Matching Items (10)
Filtering by
- All Subjects: Computer vision
- Creators: Liang, Jianming
- Creators: Seo, Jae-Sun
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Computer vision is becoming an essential component of embedded system applications such as smartphones, wearables, autonomous systems and internet-of-things (IoT). These applications are generally deployed into environments with limited energy, memory bandwidth and computational resources. This trend is driving the development of energy-effi cient image processing solutions from sensing to computation. In this thesis, diff erent alternatives are explored to implement energy-efficient computer vision systems. First, I present a fi eld programmable gate array (FPGA) implementation of an adaptive subsampling algorithm for region-of-interest (ROI) -based object tracking. By implementing the computationally intensive sections of this algorithm on an FPGA, I aim to offl oad computing resources from energy-ineffi cient graphics processing units (GPUs) and/or general-purpose central processing units (CPUs). I also present a working system executing this algorithm in near real-time latency implemented on a standalone embedded device. Secondly, I present a neural network-based pipeline to improve the performance of event-based cameras in non-ideal optical conditions. Event-based cameras or dynamic vision sensors (DVS) are bio-inspired sensors that measure logarithmic per-pixel brightness changes in a scene. Their advantages include high dynamic range, low latency and ultra-low power when compared to standard frame-based cameras. Several tasks have been proposed to take advantage of these novel sensors but they rely on perfectly calibrated optical lenses that are in-focus. In this work I propose a methodto reconstruct events captured with an out-of-focus event-camera so they can be fed into an intensity reconstruction task. The network is trained with a dataset generated by simulating defocus blur in sequences from object tracking datasets such as LaSOT and OTB100. I also test the generalization performance of this network in scenes captured with a DAVIS event-based sensor equipped with an out-of-focus lens.
ContributorsTorres Muro, Victor Isaac (Author) / Jayasuriya, Suren (Thesis advisor) / Spanias, Andreas (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description
Machine learning techniques have found extensive application in dynamic fields like drones, self-driving vehicles, surveillance, and more. Their effectiveness stems from meticulously crafted deep neural networks (DNNs), extensive data gathering efforts, and resource-intensive model training processes. However, due to the unpredictable nature of the environment, these systems will inevitably encounter input samples that deviate from the distribution of their original training data, resulting in instability and performance degradation.To effectively detect the emergence of out-of-distribution (OOD) data, this dissertation first proposes a novel, self-supervised approach that evaluates the Mahalanobis distance between the in-distribution (ID) and OOD in gradient space. A binary classifier is then introduced to guide the label selection for gradients calculation, which further boosts the detection performance.
Next, to continuously adapt the new OOD into the existing knowledge base, an unified framework for novelty detection and continual learning is proposed. The binary classifier, trained to distinguish OOD data from ID, is connected sequentially with the pre-trained model to form a “N + 1” classifier, where “N” represents prior knowledge which contains N classes and “1” refers to the newly arrival OOD. This continual learning process continues as “N+1+1+1+...”, assimilating the knowledge of each new OOD instance into the system.
Finally, this dissertation demonstrates the practical implementation of novelty detection and continual learning within the domain of thermal analysis. To rapidly address the impact of voids in thermal interface material (TIM), a continuous adaptation approach is proposed, which integrates trainable nodes into the graph at the locations where abnormal thermal behaviors are detected. With minimal training overhead, the model can quickly adapts to the change caused by the defects and regenerate accurate thermal prediction.
In summary, this dissertation proposes several algorithms and practical applications in continual learning aimed at enhancing the stability and adaptability of the system. All proposed algorithms are validated through extensive experiments conducted on benchmark datasets such as CIFAR-10, CIFAR-100, TinyImageNet for continual learning, and real thermal data for thermal analysis.
ContributorsSun, Jingbo (Author) / Cao, Yu (Thesis advisor) / Chhabria, Vidya (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Fan, Deliang (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2024
Description
The rapid improvement in computation capability has made deep convolutional neural networks (CNNs) a great success in recent years on many computer vision tasks with significantly improved accuracy. During the inference phase, many applications demand low latency processing of one image with strict power consumption requirement, which reduces the efficiency of GPU and other general-purpose platform, bringing opportunities for specific acceleration hardware, e.g. FPGA, by customizing the digital circuit specific for the deep learning algorithm inference. However, deploying CNNs on portable and embedded systems is still challenging due to large data volume, intensive computation, varying algorithm structures, and frequent memory accesses. This dissertation proposes a complete design methodology and framework to accelerate the inference process of various CNN algorithms on FPGA hardware with high performance, efficiency and flexibility.
