Relevant bibliographies by topics / Visual recognition system

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Visual recognition system'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Visual recognition system"

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Laszlo, Sarah, and Elizabeth Sacchi. "Individual differences in involvement of the visual object recognition system during visual word recognition." Brain and Language 145-146 (June 2015): 42–52. http://dx.doi.org/10.1016/j.bandl.2015.03.009.

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Gornostal, Alexandr, and Yaroslaw Dorogyy. "Development of audio-visual speech recognition system." ScienceRise 12, no. 1 (December 30, 2017): 42–47. http://dx.doi.org/10.15587/2313-8416.2017.118212.

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Khosla, Deepak, David J. Huber, and Christopher Kanan. "A neuromorphic system for visual object recognition." Biologically Inspired Cognitive Architectures 8 (April 2014): 33–45. http://dx.doi.org/10.1016/j.bica.2014.02.001.

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ASAKURA, Toshiyuki, and Yasutomi IIDA. "Intelligent Visual Recognition System in Harvest Robot." Proceedings of the JSME annual meeting 2002.1 (2002): 195–96. http://dx.doi.org/10.1299/jsmemecjo.2002.1.0_195.

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Wong, Yee Wan, Kah Phooi Seng, and Li-Minn Ang. "Audio-Visual Recognition System in Compression Domain." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 5 (May 2011): 637–46. http://dx.doi.org/10.1109/tcsvt.2011.2129670.

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CAO, JIANGTAO, NAOYUKI KUBOTA, PING LI, and HONGHAI LIU. "THE VISUAL-AUDIO INTEGRATED RECOGNITION METHOD FOR USER AUTHENTICATION SYSTEM OF PARTNER ROBOTS." International Journal of Humanoid Robotics 08, no. 04 (December 2011): 691–705. http://dx.doi.org/10.1142/s0219843611002678.

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Some of noncontact biometric ways have been used for user authentication system of partner robots, such as visual-based recognition methods and speech recognition. However, the methods of visual-based recognition are sensitive to the light noise and speech recognition systems are perturbed to the acoustic environment and sound noise. Inspiring from the human's capability of compensating visual information (looking) with audio information (hearing), a visual-audio integrating method is proposed to deal with the disturbance of light noise and to improve the recognition accuracy. Combining with the PCA-based and 2DPCA-based face recognition, a two-stage speaker recognition algorithm is used to extract useful personal identity information from speech signals. With the statistic properties of visual background noise, the visual-audio integrating method is performed to draw the final decision. The proposed method is evaluated on a public visual-audio dataset VidTIMIT and a partner robot authentication system. The results verified the visual-audio integrating method can obtain satisfied recognition results with strong robustness.
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Malowany, Dan, and Hugo Guterman. "Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems." Algorithms 13, no. 7 (July 11, 2020): 167. http://dx.doi.org/10.3390/a13070167.

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Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to unprecedented sensitivity and precision in object detection and recognition tasks. Nevertheless, the findings in recent years on the sensitivity of neural networks to additive noise, light conditions, and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. CNNs are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state-of-the-art bottom-up object recognition models, e.g., deep CNNs. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state-of-the-art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model.
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Stork, David G. "Neural network acoustic and visual speech recognition system." Journal of the Acoustical Society of America 102, no. 3 (September 1997): 1282. http://dx.doi.org/10.1121/1.420021.

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Jiao, Chenlei, Binbin Lian, Zhe Wang, Yimin Song, and Tao Sun. "Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm." International Journal of Advanced Robotic Systems 17, no. 5 (September 1, 2020): 172988142094872. http://dx.doi.org/10.1177/1729881420948727.

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Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.
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Stringa, Luigi. "A VISUAL MODEL FOR PATTERN RECOGNITION." International Journal of Neural Systems 03, supp01 (January 1992): 31–39. http://dx.doi.org/10.1142/s0129065792000358.

