Papers by Larry Pearlstein
2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Deep convolutional neural networks have been successfully deployed by large, well-funded teams, b... more Deep convolutional neural networks have been successfully deployed by large, well-funded teams, but their wider adoption is often limited by the cost and schedule ramifications of their requirement for massive amounts of labeled data. We address this problem through the use of a parameterized synthetic image generator. Our approach is particularly novel in that we have been able to fine tune the generator’s parameters through the use of a generative adversarial network. We describe our approach, and present results that demonstrate its potential benefits. We demonstrate the PSIG-GAN by creating images for training a DCNN to detect the existence and location of weeds in lawn grass.
COMPUSOFT: An International Journal of Advanced Computer Technology, Oct 3, 2019
2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2016
Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challeng... more Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challenging problems in image classification. Based on the successes of DCNNs in scene classification and object detection and localization it is natural to consider whether they would be effective for much simpler computer vision tasks. Our work involves the application of a DCNN to the relatively simple task of detecting weeds in lawn grass. We looked at the effects of the choice of CNN hyper-parameters on accuracy and training convergence behavior. In order to obtain a large labeled set of interesting data we generated realistic synthetic imagery. Since our problem is somewhat constrained we were able to run thousands of training experiments and do accurate estimation of the probability density function of the convergence rate. Our results suggest that the use of realistic synthetic imagery is an effective approach for training DCNNs, and that very small DCNNs can be effective for simple image recognition tasks.
2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2018
In recent years, deep convolution neural networks (DCNNs) have won many contests in machine learn... more In recent years, deep convolution neural networks (DCNNs) have won many contests in machine learning, object detection, and pattern recognition. Furthermore, deep learning techniques achieved exceptional performance in image classification, reaching accuracy levels beyond human capability. Malware variants from similar categories often contain similarities due to code reuse. Converting malware samples into images can cause these patterns to manifest as image features, which can be exploited for DCNN classification. Techniques for converting malware binaries into images for visualization and classification have been reported in the literature, and while these methods do reach a high level of classification accuracy on training datasets, they tend to be vulnerable to overfitting and perform poorly on previously unseen samples. In this paper, we explore and document a variety of techniques for representing malware binaries as images with the goal of discovering a format best suited for deep learning. We implement a database for malware binaries from several families, stored in hexadecimal format. These malware samples are converted into images using various approaches and are used to train a neural network to recognize visual patterns in the input and classify malware based on the feature vectors. Each image type is assessed using a variety of learning models, such as transfer learning with existing DCNN architectures and feature extraction for support vector machine classifier training. Each technique is evaluated in terms of classification accuracy, result consistency, and time per trial. Our preliminary results indicate that improved image representation has the potential to enable more effective classification of new malware.
2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2018
Prior to the advent of ITU-R Recommendation BT.709 the overwhelming majority of compressed digita... more Prior to the advent of ITU-R Recommendation BT.709 the overwhelming majority of compressed digital video and imagery used the colorspace conversion matrix specified in ITU-R Recommendation BT.601. The introduction of high-definition video formats led to the adoption of Rec. BT.709 for use in colorspace conversion by new systems, and this resulted in confusion in the industry. Specifically, video decoders may not be able to determine the correct matrix to use for converting from the luma/chroma representation used for coding, to the Red-Green-Blue representation needed for display. This confusion has led to a situation where some viewers of decompressed video streams experience subtle, but noticeable, errors in coloration. We have successfully developed and trained a deep convolutional neural network to address this heretofore unsolved problem. We obtained outstanding accuracy on ImageNet data, and on YouTube video frames, and our work can be expected to lead to more accurate color rendering delivered to users of digital imaging and video systems.
2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2016
One challenge in a video surveillance system is the data rate required to represent digital video... more One challenge in a video surveillance system is the data rate required to represent digital video. Accordingly, the use of lossy video compression at a compression ratio of 100:1, or higher, is an essential part of any distributed live video system. The ensuing distortion can interfere with the goals of surveillance by confounding both human analysis and computer vision based processing. This paper investigates the interaction between the video coding layer and target detection, and proposes methods for improving overall system effectiveness. Previous related research has focused on joint optimization of the video coding layer where several streams share the same bandwidth. Our work is distinguished from prior studies in several area: we use Gradual Decoder Refresh, rather than the traditional GOP, to enable low delay and similarly avoid the use of B frames, which necessitate frame reordering. We extend the previous work by providing the ROC curves for the detection of foreground ob...
L'invention concerne des procedes et des appareils facilitant le fonctionnement de decodeurs ... more L'invention concerne des procedes et des appareils facilitant le fonctionnement de decodeurs video. Les procedes de cette invention s'appuient notamment sur des techniques de reduction des donnees et de quantification inverse simplifiees, destinees a modifier de maniere dynamique la complexite des operations de rehaussement des images, notamment par des operations utilisant un filtre de prediction, au fur et a mesure qu'une fonction des donnees relatives a la luminance ou a la chrominance est traitee. Afin de reduire l'encombrement en memoire, ces donnees relatives a la luminance ou a la chrominance correspondant a des images prealablement codees peuvent etre memorisees a differentes resolutions. Des parties des trames B non destinees a etre affichees sont eliminees, les parties des trames I et P non destinees a etre affichees etant decodees a une resolution reduite et/ou a l'aide de techniques de quantification inverse simplifiees. Une autre technique de reducti...
