Mars Rover imaging systems and directional filtering

Computer literature searches were carried out at Duke University and NASA Langley Research Center. The purpose is to enhance personal knowledge based on the technical problems of pattern recognition and image understanding which must be solved for the Mars Rover and Sample Return Mission. Intensive study effort of a large collection of relevant literature resulted in a compilation of all important documents in one place. Furthermore, the documents are being classified into: Mars Rover; computer vision (theory); imaging systems; pattern recognition methodologies; and other smart techniques (AI, neural networks, fuzzy logic, etc)

Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras

In this paper we propose a fuzzy method to detect free paths in real-time using digital stereo images. It is based on looking for linear variations of depth in disparity maps, which are obtained by processing a pair of rectified images from two stereo cameras. By applying least-squares fitting over groups of disparity maps columns to a linear model, free paths are detected by giving a certainty using a fuzzy rule. Experimental results on real outdoor images are also presented.Nuria Ortigosa acknowledges the support of Universidad Polit'ecnica de Valencia under grant FPI-UPV 2008. Samuel Morillas acknowledges the support of Spanish Ministry of Education and Science under grant MTM 2009-12872-C02-01.Ortigosa Araque, N.; Morillas Gómez, S.; Peris Fajarnes, G.; Dunai Dunai, L. (2012). Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 20(2):245-259. doi:10.1142/S0218488512500122S245259202Grosso, E., & Tistarelli, M. (1995). Active/dynamic stereo vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(9), 868-879. doi:10.1109/34.406652Wedel, A., Badino, H., Rabe, C., Loose, H., Franke, U., & Cremers, D. (2009). B-Spline Modeling of Road Surfaces With an Application to Free-Space Estimation. IEEE Transactions on Intelligent Transportation Systems, 10(4), 572-583. doi:10.1109/tits.2009.2027223Bloch, I. (2005). Fuzzy spatial relationships for image processing and interpretation: a review. Image and Vision Computing, 23(2), 89-110. doi:10.1016/j.imavis.2004.06.013Keller, J. M., & Wang, X. (2000). A Fuzzy Rule-Based Approach to Scene Description Involving Spatial Relationships. Computer Vision and Image Understanding, 80(1), 21-41. doi:10.1006/cviu.2000.0872Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., & López, M. T. (2010). Video sequence motion tracking by fuzzification techniques. Applied Soft Computing, 10(1), 318-331. doi:10.1016/j.asoc.2009.08.002Morillas, S., Gregori, V., & Hervas, A. (2009). Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise From Color Images. IEEE Transactions on Image Processing, 18(7), 1452-1466. doi:10.1109/tip.2009.2019305Poloni, M., Ulivi, G., & Vendittelli, M. (1995). Fuzzy logic and autonomous vehicles: Experiments in ultrasonic vision. Fuzzy Sets and Systems, 69(1), 15-27. doi:10.1016/0165-0114(94)00237-2Alonso, J. M., Magdalena, L., Guillaume, S., Sotelo, M. A., Bergasa, L. M., Ocaña, M., & Flores, R. (2007). Knowledge-based Intelligent Diagnosis of Ground Robot Collision with Non Detectable Obstacles. Journal of Intelligent and Robotic Systems, 48(4), 539-566. doi:10.1007/s10846-006-9125-6McFetridge, L., & Ibrahim, M. Y. (2009). A new methodology of mobile robot navigation: The agoraphilic algorithm. Robotics and Computer-Integrated Manufacturing, 25(3), 545-551. doi:10.1016/j.rcim.2008.01.008Sun, H., & Yang, J. (2001). Obstacle detection for mobile vehicle using neural network and fuzzy logic. Neural Network and Distributed Processing. doi:10.1117/12.441696Ortigosa, N., Morillas, S., & Peris-Fajarnés, G. (2010). Obstacle-Free Pathway Detection by Means of Depth Maps. Journal of Intelligent & Robotic Systems, 63(1), 115-129. doi:10.1007/s10846-010-9498-4Picton, P. D., & Capp, M. D. (2008). Relaying scene information to the blind via sound using cartoon depth maps. Image and Vision Computing, 26(4), 570-577. doi:10.1016/j.imavis.2007.07.005Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334. doi:10.1109/34.888718Scharstein, D., & Szeliski, R. (2002). International Journal of Computer Vision, 47(1/3), 7-42. doi:10.1023/a:1014573219977Felzenszwalb, P. F., & Huttenlocher, D. P. (2006). Efficient Belief Propagation for Early Vision. International Journal of Computer Vision, 70(1), 41-54. doi:10.1007/s11263-006-7899-4Qingxiong Yang, Liang Wang, Ruigang Yang, Stewenius, H., & Nister, D. (2009). Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), 492-504. doi:10.1109/tpami.2008.99Zitnick, C. L., & Kang, S. B. (2007). Stereo for Image-Based Rendering using Image Over-Segmentation. International Journal of Computer Vision, 75(1), 49-65. doi:10.1007/s11263-006-0018-8Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. doi:10.1017/cbo9780511811685Lee, C. C. (1990). Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404-418. doi:10.1109/21.52551C. Fodor, J. (1993). A new look at fuzzy connectives. Fuzzy Sets and Systems, 57(2), 141-148. doi:10.1016/0165-0114(93)90153-9Nalpantidis, L., & Gasteratos, A. (2010). Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image and Vision Computing, 28(6), 940-951. doi:10.1016/j.imavis.2009.11.011BOHANNON, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and Ageing, 26(1), 15-19. doi:10.1093/ageing/26.1.1

Intelligent systems for welding process automation

This paper presents and evaluates the concept and implementation of two distinct multi-sensor systems for the automated manufacturing based on parallel hardware. In the most sophisticated implementation, 12 processors had been integrated in a parallel multi-sensor system. Some specialized nodes implement an Artificial Neural Network, used to improve photogrammetry-based computer vision, and Fuzzy Logic supervision of the sensor fusion. Trough the implementation of distributed and intelligent processing units, it was shown that parallel architectures can provide significant advantages compared to conventional bus-based systems. The paper concludes with the comparison of the main aspects of the transputer and the DSP-based implementation of sensor guided robots

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal − more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657