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A general model for an optical recognition system capable of simultaneous recognition of patterns at different resolution levels is outlined. The model is based on two hierarchic stages of processing networks and presents interesting analogies with the human visual system. Illustrative applications and preliminary experimental results are also briefly discussed.
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Dissertations / Theses on the topic "Visual recognition system"

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Campbell, Larry W. "An intelligent tutor system for visual aircraft recognition." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/27723.

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Visual aircraft recognition (VACR) is a critical skill for U.S. Army Short Range Air Defense (SHORAD) soldiers. It is the most reliable means of identifying aircraft, however VACR skills are not easy to teach or learn, and once learned they are highly degradable. The numerous training aids that exist to help units train soldiers require qualified instructors who are not always available. Also, the varying degrees of proficiency among soldiers make group training less than ideal. In an attempt to alleviate the problems in most VASC training programs, an intelligent tutor system has been developed to teach VACR in accordance with the Wings, Engine, Fuselage, Tail (WEFT) cognitive model. The Aircraft Recognition Tutor is a graphics based, object oriented instructional program that teaches, reviews and tests VACR skills at a level appropriate to the student. The tutor adaptively coaches the student from the novice level, through the intermediate level, to the expert level. The tutor was provided to two U.S. Army Air Defense Battalions for testing and evaluation. The six month implementation, testing, and evaluation process demonstrated that, using existing technology in Computer Science and Artificial Intelligence, useful training tools could be developed quickly and inexpensively for deployment on existing computers in field.
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Dong, Junda. "Designing a Visual Front End in Audio-Visual Automatic Speech Recognition System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1382.

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Audio-visual automatic speech recognition (AVASR) is a speech recognition technique integrating audio and video signals as input. Traditional audio-only speech recognition system only uses acoustic information from an audio source. However the recognition performance degrades significantly in acoustically noisy environments. It has been shown that visual information also can be used to identify speech. To improve the speech recognition performance, audio-visual automatic speech recognition has been studied. In this paper, we focus on the design of the visual front end of an AVASR system, which mainly consists of face detection and lip localization. The front end is built upon the AVICAR database that was recorded in moving vehicles. Therefore, diverse lighting conditions and poor quality of imagery are the problems we must overcome. We first propose the use of the Viola-Jones face detection algorithm that can process images rapidly with high detection accuracy. When the algorithm is applied to the AVICAR database, we reach an accuracy of 89% face detection rate. By separately detecting and integrating the detection results from all different color channels, we further improve the detection accuracy to 95%. To reliably localize the lips, three algorithms are studied and compared: the Gabor filter algorithm, the lip enhancement algorithm, and the modified Viola-Jones algorithm for lip features. Finally, to increase detection rate, a modified Viola-Jones algorithm and lip enhancement algorithms are cascaded based on the results of three lip localization methods. Overall, the front end achieves an accuracy of 90% for lip localization.
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Wojnowski, Christine. "Reasoning with visual knowledge in an object recognition system /." Online version of thesis, 1990. http://hdl.handle.net/1850/10596.

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Sun, Yongbin Ph D. Massachusetts Institute of Technology. "An RFID-based visual recognition system for the retail industry." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104277.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 63-67).
In this thesis, I aim to build an accurate fine-grained retail product recognition system for improving customer in-store shopping experience. To achieve high accuracy, I developed a two-phase visual recognition scheme to identify the viewed retail product by verifying different types of visual features. The proposed scheme is robust enough to distinguish visually similar products in the tests. However, the computation cost of this scheme increases as the database scale becomes larger since it needs to verify all the products in the database. To improve the computation efficiency, my system integrates RFID as a second data source. By attaching an RFID tag to each product, the RFID reader is able to capture the identity information of surrounding products. The detection results can help reduce the verification scope from the whole database to the detected products only. Hence computation cost is saved. In the experiments, I first tested the recognition accuracy of my visual recognition scheme on a database containing visually similar products for different viewing angles, and my scheme achieved over 97.92% recognition accuracy for horizontal viewpoint variations of less than 30 degree. I then experimentally measured the computation cost of both the original system and the RFID-enhanced system. The computation cost is the processing time to recognize a target product. The RFID-enhanced system speeds up system performance dramatically when the scale of detected surrounding products is small.
by Yongbin Sun.
S.M.
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Koprnicky, Miroslav. "Towards a Versatile System for the Visual Recognition of Surface Defects." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/888.

