Contreras et al., 2016 - Google Patents
Using deep learning for exploration and recognition of objects based on imagesContreras et al., 2016
- Document ID
- 2497490932188243308
- Author
- Contreras S
- De La Rosa F
- Publication year
- Publication venue
- 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR)
External Links
Snippet
Deep Learning is a machine learning technique that seeks to define neural networks based on pattern recognition from input data, and to achieve results with a high level of confidence and time efficiency. In this paper, this technique is used in two processes that aim to improve …
- 230000001537 neural 0 abstract description 44
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Pfeiffer et al. | From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots | |
EP3405845B1 (en) | Object-focused active three-dimensional reconstruction | |
US20180079076A1 (en) | Machine learning device, robot system, and machine learning method for learning operation program of robot | |
Contreras et al. | Using deep learning for exploration and recognition of objects based on images | |
CN112347923A (en) | Roadside end pedestrian track prediction algorithm based on confrontation generation network | |
CN114708435B (en) | Obstacle size prediction and uncertainty analysis method based on semantic segmentation | |
Koutras et al. | Autonomous and cooperative design of the monitor positions for a team of uavs to maximize the quantity and quality of detected objects | |
Yang et al. | Monocular camera and single-beam sonar-based underwater collision-free navigation with domain randomization | |
Rezaei et al. | Mobile robot monocular vision-based obstacle avoidance algorithm using a deep neural network | |
Elfwing et al. | Scaled free-energy based reinforcement learning for robust and efficient learning in high-dimensional state spaces | |
Agarwal et al. | Predicting the future motion of divers for enhanced underwater human-robot collaboration | |
Fu et al. | Mimicking fly motion tracking and fixation behaviors with a hybrid visual neural network | |
Nikdel et al. | Recognizing and tracking high-level, human-meaningful navigation features of occupancy grid maps | |
US11467592B2 (en) | Route determination method | |
Petrović et al. | Efficient machine learning of mobile robotic systems based on convolutional neural networks | |
Pajaziti et al. | Path Control of Quadruped Robot through Convolutional Neural Networks | |
Benjamin et al. | A cognitive approach to vision for a mobile robot | |
Luo | Multi-sensor based strategy learning with deep reinforcement learning for unmanned ground vehicle | |
Zeng et al. | Obstacle avoidance through incremental learning with attention selection | |
Chen et al. | What should be the input: Investigating the environment representations in sim-to-real transfer for navigation tasks | |
Kopitkov et al. | Bayesian information recovery from CNN for probabilistic inference | |
Gao et al. | Shared autonomy for assisted mobile robot teleoperation by recognizing operator intention as contextual task | |
Zheng et al. | Approaching camera-based real-world navigation using object recognition | |
Crnokic et al. | Artificial neural networks-based simulation of obstacle detection with a mobile robot in a virtual environment | |
Wang et al. | Path planning model of mobile robots in the context of crowds |