As convolution contributes most operations in CNNs, the convolution acceleration scheme significantly affects the efficiency and performance of a hardware CNN accelerator. Convolution involves multiply and accumulate (MAC) operations with four levels of loops. Without fully studying the convolution loop optimization before the hardware design phase, the resulting accelerator can hardly exploit the data reuse and manage data movement efficiently. This work overcomes these barriers by quantitatively analyzing and optimizing the design objectives (e.g. memory access) of the CNN accelerator based on multiple design variables. An efficient dataflow and hardware architecture of CNN acceleration are proposed to minimize the data communication while maximizing the resource utilization to achieve high performance.
Although great performance and efficiency can be achieved by customizing the FPGA hardware for each CNN model, significant efforts and expertise are required leading to long development time, which makes it difficult to catch up with the rapid development of CNN algorithms. In this work, we present an RTL-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of various CNNs, in order to enable high-level fast prototyping of CNNs from software to FPGA and still keep the benefits of low-level hardware optimization. First, a general-purpose library of RTL modules is developed to model different operations at each layer. The integration and dataflow of physical modules are predefined in the top-level system template and reconfigured during compilation for a given CNN algorithm. The runtime control of layer-by-layer sequential computation is managed by the proposed execution schedule so that even highly irregular and complex network topology, e.g. GoogLeNet and ResNet, can be compiled. The proposed methodology is demonstrated with various CNN algorithms, e.g. NiN, VGG, GoogLeNet and ResNet, on two different standalone FPGAs achieving state-of-the art performance.
Based on the optimized acceleration strategy, there are still a lot of design options, e.g. the degree and dimension of computation parallelism, the size of on-chip buffers, and the external memory bandwidth, which impact the utilization of computation resources and data communication efficiency, and finally affect the performance and energy consumption of the accelerator. The large design space of the accelerator makes it impractical to explore the optimal design choice during the real implementation phase. Therefore, a performance model is proposed in this work to quantitatively estimate the accelerator performance and resource utilization. By this means, the performance bottleneck and design bound can be identified and the optimal design option can be explored early in the design phase.
As convolution contributes most operations in CNNs, the convolution acceleration scheme significantly affects the efficiency and performance of a hardware CNN accelerator. Convolution involves multiply and accumulate (MAC) operations with four levels of loops. Without fully studying the convolution loop optimization before the hardware design phase, the resulting accelerator can hardly exploit the data reuse and manage data movement efficiently. This work overcomes these barriers by quantitatively analyzing and optimizing the design objectives (e.g. memory access) of the CNN accelerator based on multiple design variables. An efficient dataflow and hardware architecture of CNN acceleration are proposed to minimize the data communication while maximizing the resource utilization to achieve high performance.
Although great performance and efficiency can be achieved by customizing the FPGA hardware for each CNN model, significant efforts and expertise are required leading to long development time, which makes it difficult to catch up with the rapid development of CNN algorithms. In this work, we present an RTL-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of various CNNs, in order to enable high-level fast prototyping of CNNs from software to FPGA and still keep the benefits of low-level hardware optimization. First, a general-purpose library of RTL modules is developed to model different operations at each layer. The integration and dataflow of physical modules are predefined in the top-level system template and reconfigured during compilation for a given CNN algorithm. The runtime control of layer-by-layer sequential computation is managed by the proposed execution schedule so that even highly irregular and complex network topology, e.g. GoogLeNet and ResNet, can be compiled. The proposed methodology is demonstrated with various CNN algorithms, e.g. NiN, VGG, GoogLeNet and ResNet, on two different standalone FPGAs achieving state-of-the art performance.
Based on the optimized acceleration strategy, there are still a lot of design options, e.g. the degree and dimension of computation parallelism, the size of on-chip buffers, and the external memory bandwidth, which impact the utilization of computation resources and data communication efficiency, and finally affect the performance and energy consumption of the accelerator. The large design space of the accelerator makes it impractical to explore the optimal design choice during the real implementation phase. Therefore, a performance model is proposed in this work to quantitatively estimate the accelerator performance and resource utilization. By this means, the performance bottleneck and design bound can be identified and the optimal design option can be explored early in the design phase.
