This work addresses the issue of Terrain Classification that can be applied for path planning for an Unmanned Ground Vehicle (UGV) platform. We are interested in classification of features such as rocks, bushes, trees and dirt roads. Currently, the data is acquired from a color camera mounted on the UGV as we can add range data from a second sensor in the future. The classification is accomplished by first, coarse segmenting a frame and then refining the initial segmentations through a convenient user interface. After the first frame, temporal information is exploited to improve the quality of the image segmentation and help classification adapt to changes due to ambient lighting, shadows, and scene changes as the platform moves. The Mean Shift Classifier algorithm provides segmentation of the current frame data. We have tested the above algorithms with four sequence of frames acquired in an environment with terrain representative of the type we expect to see in the field. A comparison of the results from this algorithm was done with accurate manually-segmented (ground-truth) data, for each frame in the sequence.
This system provides real-time guidance for training and problem-solving on production-line machinery. A prototype of a wearable, real-time, video guidance, interactive system for use in manufacturing, has been developed and demonstrated. Anticipated benefits are: relatively inexperienced personnel can provide machine servicing and the dependency on the vendor to repair or maintain equipment is significantly reduced. Additionally, servicing, training or part change-over schedules can be exercised more predictably and with less training. This approach utilizes Head Worn Display or Head Mounted Display (HMD) technology that can be readily adapted for various machines on the factory floor with training steps for a new location. Such a system can support various applications in manufacturing such as direct video guiding or applying scheduled maintenance and training to effectively resolve servicing emergencies and reduce machine downtime. It can also provide training of inexperienced operators and maintenance personnel. The gap between production line complexity and ability of production personnel to effectively maintain equipment is expected to widen in the future and advanced equipment will require complex servicing procedures that are neither well documented nor user-friendly. This system offers benefits in increased manufacturing equipment availability by facilitating effective servicing and training and can interface to a server system for additional computational resources on an as-needed basis. This system utilizes markers to guide the user and enforces a well defined sequence of operations. It performs augmentation of information on the display in order to provide guidance in real-time.
We present a system for intelligent machine fault detection and analysis. This system examines the signals in real-time, determines the quality of the signature for the entire set of signals and evaluates the error states of these signal combinations or signatures. This approach of continually evaluating quality of signals allows for predictive maintenance of the manufacturing system. The signals from the manufacturing system are obtained in a standard, optically isolated interface, the signals into this Remote Observation Manufacturing Equipment (ROME) system is processed and evaluated in real-time and history of these signals is stored. This system allows for the monitoring of signals in a continuous manner and these signals are recorded till a fault occurs. The graphical user interface provides user visualization control of the full family of signals at various time instants. These analog and digital signals are synchronized with the color images from two cameras and can be viewed with this GUI. The user can review both error and normal condition state using this interface.
This work presents methods for terrain classification that support adaptive selection of parameters for Terrain Classification system. Work is also presented for water body detection and we present results from experiments conducted for water detection methods utilizing LADAR, color camera and polarization filter based sensors. Use of multiple sensors can provide better water detection capability. An approach for adaptive terrain classification is shown
for existing rule-based classification algorithms. This approach allows us to develop a set of rules for various representative terrain types from various sites and operating conditions (light level, humidity, season, etc.) and exploit the onboard vehicle situational knowledge to select the most suitable set of rules for operation. An important element of this work requires use of data collected for different seasons and locations or terrain types in order to provide sensitivity measures. Existing terrain classification algorithms can utilize input from multiple sensors such as: Color, LADAR, FLIR and Multi-Spectral imagery. The performance of these algorithms is expected to improve as we acquire
an increasing number of additional data sets that includes features of interest taken under various conditions of terrain-types types,
illumination, temperature, humidity etc. and allow us to build a database of terrain knowledge. Environmental nformation and ground-truth is also collected along with the sensor data data. A Geographical Information System (GIS) interface is utilized along with related public-domain tools. Such tools are integrated to our system and used to provide data-management, spatial-modeling, and visualization.
In this paper, a novel multi-level adaptive lines of communication extraction method for multispectral images is presented. The method takes into account both spectral and spatial characteristics of the data on different levels of processing. The principal background classes are obtained first using K-means clustering. Each pixel is examined next for classification using a minimum distance classifier with principal class signatures obtained in the previous level. In the next level, the neighborhood of each unclassified pixel is analyzed for inclusion of candidate classes for use as endmembers in a spectral unmixing model. If the list of candidate background classes is empty, the conditions for their inclusion are relaxed. The fractions of backgrounds and lines of communication signatures for the unclassified pixels are determined by means of linear least-squares method. If the results of unmixing are not satisfactory, the candidate clusters list is renewed, and unmixing is repeated. The lines of communication detection within each pixel is performed next. The line segments detection parameters are initialized, directional confidence is calculated, and line segment tracking is initialized. The line segments are incremented until the composite confidence becomes too low. At the end, segment connection, and lines of communications identification is performed. The proposed method was successfully applied to both synthetic and AVIRIS hyperspectral data sets.