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Automated visual inspection is an emerging multi-disciplinary field with many challenges; it combines different aspects of computer vision, pattern recognition, automation, and control systems. There does not exist a large body of work dedicated to the design of generalized visual inspection systems; that is, those that might easily be made applicable to different product types. This is an important oversight, in that many improvements in design and implementation times, as well as costs, might be realized with a system that could easily be made to function in different production environments.

This thesis proposes a framework for generalizing and automating the design of the defect classification stage of an automated visual inspection system. It involves using an expandable set of features which are optimized along with the classifier operating on them in order to adapt to the application at hand. The particular implementation explored involves optimizing the feature set in disjoint sets logically grouped by feature type to keep search spaces reasonable. Operator input is kept at a minimum throughout this customization process, since it is limited only to those cases in which the existing feature library cannot adequately delineate the classes at hand, at which time new features (or pools) may have to be introduced by an engineer with experience in the domain.

Two novel methods are put forward which fit well within this framework: cluster-space and hybrid-space classifiers. They are compared in a series of tests against both standard benchmark classifiers, as well as mean and majority vote multi-classifiers, on feature sets comprised of just the logical feature subsets, as well as the entire feature sets formed by their union. The proposed classifiers as well as the benchmarks are optimized with both a progressive combinatorial approach and with an genetic algorithm. Experimentation was performed on true colour industrial lumber defect images, as well as binary hand-written digits.

Based on the experiments conducted in this work, it was found that the sequentially optimized multi hybrid-space methods are capable of matching the performances of the benchmark classifiers on the lumber data, with the exception of the mean-rule multi-classifiers, which dominated most experiments by approximately 3% in classification accuracy. The genetic algorithm optimized hybrid-space multi-classifier achieved best performance however; an accuracy of 79. 2%.

The numeral dataset results were less promising; the proposed methods could not equal benchmark performance. This is probably because the numeral feature-sets were much more conducive to good class separation, with standard benchmark accuracies approaching 95% not uncommon. This indicates that the cluster-space transform inherent to the proposed methods appear to be most useful in highly dependant or confusing feature-spaces, a hypothesis supported by the outstanding performance of the single hybrid-space classifier in the difficult texture feature subspace: 42. 6% accuracy, a 6% increase over the best benchmark performance.

The generalized framework proposed appears promising, because classifier performance over feature sets formed by the union of independently optimized feature subsets regularly met and exceeded those classifiers operating on feature sets formed by the optimization of the feature set in its entirety. This finding corroborates earlier work with similar results [3, 9], and is an aspect of pattern recognition that should be examined further.
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Sjöholm, Alexander. "Closing the Loop : Mobile Visual Location Recognition." Thesis, Linköpings universitet, Datorseende, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112547.

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Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, but with recent advances in mobile performance visual SLAM is entering the consumer market in a completely new way. A visual SLAM system will however be sensitive to non cautious use that may result in severe motion, occlusion or poor surroundings in terms of visual features that will cause the system to temporarily fail. The procedure of recovering from such a fail is called relocalization. Together with two similar problems localization, to find your position in an existing SLAM session, and loop closing, the online reparation and perfection of the map in an active SLAM session, these can be grouped as visual location recognition (VLR). This thesis presents novel results by combining the scalability of FabMap and the precision of 13th Lab's tracking yielding high-precision VLR, +/- 10 cm, while maintaining above 99 % precision and 60 % recall for sessions containing thousands of images. Everything functional purely on a normal mobile phone. The applications of VLR are many. Indoors, where GPS is not functioning, VLR can still provide positional information and navigate you through big complexes like airports and museums. Outdoors, VLR can improve the precision of GPS tenfold yielding a new level of navigational experience. Virtual and augmented reality applications are other areas that benefit from improved positioning and localization.
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Su, Ying-fung. "Role of temporal texture in visual system exploration with computer simulations /." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B43703768.