ContributorsMa, Yufei (Author) / Vrudhula, Sarma (Thesis advisor) / Seo, Jae-Sun (Thesis advisor) / Cao, Yu (Committee member) / Barnaby, Hugh (Committee member) / Arizona State University (Publisher)
Created2018
Description
The ubiquity of single camera systems in society has made improving monocular depth estimation a topic of increasing interest in the broader computer vision community. Inspired by recent work in sparse-to-dense depth estimation, this thesis focuses on sparse patterns generated from feature detection based algorithms as opposed to regular grid sparse patterns used by previous work. This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other. These interpolated sparse depths are used to enforce additional constraints on the network’s predictions. In addition to the improved depth prediction performance observed from incorporating the sparse sample information in the network compared to pure RGB-based methods, the experiments show that actively retraining a network on a small number of samples that deviate most from the interpolated sparse depths leads to better depth prediction overall.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
ContributorsRai, Anshul (Author) / Yang, Yezhou (Thesis advisor) / Zhang, Wenlong (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2019
Description
Photovoltaic (PV) systems are affected by converter losses, partial shading and other mismatches in the panels. This dissertation introduces a sub-panel maximum power point tracking (MPPT) architecture together with an integrated CMOS current sensor circuit on a chip to reduce the mismatch effects, losses and increase the efficiency of the PV system. The sub-panel MPPT increases the efficiency of the PV during the shading and replaces the bypass diodes in the panels with an integrated MPPT and DC-DC regulator. For the integrated MPPT and regulator, the research developed an integrated standard CMOS low power and high common mode range Current-to-Digital Converter (IDC) circuit and its application for DC-DC regulator and MPPT. The proposed charge based CMOS switched-capacitor circuit directly digitizes the output current of the DC-DC regulator without an analog-to-digital converter (ADC) and the need for high-voltage process technology. Compared to the resistor based current-sensing methods that requires current-to-voltage circuit, gain block and ADC, the proposed CMOS IDC is a low-power efficient integrated circuit that achieves high resolution, lower complexity, and lower power consumption. The IDC circuit is fabricated on a 0.7 um CMOS process, occupies 2mm x 2mm and consumes less than 27mW. The IDC circuit has been tested and used for boost DC-DC regulator and MPPT for photo-voltaic system. The DC-DC converter has an efficiency of 95%. The sub-module level power optimization improves the output power of a shaded panel by up to 20%, compared to panel MPPT with bypass diodes.
ContributorsMarti-Arbona, Edgar (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kitchen, Jennifer (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Rapid development of computer vision applications such as image recognition and object detection has been enabled by the emerging deep learning technologies. To improve the accuracy further, deeper and wider neural networks with diverse architecture are proposed for better feature extraction. Though the performance boost is impressive, only marginal improvement can be achieved with significantly increased computational overhead. One solution is to compress the exploding-sized model by dropping less important weights or channels. This is an effective solution that has been well explored. However, by utilizing the rich relation information of the data, one can also improve the accuracy with reasonable overhead. This work makes progress toward efficient and accurate visual tasks including detection, prediction and understanding by using relations.
For object detection, a novel approach, Graph Assisted Reasoning (GAR), is proposed to utilize a heterogeneous graph to model object-object relations and object-scene relations. GAR fuses the features from neighboring object nodes as well as scene nodes. In this way, GAR produces better recognition than that produced from individual object nodes. Moreover, compared to previous approaches using Recurrent Neural Network (RNN), GAR's light-weight and low-coupling architecture further facilitate its integration into the object detection module.
For trajectories prediction, a novel approach, namely Diverse Attention RNN (DAT-RNN), is proposed to handle the diversity of trajectories and modeling of neighboring relations. DAT-RNN integrates both temporal and spatial relations to improve the prediction under various circumstances.
Last but not least, this work presents a novel relation implication-enhanced (RIE) approach that improves relation detection through relation direction and implication. With the relation implication, the SGG model is exposed to more ground truth information and thus mitigates the overfitting problem of the biased datasets. Moreover, the enhancement with relation implication is compatible with various context encoding schemes.
