In this paper we present a comparative analysis of three vision systems to nondestructively predict defects on the surfaces of aluminum castings. A hyperspectral imaging system, a thermal imager, and a digital color camera have been used to inspect aluminum metal cast surfaces. Hyperspectral imaging provides both spectral and spatial information, as each material produces specific spectral signatures which are also affected by surface texture. Thermal imager detects infrared radiation whereby hotspots can be investigated to identify possible trapped inclusions close to the surface, or other superficial defects. Finally, digital color images show apparent surface defects that can also be viewed with the naked eye but can be automated for fast and efficient data analysis. The surface defect locations predicted using the three systems are then verified by breaking the casings using a tensile tester. Of the three nondestructive methods, the thermal imaging camera was found to produce the most accurate predictions for defect location that caused breakage.
Coral reefs are one of the most diverse and threatened ecosystems in the world. Corals worldwide are at risk, and in many instances, dying due to factors that affect their environment resulting in deteriorating environmental conditions. Because corals respond quickly to the quality of the environment that surrounds them, corals have been identified as bioindicators of water quality and marine environmental health. The hyperspectral imaging system is proposed as a noninvasive tool to monitor different species of corals as well as coral state over time. This in turn can be used as a quick and non-invasive method to monitor environmental health that can later be extended to climate conditions. In this project, a laboratory-based hyperspectral imaging system is used to collect spectral and spatial information of corals. In the work presented here, MATLAB and ENVI software tools are used to view and process spatial information and coral spectral signatures to identify differences among the coral data. The results support the hypothesis that hyperspectral properties of corals vary among different coral species, and coral state over time, and hyperspectral imaging can be a used as a tool to document changes in coral species and state.
A real-time iris detection and tracking algorithm has been implemented on a smart camera using LabVIEW graphical
programming tools. The program detects the eye and finds the center of the iris, which is recorded and stored in
Cartesian coordinates. In subsequent video frames, the location of the center of the iris corresponding to the previously
detected eye is computed and recorded for a desired period of time, creating a list of coordinates representing the
moving iris center location across image frames. We present an application for the developed smart camera iris tracking
system that involves the assessment of reading patterns. The purpose of the study is to identify differences in reading
patterns of readers at various levels to eventually determine successful reading strategies for improvement. The readers
are positioned in front of a computer screen with a fixed camera directed at the reader's eyes. The readers are then asked
to read preselected content on the computer screen, one comprising a traditional newspaper text and one a Web page.
The iris path is captured and stored in real-time. The reading patterns are examined by analyzing the path of the iris
movement. In this paper, the iris tracking system and algorithms, application of the system to real-time capture of
reading patterns, and representation of 2D/3D iris track are presented with results and recommendations.
A hyperspectral imaging system has been set up and used to capture hyperspectral image cubes from various samples in
the 400-1000 nm spectral region. The system consists of an imaging spectrometer attached to a CCD camera with fiber
optic light source as the illuminator. The significance of this system lies in its capability to capture 3D spectral and
spatial data that can then be analyzed to extract information about the underlying samples, monitor the variations in their
response to perturbation or changing environmental conditions, and compare optical properties. In this paper preliminary
results are presented that analyze the 3D spatial and spectral data in reflection mode to extract features to differentiate
among different classes of interest using biological and metallic samples. Studied biological samples possess
homogenous as well as non-homogenous properties. Metals are analyzed for their response to different surface
treatments, including polishing. Similarities and differences in the feature extraction process and results are presented.
The mathematical approach taken is discussed. The hyperspectral imaging system offers a unique imaging modality that
captures both spatial and spectral information that can then be correlated for future sample predictions.
Digital watermarking continues to be an open area of research. In this work, fractals are employed to spatially embed the
watermarks in the RGB domain. The watermarks are tested separately in each of the three planes R, G, and B. A blind
detection scheme is utilized in which the only information required for detection is the fractals used for the embedding.
Next, combinations of embedding in the RG, RB, GB, and RGB planes are used. The efficacy of the embedding
combinations in the various planes are studied to determine the best combinations for the tested fractals, images and
attacks. The results are compared with previously published methods by the authors.
This paper presents a real-time iris detection procedure for gray intensity images. Typical
applications for iris detection utilize template and feature based methods. These methods are
generally time and memory intensive and not applicable for all practical real-time embedded
realizations. Here, we propose a method that utilizes a simple algorithm that is time-efficient with
high detection and low error rates that is implemented in a smart camera. The system used for this
research involves a National Instruments smart camera with LabVIEW Real-Time Module. First, the
images are analyzed to determine the region of interest (face). The iris location is determined by
applying a convolution-based algorithm on the edge image and then using the Hough Transform.
