Scene segmentation is a pre-processing step for many vision systems. We are concerned with segmentation as a precursor to 3-D scene modeling. Segmentation of a scene for this purpose usually involves dividing an image...
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(纸本)081942983X
Scene segmentation is a pre-processing step for many vision systems. We are concerned with segmentation as a precursor to 3-D scene modeling. Segmentation of a scene for this purpose usually involves dividing an image into areas that are relatively uniform in some value (e.g. intensity, range, or curvature). This single segmented image represents the analogous segmented scene. This paper presents a segmentation method that uses features to indicate boundaries or edges between regions. We incorporate features from multiple image types to obtain an more accurate segmentation of objects or object parts in the scene. Multiple features are not only combined directly to improve segmentation results, but they are also used to guide a smoothing operation. This smoothing technique preserves features representing edges while smoothing noise in the images. The segmentation method is based on applying a watershed algorithm to a fuzzy feature map. A fuzzy feature map being any image containing fuzzy values representing degree of membership in a particular feature class. The first step in obtaining the fuzzy feature map involves smoothing noise from the image pair. We apply an anisotropic diffusion algorithm to both images. This algorithm smooths noise in the images while preserving changes in range, intensity, and surface normal. We create three fuzzy feature maps from the smoothed range and intensity image pair: gradient of the range image, gradient of the intensity image, sind gradient of the surface normal of the range image. We fuse these fuzzy feature maps to create a fuzzy feature map of edges. This map includes both step and crease edges. Where step edges are defined as discontinuities in range and crease edges are discontinuities in surface normal of range. We derive a segmentation from this fuzzy edge map by application of a morphological watershed algorithm. We present results from analysis of real images.
An algorithm consisting of an artificial neural network and phase only match filter (POMF) method to tracking an object in a series of satellite images with variant intensity level is proposed. The notable adaptive re...
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An algorithm consisting of an artificial neural network and phase only match filter (POMF) method to tracking an object in a series of satellite images with variant intensity level is proposed. The notable adaptive resonance theory (ART) neural network is used as a high frequency noise filter for the POMF. The algorithm is examined by means of several satellite images. From the experimental results, the proposed method demonstrates that the performance of POMF can be successfully improved by reducing the high frequent noise of the background in the satellite images.
We present new test results for our active object recognition algorithms. The algorithms are used to classify and estimate the pose of objects in different stable rest positions and automatically re-position the camer...
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We present new test results for our active object recognition algorithms. The algorithms are used to classify and estimate the pose of objects in different stable rest positions and automatically re-position the camera if the class or pose of an object is ambiguous in a given image. Multiple object views are now used in determining both the final object class and pose estimate;previously, multiple views were used for classification only. A feature space trajectory (FST) in eigenspace is used to represent 3-D distorted views of an object. FSTs are constructed using images rendered from solid models. We discuss lighting and material settings for photorealism in the rendering process. The FSTs are analyzed to determine the camera positions that best resolve ambiguities. Real objects are recognized from intensity images using the FST representation derived from rendered imagery.
The basic idea that the perception of actual embodied beings, be they animal or robotic, is fundamentally related to their embodiment is generally referred to as Purposive or Animate vision. Research in this field gen...
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The basic idea that the perception of actual embodied beings, be they animal or robotic, is fundamentally related to their embodiment is generally referred to as Purposive or Animate vision. Research in this field generally emphasizes low-level vision techniques. This paper outlines a philosophical basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. The aim is to facilitate the use of high-level vision through an active perception framework. We argue that the classical computervision paradigm has problems in high-level vision due to an implicit assumption that objects in the world can be objectively subdivided into categories. Further, that through conceptual embodiment, active perception offers a way forward. We present a mobile robot navigation system based on the principles of conceptual embodiment. The system uses object recognition to guide a robot around known objects. The robot's object model is embodied, and this embodiment yields specific advantages for the robot.
This paper addresses a method for implementation of some basic geometric transformation, namely rotation and scale, on 1D parallel SIMD computers. Solutions to solve many implementation details such as 2D image scanni...