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Kaplan, Bernhard. "Modeling prediction and pattern recognition in the early visual and olfactory systems." Doctoral thesis, KTH, Beräkningsbiologi, CB, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166127.

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Our senses are our mind's window to the outside world and determine how we perceive our environment.Sensory systems are complex multi-level systems that have to solve a multitude of tasks that allow us to understand our surroundings.However, questions on various levels and scales remain to be answered ranging from low-level neural responses to behavioral functions on the highest level.Modeling can connect different scales and contribute towards tackling these questions by giving insights into perceptual processes and interactions between processing stages.In this thesis, numerical simulations of spiking neural networks are used to deal with two essential functions that sensory systems have to solve: pattern recognition and prediction.The focus of this thesis lies on the question as to how neural network connectivity can be used in order to achieve these crucial functions.The guiding ideas of the models presented here are grounded in the probabilistic interpretation of neural signals, Hebbian learning principles and connectionist ideas.The main results are divided into four parts.The first part deals with the problem of pattern recognition in a multi-layer network inspired by the early mammalian olfactory system with biophysically detailed neural components.Learning based on Hebbian-Bayesian principles is used to organize the connectivity between and within areas and is demonstrated in behaviorally relevant tasks.Besides recognition of artificial odor patterns, phenomena like concentration invariance, noise robustness, pattern completion and pattern rivalry are investigated.It is demonstrated that learned recurrent cortical connections play a crucial role in achieving pattern recognition and completion.The second part is concerned with the prediction of moving stimuli in the visual system.The problem of motion-extrapolation is studied using different recurrent connectivity patterns.The main result shows that connectivity patterns taking the tuning properties of cells into account can be advantageous for solving the motion-extrapolation problem.The third part focuses on the predictive or anticipatory response to an approaching stimulus.Inspired by experimental observations, particle filtering and spiking neural network frameworks are used to address the question as to how stimulus information is transported within a motion sensitive network.In particular, the question if speed information is required to build up a trajectory dependent anticipatory response is studied by comparing different network connectivities.Our results suggest that in order to achieve a dependency of the anticipatory response to the trajectory length, a connectivity that uses both position and speed information seems necessary.The fourth part combines the self-organization ideas from the first part with motion perception as studied in the second and third parts.There, the learning principles used in the olfactory system model are applied to the problem of motion anticipation in visual perception.Similarly to the third part, different connectivities are studied with respect to their contribution to anticipate an approaching stimulus.The contribution of this thesis lies in the development and simulation of large-scale computational models of spiking neural networks solving prediction and pattern recognition tasks in biophysically plausible frameworks.

QC 20150504

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Adjei-Kumi, Theophilus. "The development of an intelligent system for visual simulation of construction projects." Thesis, University of Strathclyde, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311845.

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Su, Ying-fung, and 蘇盈峰. "Role of temporal texture in visual system: exploration with computer simulations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B43703768.

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Books on the topic "Visual recognition system"

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Bastian, Leibe, ed. Visual object recognition. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.

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Jinkins, J. Randy, and Claudia da Costa Leite. Neurodiagnostic imaging: Pattern analysis and differential diagnosis. Edited by Jinkins J. Randy and Leite Claudia da Costa. Philadelphia: Lippincott-Raven, 1998.

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A, Tsihrintzis George, ed. Visual affect recognition. Amsterdam: IOS Press, 2010.

