Critical information about surface water bodies, particularly their dynamic behavior, is most effectively derived from water contour detection. However, the accurate detection of contours is complicated by the land–water ambiguity and the great imbalance between contour and non-contour data. A unique fully convolutional multiscale UNet-styled (MS UNet) deep network is proposed for accurate water contour detection in the visible spectrum. The MS UNet utilizes blocks of multiscale convolutional filters to improve contour detection and employs loss functions to correct the imbalance between contour and non-contour data, as well as capture the loss at both the pixel and object levels. The proposed system is shown to be more effective at detecting water contours than recent water detection systems and other popular image segmentation networks while using a fraction of the parameters.
An inexpensive, non-intrusive, vision-based, active fatigue monitoring system is presented. The
system employs a single consumer webcam that is modified to operate in the near-IR range. An
active IR LED system is developed to facilitate the quick localization of the eye pupils. Imaging
software tracks the eye features by analyzing intensity areas and their changes in the vicinity of
localization. To quantify the level of fatigue the algorithm measures the opening of the eyelid,
PERCLOS.
The software developed runs on the workstation and is designed to draw limited
computational power, so as to not interfere with the user task. To overcome low-frame rate and
improve real-time monitoring, a two-phase detection and tacking algorithm is implemented. The
results presented show that the system successfully monitors the level of fatigue at a low rate of
8 fps. The system is well suited to monitor users in command centers, flight control centers,
airport traffic dispatchers, military operation and command centers, etc., but the work can be
extended to wearable devices and other environments.
In this paper, we propose the use of directional Gabor filtering and multifractal analysis based quality control (QC) to
provide accurate identification of precipitation in weather data collected from meteorological-radar volume scans. The
QC algorithm is an objective algorithm that minimizes human interaction. The algorithm utilizes both textural and
intensity information obtained from the two lower-elevation reflectivity maps. Computer simulations are provided to
show the effectiveness of this algorithm.
In this paper we propose the use of discrete pseudorandom phase mask for a spatially efficient phase encoded JTC. In the proposed JTC system the reference image is phase encoded using a pseudorandom phase mask to eliminate extraneous peaks cluttering or overshadowing the correlation output. The phase encoding scheme also eliminates the need for a spatial separation in the joint input image resulting in the full use of the SLM. An optoelectronic architecture of the proposed system is presented. To relax the need for a phase-only SLM with a high phase resolution we proposed the use of discrete pentary pseudorandom phase mask.
This paper presents a phase-modulated optoelectronic joint transfer correlator (PM-JTC) in which the
reference image is phase encoded using a random phase function. The phase encoding is used to remove
extraneous peaks from the correlation plane as well as improve the spatially efficiency of the JTC system.
The reference images are phase encoded apriori making the system fast for real-time feedback. The
optoelectronic PM-JTC is presented and analyzed as an effective optoelectronic correlator. The
architecture of the proposed system is presented and a computer simulations of the systems are shown.
Face recognition based on independent component analysis (ICA) has emerged as a popular approach for face recognition application. In this paper we present a comparison between various optoelectronic face recognition techniques and ICA based face recognition. Computer simulations are used to study the effectiveness of the fastICA algorithm in recognizing facial images with a high level of three-dimensional (3-D) distortion. Results are then compared to various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical correlator based neural networks, and pose estimation based correlation.
In this paper we present a multiple reference optoelectronic joint transfer correlator (JTC) in which the reference images are phase encoded using a pseudorandom phase sequence. The phase encoding is used to remove extraneous peaks from the correlation plane as well as improve the spatially efficiency of the JTC system. The reference images are phase encoded apriori making the system fast for real-time feedback. The optoelectronic joint transform correlator (JTC) is presented and analyzed as an effective optoelectronic correlator. The architecture of the proposed system is presented.
In this paper, we propose the use of optoelectronic joint transform correlator (JTC) and multifractal analysis based quality control (QC) to provide accurate, real-time identification of precipitation in weather data collected from meteorological-radar volume scans. The multifractal based QC algorithm is an objective algorithm that minimizes human interaction. The algorithm utilizes both textural and intensity information obtained from the two lower-elevation reflectivity maps. The multifractal exponents are obtained using the JTC system. Computer simulations are provided to show the effectiveness of this system.
We present a technique for multiwavelet analysis using a phase-encoded joint transfer correlator (JTC). The optoelectronic JTC is presented and analyzed as an effective tool for ultrafast wavelet analysis. By multiplexing reference wavelets in phase, multiwavelet analysis is performed in a single correlation. The architecture of the proposed system and computer simulations are presented.
In this paper we present a technique for multiwavelet analysis using a phase-encoded optoelectronic joint transfer correlator (JTC). The optoelectronic joint transform correlator (JTC) is presented and analyzed as an effective optoelectronic correlator for ultra-fast wavelet analysis. By multiplexing reference wavelets in phase, mutiwavelet analysis is performed in a single correlation. The architecture of the proposed system and computer simulations are presented.
Face recognition based on principal component analysis (PCA) using eigenfaces is popular in face recognition markets. In this paper we present a comparison between various optoelectronic face recognition techniques and principal component analysis (PCA) based technique for face recognition. Computer simulations are used to study the effectiveness of PCA based technique especially for facial images with a high level of distortion. Results are then compared to various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical correlator based neural networks, and pose estimation based correlation.
In this paper we present a comparison between four invariant face recognition techniques: synthetic discriminant function (SDF) based recognition, projection-slice SDF based recognition, optoelectronic correlator based neural network, and pose estimation based recognition. The pose estimation technique does not involve composite image generation and is most successful of these techniques mainly due to its success in maintaining a low interclass crosscorrelation. The optoelectronic fringe-adjusted joint transform correlator (JTC) is used to provide correlation. A description of the optoelectronic system and the simulation results are provided.
In this paper we describe a simple pose estimation based optoelectronic system for the recognition of facial images. The pose estimation described herein is a simple digital algorithm that is used to replace the composite image generation techniques while maintaining a low interclass crosscorrelation. The optoelectronic fringe adjusted joint transform correlator (FJTC) is then used to provide correlation. A description of the optoelectronic system and the entire process is presented. In addition, simulation results and comparison to SDF based recognition are provided to prove the effectiveness of the proposed system.
The projection slice theorem has been used to create synthetic discriminant function (SDF) based matched filters that are capable of discerning rotation and scale distortions. In this paper, we propose to use the projection slice algorithm for invariant face recognition that is capable of accommodating in-plane and out-of-plane 3D distortions. The projection slice algorithm is applied to train images in order to synthesize a composite image to represent each class. The optoelectronic fringe-adjusted joint transform correlator (JTC) technique is then used to correlate the test images with the composite images of each class. The fringe-adjusted JTC technique has been chosen due to its superior performance over alternate JTCs and the feasibility of its implementation in the all- optical domain. Simulation results are furnished to prove the effectiveness of the proposed system.
In this paper, we propose an optoelectronic fringe-adjusted joint transform correlator (JTC) based two-layer neural network for invariant face recognition while accommodating in-plane and out-of-plane 3D distortions. The neural network is utilized in the training stage for a sequence of facial images and through a process of supervised learning in order to create composite images that are invariant to 3D distortions. The proposed technique is implemented by using the FJTC technique. The FJTC technique has been chosen due to its superior performance over alternate JTCs and the feasibility of its implementation in the all-optical domain. The simulation results obtained from the proposed technique are then compared with those obtained using alternate techniques (such as using synthetic discriminant functions). The fringe-adjusted JTC based neural network technique has been found to be more efficient and yields better results than the synthetic discriminant function based technique.
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