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- All Subjects: engineering
- All Subjects: Image processing--Digital techniques.
- Genre: Doctoral Dissertation
- Creators: Li, Baoxin
Description
Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.
ContributorsVaradarajan, Srenivas (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Li, Baoxin (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Situations of sensory overload are steadily becoming more frequent as the ubiquity of technology approaches reality--particularly with the advent of socio-communicative smartphone applications, and pervasive, high speed wireless networks. Although the ease of accessing information has improved our communication effectiveness and efficiency, our visual and auditory modalities--those modalities that today's computerized devices and displays largely engage--have become overloaded, creating possibilities for distractions, delays and high cognitive load; which in turn can lead to a loss of situational awareness, increasing chances for life threatening situations such as texting while driving. Surprisingly, alternative modalities for information delivery have seen little exploration. Touch, in particular, is a promising candidate given that it is our largest sensory organ with impressive spatial and temporal acuity. Although some approaches have been proposed for touch-based information delivery, they are not without limitations including high learning curves, limited applicability and/or limited expression. This is largely due to the lack of a versatile, comprehensive design theory--specifically, a theory that addresses the design of touch-based building blocks for expandable, efficient, rich and robust touch languages that are easy to learn and use. Moreover, beyond design, there is a lack of implementation and evaluation theories for such languages. To overcome these limitations, a unified, theoretical framework, inspired by natural, spoken language, is proposed called Somatic ABC's for Articulating (designing), Building (developing) and Confirming (evaluating) touch-based languages. To evaluate the usefulness of Somatic ABC's, its design, implementation and evaluation theories were applied to create communication languages for two very unique application areas: audio described movies and motor learning. These applications were chosen as they presented opportunities for complementing communication by offloading information, typically conveyed visually and/or aurally, to the skin. For both studies, it was found that Somatic ABC's aided the design, development and evaluation of rich somatic languages with distinct and natural communication units.
ContributorsMcDaniel, Troy Lee (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2012
Description
Digital imaging and image processing technologies have revolutionized the way in which
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.
ContributorsGolestaneh, Seyedalireza (Author) / Karam, Lina (Thesis advisor) / Bliss, Daniel W. (Committee member) / Li, Baoxin (Committee member) / Turaga, Pavan K. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Visual attention (VA) is the study of mechanisms that allow the human visual system (HVS) to selectively process relevant visual information. This work focuses on the subjective and objective evaluation of computational VA models for the distortion-free case as well as in the presence of image distortions.
Existing VA models are traditionally evaluated by using VA metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though there is a considerable number of objective VA metrics, there exists no study that validates that these metrics are adequate for the evaluation of VA models. This work constructs a VA Quality (VAQ) Database by subjectively assessing the prediction performance of VA models on distortion-free images. Additionally, shortcomings in existing metrics are discussed through illustrative examples and a new metric that uses local weights based on fixation density and that overcomes these flaws, is proposed. The proposed VA metric outperforms all other popular existing metrics in terms of the correlation with subjective ratings.
In practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression, and transmission. However, none of the existing studies have discussed the subjective and objective evaluation of visual saliency models in the presence of distortion. In this work, a Distortion-based Visual Attention Quality (DVAQ) subjective database is constructed to evaluate the quality of VA maps for images in the presence of distortions. For creating this database, saliency maps obtained from images subjected to various types of distortions, including blur, noise and compression, and varying levels of distortion severity are rated by human observers in terms of their visual resemblance to corresponding ground-truth fixation density maps. The performance of traditionally used as well as recently proposed VA metrics are evaluated by correlating their scores with the human subjective ratings. In addition, an objective evaluation of 20 state-of-the-art VA models is performed using the top-performing VA metrics together with a study of how the VA models’ prediction performance changes with different types and levels of distortions.
Existing VA models are traditionally evaluated by using VA metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though there is a considerable number of objective VA metrics, there exists no study that validates that these metrics are adequate for the evaluation of VA models. This work constructs a VA Quality (VAQ) Database by subjectively assessing the prediction performance of VA models on distortion-free images. Additionally, shortcomings in existing metrics are discussed through illustrative examples and a new metric that uses local weights based on fixation density and that overcomes these flaws, is proposed. The proposed VA metric outperforms all other popular existing metrics in terms of the correlation with subjective ratings.
In practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression, and transmission. However, none of the existing studies have discussed the subjective and objective evaluation of visual saliency models in the presence of distortion. In this work, a Distortion-based Visual Attention Quality (DVAQ) subjective database is constructed to evaluate the quality of VA maps for images in the presence of distortions. For creating this database, saliency maps obtained from images subjected to various types of distortions, including blur, noise and compression, and varying levels of distortion severity are rated by human observers in terms of their visual resemblance to corresponding ground-truth fixation density maps. The performance of traditionally used as well as recently proposed VA metrics are evaluated by correlating their scores with the human subjective ratings. In addition, an objective evaluation of 20 state-of-the-art VA models is performed using the top-performing VA metrics together with a study of how the VA models’ prediction performance changes with different types and levels of distortions.