Coronary arteriography is a technique used for evaluating the state of the coronary arteries. Matching of coronary arteries from multiple views is necessary for obtaining a 3-D description of the arterial tree. Overlapping vessels and artifacts due to digital subtraction of the angiogram background make the matching process quite difficult. The simplex method applied for linear programming and a relaxation technique for pre-processing the data are applied to skeletons from two views in order to obtain a matching of branches between views. The elements of the centerline along the branch are modeled as a Markov random field and a matching of each element in the two views is obtained by minimizing the energy of the matching contour. The element matching is treated as an estimation problem such that the a- posteriori probability is maximized. Results are provided for the 3-D reconstruction using these algorithms for automatic correspondence, and compared to those obtained by manual correspondence specification. This work was performed using a pig-cast realistic phantom. The results are encouraging.
Breast cancer is the leading cause of death among women. Breast cancer can be detected earlier by mammography than any other non-invasive examination. About 30% to 50% of breast cancers demonstrate tiny granulelike deposits of calcium called microcalcifications. It is difficult to distinguish between benign and malignant cases based on an examination of calcification regions, especially in hard-to-diagnose cases. We investigate the potential of using energy and entropy features computed from wavelet packets for their correlation with malignancy. Two types of Daubechies discrete filters were used as prototype wavelets. The energy and entropy features were computed for 128 benign and 63 malignant cases and analyzed using a multivariate cluster analysis and a univariate statistical analysis to reduce the feature set to a `five best set of features.' The efficacy of the reduced feature set to discriminate between the malignant and benign categories was evaluated using different multilayer perceptron architectures. The multilayer perceptron was trained using the backpropagation algorithm for various training and test set sizes. For each case 40 partitions of the data set were used to set up the training and test sets. The performance of the features was evaluated by computing the best area under the relative operating characteristic (ROC) curve and the average area under the ROC curve. The performance of the features computed from the wavelet packets was compared to a second set of features consisting of the wavelet packet features, image structure features and cluster features. The classification results are encouraging and indicate the potential of using features derived from wavelet packets in discriminating microcalcification regions into benign and malignant categories.
The detection after surgery of residual tumor from magnetic resonance (MR) images is difficult due to the low contrast level of the images. Gadolinium-enhanced MR imaging has been found valuable in detecting residual enhancing tumor when performed within 72 hours after surgery. The patient is scanned by the MR scanner with and without infusion of gadolinium, a contrast agent. Usually, the estimation of post-operative tumor volume is done by visual comparison of the T1 MR images obtained with and without gadolinium infusion. The T1 MR images, in most cases, without contrast demonstrates areas of hyper intensities (high brightness levels), consistent with hemorrhage. These hyper intense areas often make it difficult to detect residual tumor in post contrast images. This is due to the presence of both acute hemorrhage and gadolinium enhancement which have high brightness levels in T1 MR images. Even in MR images taken within 72 hours after surgery, detection of tumor enhancement in areas of increased T1 signal produced by blood products or by postoperative changes can be difficult when performed by the naked eye. Due to these problems, the quantification of residual tumor becomes a subjective issue among neuro-radiologists. Thus to reduce errors produced by the human factor, an automated procedure to detect residual tumor is required. We have developed a technique to differentiate tumor enhancement from postoperative changes and blood products on MR imaging. The technique involves fusion of pre- and post-gadolinium MR images performed in the immediate postoperative period. Computerized slice based substraction is then done on the corresponding fused images of the two sets. The subtraction process results in a composite slice, which is examined for differences between pre- and post-gadolinium studies. The presented technique was tested on 14 cases in which MR images were obtained from brain tumor patients within 72 hours after surgery. The subtraction technique easily distinguished residual enhancing tumor from postoperative surgical changes and was simple to perform. The technique proposed and developed has given good results and will be used in clinical trial and diagnosis. Future potentials of the technique are discussed and illustrative cases presented.
Coronary arteriography is a technique used for evaluating the state of coronary arteries and assessing the need for bypass surgery and angioplasty. The present clinical application of this technology is based on the use of a contrast medium for manual radiographic visualization. This method is inaccurate due to varying interpretation of the visual results. Coronary arteriography based quantitations are impractical in a clinical setting without the use of automatic techniques applied to the 3-D reconstruction of the arterial tree. Such a system will provide an easily reproducible method for following the temporal changes in coronary morphology. The labeling of the arteries and establishing of the correspondence between multiple views is necessary for all subsequent processing required for 3-D reconstruction. This work represents a rule based expert system utilized for automatic labeling and segmentation of the arterial branches across multiple views. X-ray data of two and three views of human subjects and a pig arterial cast have been used for this research.
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