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Liew, Alan Wee-Chung. Visual speech recognition: Lip segmentation and mapping. Edited by Wang Shilin. Hershey PA: Medical Information Science Reference, 2009.

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Visual analysis of behaviour: From pixels to semantics. New York: Springer-Verlag New York Inc, 2011.

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International Workshop on Visual Form (4th 2001 Capri, Italy). Visual form 2001: 4th International Workshop on Visual Form, IWVF4, Capri, Italy, May 28-30, 2001 : proceedings. Berlin: Springer, 2001.

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Wayne, Cranton, Fihn Mark, and SpringerLink (Online service), eds. Handbook of Visual Display Technology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Werner, Backhaus, ed. Neuronal coding of perceptual systems: Proceedings of the International School of Biophysics, Casamicciola, Napoli, Italy, 12-17 October 1998. New Jersey: World Scientific, 2001.

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Salah, Albert Ali. Human Behavior Understanding: First International Workshop, HBU 2010, Istanbul, Turkey, August 22, 2010. Proceedings. Berlin, Heidelberg: Springer-Verlag Heidelberg, 2010.

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Book chapters on the topic "Visual recognition system"

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Cyganek, Bogusław, and Sławomir Gruszczyński. "Visual System for Drivers’ Eye Recognition." In Lecture Notes in Computer Science, 436–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21219-2_55.

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Neven, Hartmut. "Designing a Comprehensive Visual Recognition System." In Lecture Notes in Computer Science, 3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11301-7_3.

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Batista, Jorge P. "A Real-Time Driver Visual Attention Monitoring System." In Pattern Recognition and Image Analysis, 200–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492429_25.

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Liu, Shaofeng, Yingchun Fan, Yuliang Tang, Xin Jing, Jintao Yao, and Hong Han. "Fuzzy Control Reversing System Based on Visual Information." In Pattern Recognition and Computer Vision, 247–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_21.

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Cerman, Martin, Gayane Shalunts, and Daniel Albertini. "A Mobile Recognition System for Analog Energy Meter Scanning." In Advances in Visual Computing, 247–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50835-1_23.

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Neo, H. F., C. C. Teo, and Andrew B. J. Teoh. "A Wavelet-Based Face Recognition System Using Partial Information." In Advances in Visual Computing, 427–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17277-9_44.

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Peng, Han, and Abolfazl Razi. "Fully Autonomous UAV-Based Action Recognition System Using Aerial Imagery." In Advances in Visual Computing, 276–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_22.

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Kolawole, Akintola, and Alireza Tavakkoli. "A Novel Gait Recognition System Based on Hidden Markov Models." In Advances in Visual Computing, 125–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33191-6_13.

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Nseaf, Asama Kuder, Azizah Jaafar, Haroon Rashid, Riza Sulaiman, and Rahmita Wirza O. K. Rahmat. "Design Method of Video Based Iris Recognition System (V-IRS)." In Advances in Visual Informatics, 526–38. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02958-0_48.

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Dias, André, Jose Almeida, Alfredo Martins, and Eduardo Silva. "Real-Time Visual Ground-Truth System for Indoor Robotic Applications." In Pattern Recognition and Image Analysis, 304–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_36.

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Conference papers on the topic "Visual recognition system"

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Hsieh, Hsiang-Yu, Nanming Chen, and Ching-Lung Liao. "Visual Recognition System of Elastic Rail Clips for Mass Rapid Transit Systems." In ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/jrc/ice2007-40080.