ContributorsGide, Milind Subhash (Author) / Karam, Lina J (Thesis advisor) / Abousleman, Glen (Committee member) / Li, Baoxin (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Blur is an important attribute in the study and modeling of the human visual system. In this work, 3D blur discrimination experiments are conducted to measure the just noticeable additional blur required to differentiate a target blur from the reference blur level. The past studies on blur discrimination have measured the sensitivity of the human visual system to blur using 2D test patterns. In this dissertation, subjective tests are performed to measure blur discrimination thresholds using stereoscopic 3D test patterns. The results of this study indicate that, in the symmetric stereo viewing case, binocular disparity does not affect the blur discrimination thresholds for the selected 3D test patterns. In the asymmetric viewing case, the blur discrimination thresholds decreased and the decrease in threshold values is found to be dominated by the eye observing the higher blur.
The second part of the dissertation focuses on texture granularity in the context of 2D images. A texture granularity database referred to as GranTEX, consisting of textures with varying granularity levels is constructed. A subjective study is conducted to measure the perceived granularity level of textures present in the GranTEX database. An objective index that automatically measures the perceived granularity level of textures is also presented. It is shown that the proposed granularity metric correlates well with the subjective granularity scores and outperforms the other methods presented in the literature.
A subjective study is conducted to assess the effect of compression on textures with varying degrees of granularity. A logarithmic function model is proposed as a fit to the subjective test data. It is demonstrated that the proposed model can be used for rate-distortion control by allowing the automatic selection of the needed compression ratio for a target visual quality. The proposed model can also be used for visual quality assessment by providing a measure of the visual quality for a target compression ratio.
The effect of texture granularity on the quality of synthesized textures is studied. A subjective study is presented to assess the quality of synthesized textures with varying levels of texture granularity using different types of texture synthesis methods. This work also proposes a reduced-reference visual quality index referred to as delta texture granularity index for assessing the visual quality of synthesized textures.
The second part of the dissertation focuses on texture granularity in the context of 2D images. A texture granularity database referred to as GranTEX, consisting of textures with varying granularity levels is constructed. A subjective study is conducted to measure the perceived granularity level of textures present in the GranTEX database. An objective index that automatically measures the perceived granularity level of textures is also presented. It is shown that the proposed granularity metric correlates well with the subjective granularity scores and outperforms the other methods presented in the literature.
A subjective study is conducted to assess the effect of compression on textures with varying degrees of granularity. A logarithmic function model is proposed as a fit to the subjective test data. It is demonstrated that the proposed model can be used for rate-distortion control by allowing the automatic selection of the needed compression ratio for a target visual quality. The proposed model can also be used for visual quality assessment by providing a measure of the visual quality for a target compression ratio.
The effect of texture granularity on the quality of synthesized textures is studied. A subjective study is presented to assess the quality of synthesized textures with varying levels of texture granularity using different types of texture synthesis methods. This work also proposes a reduced-reference visual quality index referred to as delta texture granularity index for assessing the visual quality of synthesized textures.
ContributorsSubedar, Mahesh M (Author) / Karam, Lina (Thesis advisor) / Abousleman, Glen (Committee member) / Li, Baoxin (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2015
Description
The quality of real-world visual content is typically impaired by many factors including image noise and blur. Detecting and analyzing these impairments are important steps for multiple computer vision tasks. This work focuses on perceptual-based locally adaptive noise and blur detection and their application to image restoration.
In the context of noise detection, this work proposes perceptual-based full-reference and no-reference objective image quality metrics by integrating perceptually weighted local noise into a probability summation model. Results are reported on both the LIVE and TID2008 databases. The proposed metrics achieve consistently a good performance across noise types and across databases as compared to many of the best very recent quality metrics. The proposed metrics are able to predict with high accuracy the relative amount of perceived noise in images of different content.
In the context of blur detection, existing approaches are either computationally costly or cannot perform reliably when dealing with the spatially-varying nature of the defocus blur. In addition, many existing approaches do not take human perception into account. This work proposes a blur detection algorithm that is capable of detecting and quantifying the level of spatially-varying blur by integrating directional edge spread calculation, probability of blur detection and local probability summation. The proposed method generates a blur map indicating the relative amount of perceived local blurriness. In order to detect the flat
ear flat regions that do not contribute to perceivable blur, a perceptual model based on the Just Noticeable Difference (JND) is further integrated in the proposed blur detection algorithm to generate perceptually significant blur maps. We compare our proposed method with six other state-of-the-art blur detection methods. Experimental results show that the proposed method performs the best both visually and quantitatively.