Comprehensive experiments on benchmarking datasets demonstrate the efficacy of the proposed approaches.
For object detection, a novel approach, Graph Assisted Reasoning (GAR), is proposed to utilize a heterogeneous graph to model object-object relations and object-scene relations. GAR fuses the features from neighboring object nodes as well as scene nodes. In this way, GAR produces better recognition than that produced from individual object nodes. Moreover, compared to previous approaches using Recurrent Neural Network (RNN), GAR's light-weight and low-coupling architecture further facilitate its integration into the object detection module.
For trajectories prediction, a novel approach, namely Diverse Attention RNN (DAT-RNN), is proposed to handle the diversity of trajectories and modeling of neighboring relations. DAT-RNN integrates both temporal and spatial relations to improve the prediction under various circumstances.
Last but not least, this work presents a novel relation implication-enhanced (RIE) approach that improves relation detection through relation direction and implication. With the relation implication, the SGG model is exposed to more ground truth information and thus mitigates the overfitting problem of the biased datasets. Moreover, the enhancement with relation implication is compatible with various context encoding schemes.
Comprehensive experiments on benchmarking datasets demonstrate the efficacy of the proposed approaches.
ContributorsLi, Zheng (Author) / Cao, Yu (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Seo, Jae-Sun (Committee member) / Fan, Deliang (Committee member) / Arizona State University (Publisher)
Created2020
Description
This work solves the problem of incorrect rotations while using handheld devices.Two new methods which improve upon previous works are explored. The first method
uses an infrared camera to capture and detect the user’s face position and orient the
display accordingly. The second method utilizes gyroscopic and accelerometer data
as input to a machine learning model to classify correct and incorrect rotations.
Experiments show that these new methods achieve an overall success rate of 67%
for the first and 92% for the second which reaches a new high for this performance
category. The paper also discusses logistical and legal reasons for implementing this
feature into an end-user product from a business perspective. Lastly, the monetary
incentive behind a feature like irRotate in a consumer device and explore related
patents is discussed.
uses an infrared camera to capture and detect the user’s face position and orient the
display accordingly. The second method utilizes gyroscopic and accelerometer data
as input to a machine learning model to classify correct and incorrect rotations.
Experiments show that these new methods achieve an overall success rate of 67%
for the first and 92% for the second which reaches a new high for this performance
category. The paper also discusses logistical and legal reasons for implementing this
feature into an end-user product from a business perspective. Lastly, the monetary
incentive behind a feature like irRotate in a consumer device and explore related
patents is discussed.
ContributorsTallman, Riley (Author) / Yang, Yezhou (Thesis advisor) / Liang, Jianming (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2020
Description
Recently, a well-designed and well-trained neural network can yield state-of-the-art results across many domains, including data mining, computer vision, and medical image analysis. But progress has been limited for tasks where labels are difficult or impossible to obtain. This reliance on exhaustive labeling is a critical limitation in the rapid deployment of neural networks. Besides, the current research scales poorly to a large number of unseen concepts and is passively spoon-fed with data and supervision.
To overcome the above data scarcity and generalization issues, in my dissertation, I first propose two unsupervised conventional machine learning algorithms, hyperbolic stochastic coding, and multi-resemble multi-target low-rank coding, to solve the incomplete data and missing label problem. I further introduce a deep multi-domain adaptation network to leverage the power of deep learning by transferring the rich knowledge from a large-amount labeled source dataset. I also invent a novel time-sequence dynamically hierarchical network that adaptively simplifies the network to cope with the scarce data.
To learn a large number of unseen concepts, lifelong machine learning enjoys many advantages, including abstracting knowledge from prior learning and using the experience to help future learning, regardless of how much data is currently available. Incorporating this capability and making it versatile, I propose deep multi-task weight consolidation to accumulate knowledge continuously and significantly reduce data requirements in a variety of domains. Inspired by the recent breakthroughs in automatically learning suitable neural network architectures (AutoML), I develop a nonexpansive AutoML framework to train an online model without the abundance of labeled data. This work automatically expands the network to increase model capability when necessary, then compresses the model to maintain the model efficiency.
In my current ongoing work, I propose an alternative method of supervised learning that does not require direct labels. This could utilize various supervision from an image/object as a target value for supervising the target tasks without labels, and it turns out to be surprisingly effective. The proposed method only requires few-shot labeled data to train, and can self-supervised learn the information it needs and generalize to datasets not seen during training.