We present a novel approach to video coding that can dramatically reduce decoder DRAM bandwidth r... more We present a novel approach to video coding that can dramatically reduce decoder DRAM bandwidth requirements while incurring a minimal reduction in compression efficiency, and which may yield an increase in compression efficiency under certain circumstances. Our approach is based on the principle that areas of video pictures where there is high motion are typically captured with significant blur along the direction of motion. This blur permits the judicious use of reduced resolution reference pictures for prediction without significantly reducing prediction quality. Our approach makes it feasible to limit worstcase DRAM bandwidth through the use of reasonably sized onchip caches for pixel data, which can lead to provably compliant real-time behavior. The reductions in DRAM bandwidth can be expected to yield commensurate reductions in DRAM power dissipation, and consequently improvements in battery life for mobile devices. Although we concentrate our analysis on decoders, our approac...
Annual Conference of the PHM Society, 2021
Intelligent fault diagnosis utilizing deep learning algorithms has been widely investigated recen... more Intelligent fault diagnosis utilizing deep learning algorithms has been widely investigated recently. Although previous results demonstrated excellent performance, features learned by Deep Neural Networks (DNN) are part of a large black box. Consequently, lack of understanding of underlying physical meanings embedded within the features can lead to poor performance when applied to different but related datasets i.e. transfer learning applications. This study will investigate the transfer learning performance of a Convolution Neural Network (CNN) considering 4 different operating conditions. Utilizing the Case Western Reserve University (CWRU) bearing dataset, the CNN will be trained to classify 12 classes. Each class represents a unique differentfault scenario with varying severity i.e. inner race fault of 0.007”, 0.014” diameter. Initially, zero load data will be utilized for model training and the model will be tuned until testing accuracy above 99% is obtained. The model performa...
2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2017
In recent years, the accurate characterization of the boost phase of a missile's flight has b... more In recent years, the accurate characterization of the boost phase of a missile's flight has become a more challenging and prominent research topic as the noise level is extremely large relative to the quantity of interest. Reconstructing the boost phase acceleration profile of a ballistic missile from state observation is of interest to the technical intelligence community, ballistic missile defense, as well as the missile warning community. There are methods available such as Tikhonov regularization if the noise level is not too large. However, if the noise environment is very high most algorithms will perform poorly. In this paper, we explore the problem of estimating the thrust of a missile from very noisy estimates of its position over time by using wavelet techniques. Several wavelet basis functions and multi-resolution methods are explored to yield the most effective solution to this problem. These techniques have been successfully used on actual rocket-launch data in the ...
2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2019
In recent years, an increasing number of devices are being connected to the Internet that encompa... more In recent years, an increasing number of devices are being connected to the Internet that encompasses more than just traditional devices. Internet of Things integrates real-world sensors such as smart devices or environment sensors with the Internet allowing for real}-time monitoring of conditions. IoT devices are often constrained in their resources as the sensors involved are designed for specific purposes. Due to these constraints, typical methods of intrusion and anomaly detection cannot be used. Also, due to the amount of raw input data from these sensors, detecting anomalies among the noise and other background data can be computationally intensive. A possible solution to this is by using machine learning models that are trained on both normal and abnormal behavior to detect when anomalies occur. By using techniques such as autoencoders, models can be trained that have learned normal operating conditions. In this study, we explore the use of machine learning techniques such as autoencoders to effectively handle the high dimensionality of sensor datasets while consequently learning their normal operating conditions. Autoencoders are a type of neural network which attempts to reconstruct its input data by combining two NNs, an encoder, and a decoder network. The encoder learns its input by encoding it into a lower-dimensional space while capturing the interactions and correlations between variables. In this paper, we explore the use of techniques such as autoencoders to create a lower-dimensional representation of high dimensional sensor input. Autoencoders encode the data allowing for the network to learn the interactions between parameters in normal conditions which when reconstructed with the decoder represents non-anomalous behavior. When data containing anomalies are input into the network errors will occur within the reconstruction. The error between the reconstructions can be measured using a distance function to determine if an observation is anomalous.
Automatic Target Recognition XXX, 2020
This paper addresses the problem of determining the probability mass function of connected compon... more This paper addresses the problem of determining the probability mass function of connected component sizes for independent and identically distributed binary images. We derive an exact solution and an effective approximation that can be readily computed for all component sizes.
International Journal of Machine Learning and Computing
International Journal of Machine Learning and Computing
ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing
2008 IEEE Hot Chips 20 Symposium (HCS), 2008
Proceedings 137th SMPTE Technical Conference and World Media Expo, 1995
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Papers by Larry Pearlstein