This work further investigates the application of the proposed blur detection methods to image deblurring. Two selective perceptual-based image deblurring frameworks are proposed, to improve the image deblurring results and to reduce the restoration artifacts. In addition, an edge-enhanced super resolution algorithm is proposed, and is shown to achieve better reconstructed results for the edge regions.
In the context of noise detection, this work proposes perceptual-based full-reference and no-reference objective image quality metrics by integrating perceptually weighted local noise into a probability summation model. Results are reported on both the LIVE and TID2008 databases. The proposed metrics achieve consistently a good performance across noise types and across databases as compared to many of the best very recent quality metrics. The proposed metrics are able to predict with high accuracy the relative amount of perceived noise in images of different content.
In the context of blur detection, existing approaches are either computationally costly or cannot perform reliably when dealing with the spatially-varying nature of the defocus blur. In addition, many existing approaches do not take human perception into account. This work proposes a blur detection algorithm that is capable of detecting and quantifying the level of spatially-varying blur by integrating directional edge spread calculation, probability of blur detection and local probability summation. The proposed method generates a blur map indicating the relative amount of perceived local blurriness. In order to detect the flat
ear flat regions that do not contribute to perceivable blur, a perceptual model based on the Just Noticeable Difference (JND) is further integrated in the proposed blur detection algorithm to generate perceptually significant blur maps. We compare our proposed method with six other state-of-the-art blur detection methods. Experimental results show that the proposed method performs the best both visually and quantitatively.
This work further investigates the application of the proposed blur detection methods to image deblurring. Two selective perceptual-based image deblurring frameworks are proposed, to improve the image deblurring results and to reduce the restoration artifacts. In addition, an edge-enhanced super resolution algorithm is proposed, and is shown to achieve better reconstructed results for the edge regions.
ContributorsZhu, Tong (Author) / Karam, Lina (Thesis advisor) / Li, Baoxin (Committee member) / Bliss, Daniel (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Created2016
Description
Machine learning has demonstrated great potential across a wide range of applications such as computer vision, robotics, speech recognition, drug discovery, material science, and physics simulation. Despite its current success, however, there are still two major challenges for machine learning algorithms: limited robustness and generalizability.
The robustness of a neural network is defined as the stability of the network output under small input perturbations. It has been shown that neural networks are very sensitive to input perturbations, and the prediction from convolutional neural networks can be totally different for input images that are visually indistinguishable to human eyes. Based on such property, hackers can reversely engineer the input to trick machine learning systems in targeted ways. These adversarial attacks have shown to be surprisingly effective, which has raised serious concerns over safety-critical applications like autonomous driving. In the meantime, many established defense mechanisms have shown to be vulnerable under more advanced attacks proposed later, and how to improve the robustness of neural networks is still an open question.
The generalizability of neural networks refers to the ability of networks to perform well on unseen data rather than just the data that they were trained on. Neural networks often fail to carry out reliable generalizations when the testing data is of different distribution compared with the training one, which will make autonomous driving systems risky under new environment. The generalizability of neural networks can also be limited whenever there is a scarcity of training data, while it can be expensive to acquire large datasets either experimentally or numerically for engineering applications, such as material and chemical design.
In this dissertation, we are thus motivated to improve the robustness and generalizability of neural networks. Firstly, unlike traditional bottom-up classifiers, we use a pre-trained generative model to perform top-down reasoning and infer the label information. The proposed generative classifier has shown to be promising in handling input distribution shifts. Secondly, we focus on improving the network robustness and propose an extension to adversarial training by considering the transformation invariance. Proposed method improves the robustness over state-of-the-art methods by 2.5% on MNIST and 3.7% on CIFAR-10. Thirdly, we focus on designing networks that generalize well at predicting physics response. Our physics prior knowledge is used to guide the designing of the network architecture, which enables efficient learning and inference. Proposed network is able to generalize well even when it is trained with a single image pair.
The robustness of a neural network is defined as the stability of the network output under small input perturbations. It has been shown that neural networks are very sensitive to input perturbations, and the prediction from convolutional neural networks can be totally different for input images that are visually indistinguishable to human eyes. Based on such property, hackers can reversely engineer the input to trick machine learning systems in targeted ways. These adversarial attacks have shown to be surprisingly effective, which has raised serious concerns over safety-critical applications like autonomous driving. In the meantime, many established defense mechanisms have shown to be vulnerable under more advanced attacks proposed later, and how to improve the robustness of neural networks is still an open question.