The edge-based less complex and less computationally expensive algorithm results in an efficient
analysis method. The extracted iris location information is stored in the camera's image buffer, and
used to model one specific eye pattern. The location of the iris thus determined is used as a reference
to reduce the search region for the iris in the subsequent images. The iris detection algorithm has
been applied at different frame rates. The results demonstrate the speed of this algorithm allows the
tracking of the iris when the eyes or the subject is moving in front of the camera at reasonable
speeds and with limited occlusions.
In this research, a new combination discrete cosine transform (DCT) and discrete wavelet transform (DWT) based
watermarking system is studied. The first embedded watermark is encrypted using a chaos function, specifically the
Lorenz function, based key to further conceal the data. A chaos based embedded watermark method in the DCT domain
with blind watermark identification is developed and tested. Next the system is modified to utilize a second watermark
embedding and identification/detection process. The second uses a pseudo random number generated (PRNG)
watermark that is developed with a function of a Lorenz attractor data point as the seed state for the PRNG watermark in
the detection process in the DWT domain. The efficacy of the DCT based technique, DWT based method as well as the
combined DCT and DWT method is then compared to a previous techniques such as a NN based DWT based watermark
embedding and identification. The three studied methods are subjected to a subset of the Checkmark attacks. Results for
projection, shearing, warping, linear distortions, and Wiener filtering attacks are shown for the DWT embedded case.
In this paper, diabetic retinopathy is chosen for a sample target image to demonstrate the effectiveness of image
enlargement through pixel duplication in identifying regions of interest. Pixel duplication is presented as a simpler alternative to data interpolation techniques for detecting small structures in the images. A comparative analysis is performed on different image processing schemes applied to both original and pixel-duplicated images. Structures of interest are detected and and classification parameters optimized for minimum false positive detection in the original and enlarged retinal pictures. The error analysis demonstrates the advantages as well as shortcomings of pixel duplication in image enhancement when spatial averaging operations (smoothing filters) are also applied.
Two synchronized cameras are utilized to obtain independent video streams to detect moving objects from two different
viewing angles. The video frames are directly correlated in time. Moving objects in image frames from the two cameras
are identified and tagged for tracking. One advantage of such a system involves overcoming effects of occlusions that
could result in an object in partial or full view in one camera, when the same object is fully visible in another camera.
Object registration is achieved by determining the location of common features in the moving object across simultaneous
frames. Perspective differences are adjusted. Combining information from images from multiple cameras increases
robustness of the tracking process. Motion tracking is achieved by determining anomalies caused by the objects'
movement across frames in time in each and the combined video information. The path of each object is determined
heuristically. Accuracy of detection is dependent on the speed of the object as well as variations in direction of motion.
Fast cameras increase accuracy but limit the speed and complexity of the algorithm. Such an imaging system has
applications in traffic analysis, surveillance and security, as well as object modeling from multi-view images. The
system can easily be expanded by increasing the number of cameras such that there is an overlap between the scenes
from at least two cameras in proximity. An object can then be tracked long distances or across multiple cameras
continuously, applicable, for example, in wireless sensor networks for surveillance or navigation.
When images undergo filtering operations, valuable information can be lost besides the intended noise or frequencies
due to averaging of neighboring pixels. When the image is enlarged by duplicating pixels, such filtering effects can be
reduced and more information retained, which could be critical when analyzing image content automatically. Analysis of
retinal images could reveal many diseases at early stage as long as minor changes that depart from a normal retinal scan
can be identified and enhanced. In this paper, typical filtering techniques are applied to an early stage diabetic
retinopathy image which has undergone digital pixel duplication. The same techniques are applied to the original images
for comparison. The effects of filtering are then demonstrated for both pixel duplicated and original images to show the
information retention capability of pixel duplication. Image quality is computed based on published metrics. Our
analysis shows that pixel duplication is effective in retaining information on smoothing operations such as mean filtering
in the spatial domain, as well as lowpass and highpass filtering in the frequency domain, based on the filter window size.
Blocking effects due to image compression and pixel duplication become apparent in frequency analysis.