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In recent years, the railway transportation system has become one of the main means of transportation. Therefore, driving safety is of great importance. However, because of the potential of multiple breaks of elastic rail clips in a fixed rail, accidents may occur when a train passes through the track. This paper presents the development of a computer visual recognition system which can detect the status of elastic rail clips. This visual recognition system can be used in mass rapid transit systems to reduce the substantial need of manpower for checking elastic rail clips at present. The visual recognition system under current development includes five components: preprocessing, identification of rail position, search of elastic rail clip regions, selection of the elastic rail clip, and recognition of the elastic rail clip. The preprocessing system transforms the colored images into grey-level images and eliminates noises. The identification of rail position system uses characteristics of the grey-level variation and confirms the rail position. The search system uses wavelet transformation to carry out the search of elastic rail clip regions. The selection system finds a suitable threshold, using techniques from morphological processing, object search and image processing. The recognition system processes characteristics and structures of elastic rail clips. Experimental testing shows the ability of the developed system to recognize both normal elastic rail clip images and broken elastic rail clip images. This result confirms the feasibility in developing such a visual recognition system.
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Fukushima, Kazue, Harumi Kawamura, Makoto Kosugi, and Noburu Sonehara. "Personal system for practical face recognition." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233238.

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Tian, X., H. Deng, K. Yamazaki, M. Fujishima, and M. Mori. "On-Machine Visual Modeling System With Object Recognition." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81661.

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This paper presents an on-machine modeling system that tries to bridge the gap between the design and the machining. This system is able to build a comprehensive solid model of the CNC machining workspace after the workpiece and fixtures have been installed onto the working table. This solid model can be used for simulation to enhance its credibility. For this purpose, one prototype of a 3D visual modeling system is proposed and designed. In order to accurately calibrate CCD cameras upon the absolute coordinate frame of the machining center, a practical calibration method is presented at first. To segment the target part and extract its 2D features on the captured images, the techniques of Image Decomposition and a modified Standard Hough Transform (SHT) are designed. Using these 2D features, the 3D visual stereovision system, powered by a designed feature matching engine, is capable of obtaining the 3D features of the target part. Furthermore, the part has been identified by the object recognition technology. This recognition includes part recognition and pose recognition. In the part recognition, the part is recognized and an initial pose transform is obtained. Using this initial pose transform, the pose optimization method, named as Dual Iterative Closest Lines (DICL), is designed to locate the optimum position and orientation of the solid model of the recognized part. Finally, this modeling system is tested on a machining center. The experimental result indicates the innovation and feasibility of the proposed modeling system.
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Frisky, Aufaclav Zatu Kusuma, Chien-Yao Wang, Andri Santoso, and Jia-Ching Wang. "Lip-based visual speech recognition system." In 2015 International Carnahan Conference on Security Technology (ICCST). IEEE, 2015. http://dx.doi.org/10.1109/ccst.2015.7389703.

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Wu, Shen, Feng Jiang, Debin Zhao, Shaohui Liu, and Wen Gao. "Viewpoint-independent hand gesture recognition system." In 2012 Visual Communications and Image Processing (VCIP). IEEE, 2012. http://dx.doi.org/10.1109/vcip.2012.6410809.

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Nguyen, Mao, and Minh-Triet Tran. "Toward a practical visual object recognition system." In the Fourth Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2542050.2542077.

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Kim, Min-Uk, and Kyoungro Yoon. "Image Recognition System That Uses Visual Word." In 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847410.

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Shdaifat, I., and R. R. Grigat. "A system for audio-visual speech recognition." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-367.

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Kobayashi, Seiji. "Optical gesture recognition system." In ACM SIGGRAPH 97 Visual Proceedings: The art and interdisciplinary programs of SIGGRAPH '97. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/259081.259206.

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Wu, Yuefeng, Nong Sang, Wei Lin, and Yuanjie Shao. "Joint image restoration and location in visual navigation system." In Automatic Target Recognition and Navigation, edited by Jayaram K. Udupa, Hanyu Hong, and Jianguo Liu. SPIE, 2018. http://dx.doi.org/10.1117/12.2284978.

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Reports on the topic "Visual recognition system"

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Abstract:
Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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Bajcsy, Ruzena. A Query Driven Computer Vision System: A Paradigm for Hierarchical Control Strategies during the Recognition Process of Three-Dimensional Visually Perceived Objects. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada185507.

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