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NASA NTRS2007

Automated Recognition of 3D Features in GPIR Images

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Park, Han↗

NASA NTRS2011

Automatic Facial Expression Recognition and Operator Functional State

The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions.

Blanson, Nina↗

NASA NTRS2012

Shape and Color Features for Object Recognition Search

A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for object recognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the object recognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired object recognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future.

Duong, Tuan A.↗

NASA NTRS2012

Automatic Facial Expression Recognition and Operator Functional State

Blanson, Nina↗

DOE OSTI2017

Molecular insights into the specific recognition between the RNA binding domain qRRM2 of hnRNP F and G-tract RNA: A molecular dynamics study

Heterogeneous nuclear ribonucleoprotein F (hnRNP F) controls the expression of various genes through regulating the alternative splicing of pre-mRNAs in the nucleus. It uses three quasi-RNA recognition motifs (qRRMs) to recognize G-tract RNA which contains at least three consecutive guanines. The structures containing qRRMs of hnRNP F in complex with G-tract RNA have been determined by nuclear magnetic resonance (NMR) spectroscopy, shedding light on the recognition mechanism of qRRMs with G-tract RNA. However, knowledge of the recognition details is still lacking. To investigate how qRRMs specifically bind with G-tract RNA and how the mutations of any guanine to an adenine in the G-tract affect the binding, molecular dynamics simulations with binding free energy analysis were performed based on the NMR structure of qRRM2 in complex with G-tract RNA. Simulation results demonstrate that qRRM2 binds strongly with G-tract RNA, but any mutation of the G-tract leads to a drastic reduction of the binding free energy. Further comparisons of the energetic components reveal that van der Waals and non-polar interactions play essential roles in the binding between qRRM2 and G-tract RNA, but the interactions are weakened by the effect of RNA mutations. Structural and dynamical analyses indicate that when qRRM2 binds with G-tract RNA, both qRRM2 and G-tract maintain stabilized structures and dynamics; however, the stability is disrupted by the mutations of the G-tract. These results provide novel insights into the recognition mechanism of qRRM2 with G-tract RNA that are not elucidated by the NMR technique. - Highlights: • qRRM2 has greater binding free energy with G-tract RNA than the mutated RNAs. • Non-polar and van der Waals interactions contribute mainly to the binding free energy. • qRRM2 maintains stabilized structure and dynamics when binding with G-tract RNA. • G-tract adopts stabilized conformation in complex with qRRM2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Insect taste receptors relevant to host identification by recognition of secondary metabolite patterns of non-host plants

Highlights: • Three putative bitter gustatory receptor of silkworm respond to several secondary metabolites in non-host plants. • Secondary metabolites responsive to the gustatory receptor are representative feeding-deterrents in non-host plants. • One feeding-deterrent gustatory receptor respond to multiple feeding deterrents. • Feeding deterrent receptors are expressed in silkworm mouthparts. • Representative secondary metabolite of a non-host plant, coumarin exactly had a suppressive effect on food intake of silkworm. The taste sensing system is crucial for food recognition in insects and other animals. It is commonly believed that insect gustatory receptors (Grs) expressed in gustatory organs are indispensable for host plant selection. Many behavioral studies have shown that mono- or oligo-phagous lepidopteran insects use the balance between feeding attractants and feeding deterrents in host plants and that these are sensed by taste organs for host plant recognition. However, the molecular mechanism underlying taste recognition, especially of feeding deterrents, remains to be elucidated. To better understand this mechanism, we studied orphan Grs, including Bombyx mori Gr (BmGr) 16, BmGr18, and BmGr53, from the mono-phagous insect, Bombyx mori. Using Calcium imaging in mammalian cells, we first confirmed in lepidoptera insects that three of the putative bitter Grs widely responded to structurally different feeding deterrents. Although the phylogenetic distance of these Grs was considerable, they responded to partially overlapping deterrents of plant secondary metabolites. These findings suggest that not only these three Grs but also most of the Grs that have been assigned to putative bitter Grs are feeding-deterrent receptors that play a role in host plant recognition.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Structural analysis of substrate recognition by glucose isomerase in Mn2+ binding mode at M2 site in S. rubiginosus