To overcome the above data scarcity and generalization issues, in my dissertation, I first propose two unsupervised conventional machine learning algorithms, hyperbolic stochastic coding, and multi-resemble multi-target low-rank coding, to solve the incomplete data and missing label problem. I further introduce a deep multi-domain adaptation network to leverage the power of deep learning by transferring the rich knowledge from a large-amount labeled source dataset. I also invent a novel time-sequence dynamically hierarchical network that adaptively simplifies the network to cope with the scarce data.
To learn a large number of unseen concepts, lifelong machine learning enjoys many advantages, including abstracting knowledge from prior learning and using the experience to help future learning, regardless of how much data is currently available. Incorporating this capability and making it versatile, I propose deep multi-task weight consolidation to accumulate knowledge continuously and significantly reduce data requirements in a variety of domains. Inspired by the recent breakthroughs in automatically learning suitable neural network architectures (AutoML), I develop a nonexpansive AutoML framework to train an online model without the abundance of labeled data. This work automatically expands the network to increase model capability when necessary, then compresses the model to maintain the model efficiency.
In my current ongoing work, I propose an alternative method of supervised learning that does not require direct labels. This could utilize various supervision from an image/object as a target value for supervising the target tasks without labels, and it turns out to be surprisingly effective. The proposed method only requires few-shot labeled data to train, and can self-supervised learn the information it needs and generalize to datasets not seen during training.
ContributorsZhang, Jie (Author) / Wang, Yalin (Thesis advisor) / Liu, Huan (Committee member) / Stonnington, Cynthia (Committee member) / Liang, Jianming (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
In recent years, Convolutional Neural Networks (CNNs) have been widely used in not only the computer vision community but also within the medical imaging community. Specifically, the use of pre-trained CNNs on large-scale datasets (e.g., ImageNet) via transfer learning for a variety of medical imaging applications, has become the de facto standard within both communities.
However, to fit the current paradigm, 3D imaging tasks have to be reformulated and solved in 2D, losing rich 3D contextual information. Moreover, pre-trained models on natural images never see any biomedical images and do not have knowledge about anatomical structures present in medical images. To overcome the above limitations, this thesis proposes an image out-painting self-supervised proxy task to develop pre-trained models directly from medical images without utilizing systematic annotations. The idea is to randomly mask an image and train the model to predict the missing region. It is demonstrated that by predicting missing anatomical structures when seeing only parts of the image, the model will learn generic representation yielding better performance on various medical imaging applications via transfer learning.
The extensive experiments demonstrate that the proposed proxy task outperforms training from scratch in six out of seven medical imaging applications covering 2D and 3D classification and segmentation. Moreover, image out-painting proxy task offers competitive performance to state-of-the-art models pre-trained on ImageNet and other self-supervised baselines such as in-painting. Owing to its outstanding performance, out-painting is utilized as one of the self-supervised proxy tasks to provide generic 3D pre-trained models for medical image analysis.
However, to fit the current paradigm, 3D imaging tasks have to be reformulated and solved in 2D, losing rich 3D contextual information. Moreover, pre-trained models on natural images never see any biomedical images and do not have knowledge about anatomical structures present in medical images. To overcome the above limitations, this thesis proposes an image out-painting self-supervised proxy task to develop pre-trained models directly from medical images without utilizing systematic annotations. The idea is to randomly mask an image and train the model to predict the missing region. It is demonstrated that by predicting missing anatomical structures when seeing only parts of the image, the model will learn generic representation yielding better performance on various medical imaging applications via transfer learning.
The extensive experiments demonstrate that the proposed proxy task outperforms training from scratch in six out of seven medical imaging applications covering 2D and 3D classification and segmentation. Moreover, image out-painting proxy task offers competitive performance to state-of-the-art models pre-trained on ImageNet and other self-supervised baselines such as in-painting. Owing to its outstanding performance, out-painting is utilized as one of the self-supervised proxy tasks to provide generic 3D pre-trained models for medical image analysis.
ContributorsSodha, Vatsal Arvindkumar (Author) / Liang, Jianming (Thesis advisor) / Devarakonda, Murthy (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2020