In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal
component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal
components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types
of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital
copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics,
the probable manipulation(s) or modification(s) performed on the digital information can be identified using the
Multi-objective evolutionary algorithms (MOEAs) have been utilized in many fields to optimize designs and constraints
using biologically inspired methods. In this research, MOEAs are used to determine more optimal DCT based filter
coefficient sets in order to enhance images under various image processing attacks and functions. The filter coefficients
are adapted to minimize the mean squared error and to remove noise-induced artifacts. The capabilities of the proposed
enhanced image filters are demonstrated on multiple digital images.
In this paper, we present a semi real-time vehicle tracking algorithm to determine the speed of the vehicles in traffic
from traffic cam video. The results of this work can be used for traffic control, security and safety both by government
agencies and commercial organizations. The method described in this paper involves object feature identification,
detection, and tracking in multiple video frames. The distance between vertical broken lane markers has been used to
estimate absolute distances within each frame and convert pixel location coordinates to world coordinates. Speed
calculations are made based on the calibrated pixel distances. Optical flow images have been computed and used for
blob analysis to extract features representing moving objects. Some challenges exist in distinguishing among vehicles in
uniform flow of traffic when the object are too close, are in low contrast with one another, and travel with the same or
close to the same speed. In the absence of a ground truth for the actual speed of the tracked vehicles accuracy cannot be
determined. However, the vehicle speeds in steady flow of traffic have been computed to within 5% of the speed limit on
the analyzed highways in the video clips.
Historically, due to its uniqueness and immutability, fingerprints have been used as evidence in criminal cases and in
security identification as well as authorization verification applications. In this research, adaptive linear DWT
models are developed to describe the fingerprint features (DWT coefficients) to be identified. The proposed model
can be used to enhance the fingerprint characteristics identified from fingerprint images to improve recognition. This
adaptive model identification technique is then applied to degraded or incomplete fingerprint images to demonstrate
the efficacy of the technique under non-ideal conditions. The performance of the method is then compared to
previously published research by the authors on identification of degraded fingerprints using PCA-and ICA-based
In this work, we extend our previous research on gray level co-occurrence matrix (GLCM) based watermark embedding in the discrete cosine transform (DCT) domain to the discrete wavelet transform (DWT) domain. The GLCM method incorporated human visual system information into the embedding process making the watermark more transparent. DWT techniques allow for more compression as fewer coefficients are required to reconstruct an image. In addition, DWT methods will not exhibit block artifacts commonly encountered when applying block based DCT methods. The watermark identification is further enhanced using neural networks. In this research, daubechies wavelets are utilized to evaluate the efficiency of the watermark identification while the method is subjected to multiple attacks such as filtering, compression, or rotation. The results are then compared with previously published methods by the authors such as LMS based correlation and adaptive DWT based watermark identification.
In previous research, we have shown the ability of neural networks to improve the performance of the watermark system to identify the watermark under different attacks. On the other hand, in this work we apply neural networks to embed the watermark in the discrete wavelet transform (DWT) domain. We then use features based on principal component analysis (PCA) to blindly identify the watermark. PCA reduces the dimensionality as well as the redundancies of the data. Neural networks classifiers are implemented to determine whether the watermark is present. Different features are used to test the performance of the method. The efficacy of the technique is then compared to previous techniques such as the gray level co-occurrence matrix (GLCM) based or the LMS enhanced watermark identification. The comparative results from the previously used methods are presented in this paper.
In industrial process control, many processes or plants are already stable. Thus, the desired process transient behavior
and steady state error are the design constraints in these cases. Two common control techniques used in process control
are internal model control (IMC) or Proportional Integral Derivative (PID) control. IMC can only be used on already
stable or stabilized plants or processes due to its structure. Many plants or processes though cannot be completely
identified or are modeled using reduced order linear models. This can lead to modeling errors. On the other hand, neural
networks can be used to identify nonlinear processes or functions. In this research, neural networks are used for
intelligent/adaptive system identification of the plant to be utilized in the internal model control. This adaptive neural
network IMC structure is simulated to control a simplified process model. The efficacy of the neural network IMC
method is compared to classic PID control.
Many algorithms have been developed for fingerprint identification. The main challenge in many of the applications
remains in the identification of degraded images in which the fingerprints are smudged or incomplete. Fingerprints from
the FVC2000 databases have been utilized in this project to develop and implement feature extraction and classification
algorithms. Besides the degraded images in the database, artificially degraded images have also been used. In this paper
we use features based on PCA (principal component analysis) and ICA (independent component analysis) to identify
fingerprints. PCA and ICA reduce the dimensionality of the input image data. PCA- and ICA-based features do not
contain redundancies in the data. Different multilayer neural network architectures have been implemented as classifiers.