Highlights: • A snapshot of metal binding at the SruGI M2 site in the presence of Mn{sup 2+}. • Metal binding at the M2 site elicits a configuration change at the M1 site. • Glucose molecule can only bind to the M1 site in presence of Mn{sup 2+} at the M2 site. • Two-step sequential process for SruGI substrate recognition, in Mn{sup 2+} at M2 site mode. Glucose isomerase (GI) catalyzes the reversible enzymatic isomerization of d-glucose and d-xylose to d-fructose and d-xylulose, respectively. This is one of the most important enzymes in the production of high-fructose corn syrup (HFCS) and biofuel. We recently determined the crystal structure of GI from S. rubiginosus (SruGI) complexed with a xylitol inhibitor in one metal binding mode. Although we assessed inhibitor binding at the M1 site, the metal binding at the M2 site and the substrate recognition mechanism for SruGI remains the unclear. Here, we report the crystal structure of the two metal binding modes of SruGI and its complex with glucose. This study provides a snapshot of metal binding at the SruGI M2 site in the presence of Mn{sup 2+}, but not in the presence of Mg{sup 2+}. Metal binding at the M2 site elicits a configuration change at the M1 site. Glucose molecule can only bind to the M1 site in presence of Mn{sup 2+} at the M2 site. Glucose and Mn{sup 2+} at the M2 site were bridged by water molecules using a hydrogen bonding network. The metal binding geometry of the M2 site indicates a distorted octahedral coordination with an angle of 55–110°, whereas the M1 site has a relatively stable octahedral coordination with an angle of 85–95°. We suggest a two-step sequential process for SruGI substrate recognition, in Mn{sup 2+} binding mode, at the M2 site. Our results provide a better understanding of the molecular role of the M2 site in GI substrate recognition.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Structural basis of glycan276-dependent recognition by HIV-1 broadly neutralizing antibodies

Recognition of N-linked glycan at residue N276 (glycan276) at the periphery of the CD4-binding site (CD4bs) on the HIV-envelope trimer is a formidable challenge for many CD4bs-directed antibodies. To understand how this glycan can be recognized, here we isolate two lineages of glycan276-dependent CD4bs antibodies. Antibody CH540-VRC40.01 (named for donor-lineage.clone) neutralizes 81% of a panel of 208 diverse strains, while antibody CH314-VRC33.01 neutralizes 45%. Cryo-electron microscopy (cryo-EM) structures of these two antibodies and 179NC75, a previously identified glycan276-dependent CD4bs antibody, in complex with HIV-envelope trimer reveal substantially different modes of glycan276 recognition. Despite these differences, binding of glycan276-dependent antibodies maintains a glycan276 conformation similar to that observed in the absence of glycan276-binding antibodies. By contrast, glycan276-independent CD4bs antibodies, such as VRC01, displace glycan276 upon binding. These results provide a foundation for understanding antibody recognition of glycan276 and suggest its presence may be crucial for priming immunogens seeking to initiate broad CD4bs recognition.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

When your cap matters: structural insights into self vs non-self recognition of 5' RNA by immunomodulatory host proteins

Cytosolic recognition of viral RNA is important for host innate immune responses. Differential recognition of self vs non-self RNA is a considerable challenge as the inability to differentiate may trigger aberrant immune responses. Recent work identified the composition of the RNA 5', including the 5' cap and its methylation state, as an important determinant of recognition by the host. Furthermore, recent studies have advanced our understanding of the modified 5' RNA recognition and viral antagonism of RNA receptors. Here, we will discuss RIG-I and IFIT proteins as examples of host proteins that detect dsRNA and ssRNA, respectively.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Effects of Postmortem Decomposition on Face Recognition

Although viable tools are available for the identification of unknown deceased individuals, recognition rates with these methods are greatly impacted by the degree to which decomposition has occurred. Therefore, identifying highly decomposed remains poses a major challenge. This paper analyzes the effect of facial decomposition on the recognition rates of several facial recognition commercial-off-theshelf systems and research-grade systems, as well as algorithms contained in a custom recognition library. The custom dataset of facial images used in the experiment is composed of 42 subjects at stages of decomposition rangingfrom recently deceased to later stages where the soft tissues are severely decomposed and facial features are deformed. It is shown that an algorithm’s ability to correctly detect a decomposing face is a crucial first step that not all face models can accurately handle. However, some of the evaluated Convolution Neural Network (CNN)–inspired methods provide promising results even in cases of severely decomposed faces.

