, etc. One of the important aspects that still need to successfully investigate is the automatic lane change across different traffic scenarios and the different nature of roads. While it is still easier to identify the road, the rapid change in the traffic makes it difficult to safely carry out automatic lane change in real-time. This work proposes a mechanism by first modeling the behavior of vehicles in a dynamic environment by generating fusion data from various externally mounted sensors, RADAR and cameras and then develop a visual perception of the same using multi-object tracker and tracking Kalman filter.
Resource Allocation Complexity Analysis for Relay-based Multihop V2V Communication
Authors: Jaswanth Nidamanuri, Chaitanya Nagpal, Hrishikesh Venkataraman
Date: 2020-10-13 00:00:00
Proceeding: Conference 2020 10
Tasks: Autonomous Driving

Over last 4-5 years, there has been tremendous development globally towards autonomous driving. However, in order to understand the neighborhood road environment, solutions using Camera, RADAR and LIDAR have significant disadvantages. The primary drawback being that the vehicles should be in visible/direct line-of-sight of each other. In this regard, vehicle-to-vehicle communication (V2V) has a great advantage that it enables transmission of information from one vehicle to another even if they are not in the line-of-sight range. The expected data rate in next generation connected vehicles for autonomous driving is expected to be in the range of 100 Mbps or higher. In this regard, a relay-based multi-hop V2V mechanism has been explored for communication between far-located source and destination vehicle. However, the number of multiple hops in V2V would affect the resource allocation in 5G/next generation network. This paper investigates the number of multiple hops required as the maximum transmission range is varied. Importantly, the paper investigates the complexity in the resource allocation as the number of multiple hops among source and destination vehicle is increased. The number of multiple hops required increases exponentially, when the communication range is around 0.3 times the maximum transmission range. Further, there is a humongous increase in the complexity of resource allocation when the number of multiple hops increase. Especially with spatial reuse in multi-hop mode, the complexity of resource allocation increases to 10 80 for a 50-node vehicular network. This is an important aspect to be considered while designing next generation connected vehicle environment.
Perceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning
Authors: Nidamanuri Jaswanth, Anjali Poornima Karri, Hrishikesh Venkataraman
Date: 2020-09-30 00:00:00
Proceeding: Conference 2020 9
Tasks: Autonomous Driving, Deep Learning

Over last 4-5 years, there has been tremendous development globally towards autonomous driving. However, in order to understand the neighborhood road environment, solutions using Camera, RADAR and LIDAR have significant disadvantages. The primary drawback being that the vehicles should be in visible/direct line-of-sight of each other. In this regard, vehicle-to-vehicle communication (V2V) has a great advantage that it enables transmission of information from one vehicle to another even if they are not in the line-of-sight range. The expected data rate in next generation connected vehicles for autonomous driving is expected to be in the range of 100 Mbps or higher. In this regard, a relay-based multi-hop V2V mechanism has been explored for communication between far-located source and destination vehicle. However, the number of multiple hops in V2V would affect the resource allocation in 5G/next generation network. This paper investigates the number of multiple hops required as the maximum transmission range is varied. Importantly, the paper investigates the complexity in the resource allocation as the number of multiple hops among source and destination vehicle is increased. The number of multiple hops required increases exponentially, when the communication range is around 0.3 times the maximum transmission range. Further, there is a humongous increase in the complexity of resource allocation when the number of multiple hops increase. Especially with spatial reuse in multi-hop mode, the complexity of resource allocation increases to 10 80 for a 50-node vehicular network. This is an important aspect to be considered while designing next generation connected vehicle environment.
Perceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning
Authors: Nidamanuri Jaswanth, Anjali Poornima Karri, Hrishikesh Venkataraman
Date: 2020-09-30 00:00:00
Tasks: Autonomous Driving, Deep Learning, vehicle detection

In recent times, advanced driver assistance system (ADAS) and autonomous driving have received significant interest in the automotive community. In this regard, lane assistance system and vehicle detection are considered as core modules of ADAS. However, the drawback is that the conventional image processing and vision-based techniques are quite slow and computationally expensive. The proposed work circumvents this limitation with the practical use of deep learning (CNN) based detection architecture. This paper proposes the use of CNN inspired detection methods such as Faster RCNN and YOLO for visual perception of road traffic. Notably, they were applied for effective vehicle detection in non-disciplined, heterogeneous Indian road traffic. This involved collecting own dataset for Indian urban heterogeneous traffic in both day and night time. An application of YOLO with VGG network resulted in a mAP score of 78.57%. On the other hand, Faster RCNN with Inception v2 and ResNet networks resulted in mAP score of 88% and 89.44%, on Indian road traffic datasets. This is a significant result; that shows the use of Deep Learning techniques for an efficient visual modelling of unconstrained road traffic scenarios.
Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations
Authors: Fanwei Kong, Shawn C. Shadden
Date: 2021-07-22 00:00:00

Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases. This paradigm requires constructing simulation-ready meshes of cardiac structures from medical image data--a process that has traditionally required significant time and human effort, limiting large-cohort analyses and potential clinical translations. We propose a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data. Our approach learns to deform a template mesh to the input image data by predicting displacements of multi-resolution control point grids. We discuss the methods of this approach and demonstrate its application to efficiently create simulation-ready whole heart meshes for computational fluid dynamics simulations of the cardiac flow. Our source code is available at https://github.com/fkong7/HeartFFDNet.
Specifying a Game-Theoretic Extensive Form as an Abstract 5-ary Relation
Authors: Peter A. Streufert
Date: 2021-07-22 00:00:00
Tasks: Form, Relation

This paper specifies an extensive form as a 5-ary relation (that is, as a set of quintuples) which satisfies eight abstract axioms. Each quintuple is understood to list a player, a situation (that is, a name for an information set), a decision node, an action, and a successor node. Accordingly, the axioms are understood to specify abstract relationships between players, situations, nodes, and actions. Such an extensive form is called a "pentaform". Finally, a "pentaform game" is defined to be a pentaform together with utility functions. To ground this new specification in the literature, the paper defines the concept of a "traditional game" to represent the literature's many specifications of finite-horizon and infinite-horizon games. The paper's main result is to construct an intuitive bijection between pentaform games and traditional games. Secondary results concern disaggregating pentaforms by subsets, constructing pentaforms by unions, and initial pentaform applications to Selten subgames and perfect-recall (an extensive application to dynamic programming is in Streufert 2023, arXiv:2302.03855).
CarneliNet: Neural Mixture Model for Automatic Speech Recognition
Authors: Aleksei Kalinov, Somshubra Majumdar, Jagadeesh Balam, Boris Ginsburg
Date: 2021-07-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

End-to-end automatic speech recognition systems have achieved great accuracy by using deeper and deeper models. However, the increased depth comes with a larger receptive field that can negatively impact model performance in streaming scenarios. We propose an alternative approach that we call Neural Mixture Model. The basic idea is to introduce a parallel mixture of shallow networks instead of a very deep network. To validate this idea we design CarneliNet -- a CTC-based neural network composed of three mega-blocks. Each mega-block consists of multiple parallel shallow sub-networks based on 1D depthwise-separable convolutions. We evaluate the model on LibriSpeech, MLS and AISHELL-2 datasets and achieved close to state-of-the-art results for CTC-based models. Finally, we demonstrate that one can dynamically reconfigure the number of parallel sub-networks to accommodate the computational requirements without retraining.
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech
Authors: Duo Ma, Nana Hou, Van Tung Pham, HaiHua Xu, Eng Siong Chng
Date: 2021-07-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Speech Enhancement, speech-recognition, Speech Recognition

To realize robust end-to-end Automatic Speech Recognition(E2E ASR) under radio communication condition, we propose a multitask-based method to joint train a Speech Enhancement (SE) module as the front-end and an E2E ASR model as the back-end in this paper. One of the advantage of the proposed method is that the entire system can be trained from scratch. Different from prior works, either component here doesn't need to perform pre-training and fine-tuning processes separately. Through analysis, we found that the success of the proposed method lies in the following aspects. Firstly, multitask learning is essential, that is the SE network is not only learning to produce more Intelligent speech, it is also aimed to generate speech that is beneficial to recognition. Secondly, we also found speech phase preserved from noisy speech is critical for improving ASR performance. Thirdly, we propose a dual channel data augmentation training method to obtain further improvement.Specifically, we combine the clean and enhanced speech to train the whole system. We evaluate the proposed method on the RATS English data set, achieving a relative WER reduction of 4.6% with the joint training method, and up to a relative WER reduction of 11.2% with the proposed data augmentation method.
Controlling the Perceived Sound Quality for Dialogue Enhancement with Deep Learning
Authors: Christian Uhle, Matteo Torcoli, Jouni Paulus
Date: 2021-07-22 00:00:00
Tasks: Speech Enhancement

Speech enhancement attenuates interfering sounds in speech signals but may introduce artifacts that perceivably deteriorate the output signal. We propose a method for controlling the trade-off between the attenuation of the interfering background signal and the loss of sound quality. A deep neural network estimates the attenuation of the separated background signal such that the sound quality, quantified using the Artifact-related Perceptual Score, meets an adjustable target. Subjective evaluations indicate that consistent sound quality is obtained across various input signals. Our experiments show that the proposed method is able to control the trade-off with an accuracy that is adequate for real-world dialogue enhancement applications.