The generalizability of neural networks refers to the ability of networks to perform well on unseen data rather than just the data that they were trained on. Neural networks often fail to carry out reliable generalizations when the testing data is of different distribution compared with the training one, which will make autonomous driving systems risky under new environment. The generalizability of neural networks can also be limited whenever there is a scarcity of training data, while it can be expensive to acquire large datasets either experimentally or numerically for engineering applications, such as material and chemical design.
In this dissertation, we are thus motivated to improve the robustness and generalizability of neural networks. Firstly, unlike traditional bottom-up classifiers, we use a pre-trained generative model to perform top-down reasoning and infer the label information. The proposed generative classifier has shown to be promising in handling input distribution shifts. Secondly, we focus on improving the network robustness and propose an extension to adversarial training by considering the transformation invariance. Proposed method improves the robustness over state-of-the-art methods by 2.5% on MNIST and 3.7% on CIFAR-10. Thirdly, we focus on designing networks that generalize well at predicting physics response. Our physics prior knowledge is used to guide the designing of the network architecture, which enables efficient learning and inference. Proposed network is able to generalize well even when it is trained with a single image pair.
ContributorsYao, Houpu (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2019
Description
Access to real-time situational information including the relative position and motion of surrounding objects is critical for safe and independent travel. Object or obstacle (OO) detection at a distance is primarily a task of the visual system due to the high resolution information the eyes are able to receive from afar. As a sensory organ in particular, the eyes have an unparalleled ability to adjust to varying degrees of light, color, and distance. Therefore, in the case of a non-visual traveler, someone who is blind or low vision, access to visual information is unattainable if it is positioned beyond the reach of the preferred mobility device or outside the path of travel. Although, the area of assistive technology in terms of electronic travel aids (ETA’s) has received considerable attention over the last two decades; surprisingly, the field has seen little work in the area focused on augmenting rather than replacing current non-visual travel techniques, methods, and tools. Consequently, this work describes the design of an intuitive tactile language and series of wearable tactile interfaces (the Haptic Chair, HaptWrap, and HapBack) to deliver real-time spatiotemporal data. The overall intuitiveness of the haptic mappings conveyed through the tactile interfaces are evaluated using a combination of absolute identification accuracy of a series of patterns and subjective feedback through post-experiment surveys. Two types of spatiotemporal representations are considered: static patterns representing object location at a single time instance, and dynamic patterns, added in the HaptWrap, which represent object movement over a time interval. Results support the viability of multi-dimensional haptics applied to the body to yield an intuitive understanding of dynamic interactions occurring around the navigator during travel. Lastly, it is important to point out that the guiding principle of this work centered on providing the navigator with spatial knowledge otherwise unattainable through current mobility techniques, methods, and tools, thus, providing the \emph{navigator} with the information necessary to make informed navigation decisions independently, at a distance.
ContributorsDuarte, Bryan Joiner (Author) / McDaniel, Troy (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020
Description
Over the past decade, machine learning research has made great strides and significant impact in several fields. Its success is greatly attributed to the development of effective machine learning algorithms like deep neural networks (a.k.a. deep learning), availability of large-scale databases and access to specialized hardware like Graphic Processing Units. When designing and training machine learning systems, researchers often assume access to large quantities of data that capture different possible variations. Variations in the data is needed to incorporate desired invariance and robustness properties in the machine learning system, especially in the case of deep learning algorithms. However, it is very difficult to gather such data in a real-world setting. For example, in certain medical/healthcare applications, it is very challenging to have access to data from all possible scenarios or with the necessary amount of variations as required to train the system. Additionally, the over-parameterized and unconstrained nature of deep neural networks can cause them to be poorly trained and in many cases over-confident which, in turn, can hamper their reliability and generalizability. This dissertation is a compendium of my research efforts to address the above challenges. I propose building invariant feature representations by wedding concepts from topological data analysis and Riemannian geometry, that automatically incorporate the desired invariance properties for different computer vision applications. I discuss how deep learning can be used to address some of the common challenges faced when working with topological data analysis methods. I describe alternative learning strategies based on unsupervised learning and transfer learning to address issues like dataset shifts and limited training data. Finally, I discuss my preliminary work on applying simple orthogonal constraints on deep learning feature representations to help develop more reliable and better calibrated models.
ContributorsSom, Anirudh (Author) / Turaga, Pavan (Thesis advisor) / Krishnamurthi, Narayanan (Committee member) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2020
Description
Despite the fact that machine learning supports the development of computer vision applications by shortening the development cycle, finding a general learning algorithm that solves a wide range of applications is still bounded by the ”no free lunch theorem”. The search for the right algorithm to solve a specific problem is driven by the problem itself, the data availability and many other requirements.
Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging.
In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the
multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.
Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging.
In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the
multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.
ContributorsHaddad, Bashar Muneer (Author) / Karam, Lina (Thesis advisor) / Li, Baoxin (Committee member) / He, Jingrui (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2019