The performance of different features and networks is presented in this paper.
Transform techniques generally are more robust than spatial techniques for watermark embedding. In this research,
neural networks and adaptive models are utilized to estimate watermarks in the presence of noise as well as other
common image processing attacks in the discrete cosine transform (DCT) and discrete wavelet transform (DWT)
domains. The proposed method can be used to semi-blindly determine the estimated watermark. In this paper, a
comparative study to a previous method, LMS correlation based detection, is performed and demonstrates the efficacy
of the proposed adaptive neural network watermark embedding and detection scheme under different attacks. Finally,
the proposed scheme in the DCT transform domain is compared to the proposed scheme in the DWT domain.
In this paper we extend our previous work to address vehicle differentiation in traffic density computations<sup>1</sup>. The main
goal of this work is to create vehicle density history for given roads under different weather or light conditions and at
different times of the day. Vehicle differentiation is important to account for connected or otherwise long vehicles, such
as trucks or tankers, which lead to over-counting with the original algorithm. Average vehicle size in pixels, given the
magnification within the field of view for a particular camera, is used to separate regular cars and long vehicles.
A separate algorithm and procedure have been developed to determine traffic density after dark when the vehicle
headlights are turned on. Nighttime vehicle recognition utilizes blob analysis based on head/taillight images. The high
intensity of vehicle lights are identified in binary images for nighttime vehicle detection.
The stationary traffic image frames are downloaded from the internet as they are updated. The procedures are
implemented in MATLAB. The results of both nighttime traffic density and daytime long vehicle identification
algorithms are described in this paper. The determination of nighttime traffic density, and identification of long vehicles
at daytime are improvements over the original work<sup>1</sup>.
Many transform domain techniques have been developed for watermarking. Most of these techniques have been proven more robust than spatial domain methods after common image processing operations are applied to the watermarked images. In this research, adaptive models are used to help identify watermarks in the discrete cosine transform (DCT) domain. The adaptive models can be used to enhance the watermark detected after an attack. The watermark can thus be semi-blindly identified or estimated further allowing the estimation of the original image. In this paper, the susceptibility of the proposed DCT-based adaptive models to attacks is demonstrated on multiple digital images. The LMS correlation based detection is shown to be more robust than a simple correlation based detection.
In planetary or hazardous environment exploration, there will be unforseen environmental circumstances which can not be planned. To overcome telerobotic control issues due to communication delays, autonomous robot control becomes necessary. Autonomously controlled landers and instrumentation can be used in exploration, such as lunar and martian missions. However, wheeled robots have difficulty in exploring uneven terrain; thus, legged robots can be used in such situations. This research develops intelligent and adaptive control of mobile robots to perform functions such as environmental exploration in coordination and obstacle avoidance. The coordinated control is demonstrated in simulations.
The goal of this project was to detect the intensity of traffic on a road at different times of the day during daytime. Although the work presented utilized images from a section of a highway, the results of this project are intended for making decisions on the type of intervention necessary on any given road at different times for traffic control, such as installation of traffic signals, duration of red, green and yellow lights at intersections, and assignment of traffic control officers near school zones or other relevant locations. In this project, directional patterns are used to detect and count the number of cars in traffic images over a fixed area of the road to determine local traffic intensity. Directional patterns are chosen because they are simple and common to almost all moving vehicles. Perspective vision effects specific to each camera orientation has to be considered, as they affect the size and direction of patterns to be recognized. In this work, a simple and fast algorithm has been developed based on horizontal directional pattern matching and perspective vision adjustment. The results of the algorithm under various conditions are presented and compared in this paper. Using the developed algorithm, the traffic intensity can accurately be determined on clear days with average sized cars. The
accuracy is reduced on rainy days when the camera lens contains raindrops, when there are very long vehicles, such as trucks or tankers, in the view, and when there is very low light around dusk or dawn.
In digital security and authentication, watermarking has emerged as a solution to unauthorized digital copies, monitoring of broadcasts, information embedding, as well as end-user and transaction authentication. In the field of watermarking, the discrete cosine transform (DCT) domain, as well as other transform domains, have been shown to be advantageous over most spatial domain techniques by its increased robustness to image processing operations and possible distortions. In this research an adaptive watermarking scheme and its implementation are investigated in images in which the watermarks are embedded in the discrete cosine transform domain. The adaptive scheme has an advantage in that watermark strength can be adjusted according to image characteristics. In addition, the watermarked image degradation is also analyzed. Finally, the system's resistance to attacks is demonstrated.