Cornett, David↗

DOE OSTI2020

Molecular basis of P[II] major human rotavirus VP8* domain recognition of histo-blood group antigens

Initial cell attachment of rotavirus (RV) to specific cell surface glycan receptors, which is the essential first step in RV infection, is mediated by the VP8* domain of the spike protein VP4. Recently, human histo-blood group antigens (HBGAs) have been identified as receptors or attachment factors for human RV strains. RV strains in the P[4] and P[8] genotypes of the P[II] genogroup share common recognition of the Lewis b (Le b ) and H type 1 antigens, however, the molecular basis of receptor recognition by the major human P[8] RVs remains unknown due to lack of experimental structural information. Here, we used nuclear magnetic resonance (NMR) spectroscopy-based titration experiments and NMR-derived high ambiguity driven docking (HADDOCK) methods to elucidate the molecular basis for P[8] VP8* recognition of the Leb (LNDFH I) and type 1 HBGAs. We also used X-ray crystallography to determine the molecular details underlying P[6] recognition of H type 1 HBGAs. Unlike P[6]/P[19] VP8*s that recognize H type 1 HBGAs in a binding surface composed of an α-helix and a β-sheet, referred as the “βα binding site”, the P[8] and P[4] VP8*s bind Le b HBGAs in a previously undescribed pocket formed by the edges of two β-sheets, referred to as the “ββ binding site”. Importantly, the P[8] and P[4] VP8*s retain binding capability to non-Le b type 1 HBGAs using the βα binding site. The presence of two distinct binding sites for Le b and non-Le b HBGA glycans in the P[8] and P[4] VP8* domains suggests host-pathogen co-evolution under structural and functional adaptation of RV pathogens to host glycan polymorphisms. Assessment and understanding of the precise impact of this co-evolutionary process in determining RV host ranges and cross-species RV transmission should facilitate improved RV vaccine development and prediction of future RV strain emergence and epidemics.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1972

Individual recognition between mother and infant bats (Myotis)

The recognition process and the basis for that recognition, in brown bats, between mother and infant are analyzed. Two parameters, ultrasonic communication and olfactory stimuli, are investigated. The test animals were not allowed any visual contact. It was concluded that individual recognition between mother and infant occurred. However, it could not be determined if the recognition was based on ultrasonic signals or olfactory stimuli.

Turner, D.↗

NASA NTRS1972

Layered 'recognition cone' networks that pre-process, classify, and describe.

Discussion of pattern recognition programs for input data preprocessing with simultaneous or subsequent characterization, or characterization into a 'recognition cone,' or description and naming, interrelated descriptions, and conversion. A computer program is described that transforms and characterizes the input through the successive layers of a recognition cone. The program can choose and put forth names of parts of the input scene. It combines pieces of a description into interrelated wholes by using n-tuple characterizers and conducts a simple and stylized conversation about what it has seen. The technique of combining recognition cones with preprocessing transformations and characterizations is expected to contribute to technology in this field.

Uhr, L.↗

NASA NTRS1973

Terrain type recognition using ERTS-1 MSS images

For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

Gramenopoulos, N.↗

NASA NTRS1975

Studies of recognition with multitemporal remote sensor data

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data.

Malila, W. A.↗

NASA NTRS1985

Pattern recognition in the satellite temperature retrieval problem

Pattern recognition procedures have been developed in order to improve the first-guess fields for satellite temperature retrievals. The first procedure is used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. The second procedure is used to organize a priori data into shape-coherent pattern libraries using structural information inherent in the data itself. On the basis of independent tests of about 800 temperature retrievals, it was found that: (1) the pattern recognition techniques reduced first-guess profile errors by nearly 50 percent in comparison with traditional partitioning schemes; and (2) with regression and physical-iterative retrieval algorithms, however, the effect of pattern recognition on temperature retrieval error was insignificant. Analysis of individual retrieval errors showed that poor retrievals may outweigh the potential benefits of both pattern recognition techniques.

Thompson, O. E.↗

NASA NTRS1988

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Juday, Richard D.↗

NASA NTRS1989

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Tescher, Andrew G.↗