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This paper addresses a method for implementation of some basic geometric transformation, namely rotation and scale, on 1D parallel SIMD computers. Solutions to solve many implementation details such as 2D image scanning with 1D machine, the floating point arithmetic simulation and an approximation of the transformed pixel spatial co-ordinates are proposed. The proposed approach tolerates up to ±15° rotation and up to ±50% continue scale changes between images. Those transformations can be used for construction of robust matching primitives useful for robot applications. The evaluation of the proposed method, on SYMPATI2, a French 1D parallel computer (designed by CEA,LETI) is reported.
A truly autonomous robot must sense its environment and react appropriately. These issues attain greater importance in an outdoor, variable environment. Previous mobile robot perception systems have relied on hand-cod...
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A truly autonomous robot must sense its environment and react appropriately. These issues attain greater importance in an outdoor, variable environment. Previous mobile robot perception systems have relied on hand-coded algorithms for processing sensor information. Recent techniques involve the use of artificial neural networks to process sensor data for mobile robot guidance. A comparison of a fuzzy logic control for an AGV and a neural network perception is described in this work. A mobile robot test bed has been constructed using a golf cart base. The test bed has a fuzzy logic controller which uses both vision and obstacle information and provides the steering and speed controls to the robot. A feed-forward neural network is described to guide the robot using vision and range data. Suitable criteria for comparison will be formulated and the hand-coded system compared with a connectionist model. A comparison of the two systems, with performance, efficiency and reliability as the criteria, will be achieved. The significance of this work is that it provides comparative tradeoffs on two important robot guidance methods.
A computervision algorithm was developed to detect moving aircraft located in video images. Using a gradient-based approach, the algorithm computes optical flow vectors in each frame of the sequence. Vectors with sim...
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A computervision algorithm was developed to detect moving aircraft located in video images. Using a gradient-based approach, the algorithm computes optical flow vectors in each frame of the sequence. Vectors with similar characteristics (location, magnitude, and direction) are clustered together using a spatial consistency test. Vectors that pass the spatial consistency test are extended temporally to make predictions about the optical flow locations, magnitudes, and directions in subsequent frames. The actual optical flow vectors that are consistent with the predicted vectors are labeled as vectors associated with a moving target. The algorithm was tested on images obtained with a video camera mounted below the nose of a Boeing 737. The algorithm correctly detected an aircraft from a distance of one mile in over 80% of the frames with zero false alarms.
Exploratory research on the design of a modular autonomous mobile robot controller for steering and speed control is described. The high level control of the robot incorporates a fuzzy logic approach. Steering and spe...
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Exploratory research on the design of a modular autonomous mobile robot controller for steering and speed control is described. The high level control of the robot incorporates a fuzzy logic approach. Steering and speed control are achieved using a three-axis Galil motion controller. The steering mechanism and the speed control involve the parallel control of the robot's two front wheels based on decisions made by the upper level fuzzy logic to guide the robot in a desired direction to follow a specific path and avoid obstacles in that path. The steering motors are Electrocraft brush DC motor. The BDC amplifiers are run in the current loop mode. The overall control is supervised by a personal computer through the multi-axis controller. The system has been simulated on Matlab and Simulink and optimal values for the digital gains were achieved for desired control. Testing of these systems has been done in a laboratory setting as well as on an outside track with positive results.
This research focuses on applications based on wireless monitoring and robot control, utilizing motion image and augmented reality. These applications include remote services and surveillance-related functions such as...
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This research focuses on applications based on wireless monitoring and robot control, utilizing motion image and augmented reality. These applications include remote services and surveillance-related functions such as remote monitoring. A remote service can be, for example, a way to deliver products at a hospital or old people's home. Due to the mobile nature of the system, monitoring at places with privacy concerns is possible. On the other hand, mobility demands wireless communications. Suitable and present technologies for wireless video transfer are weighted. Identification of objects with the help of Radio Frequency Identifying (RFID) technology and facial recognition results in intelligent actions, for example, where the control of a robot does not require extensive workload from the user. In other words, tasks can be partially autonomous. RFID can be also used in augmentation of the video view with virtual objects. As a real-life experiment, a prototype environment is being constructed that consists of a robot equipped with a video camera and wireless links to the network and multimedia computer.
For faithful 3-D reconstruction of objects with smooth surfaces, stereo vision techniques rely heavily on the surface texture. However, most of the real life objects lack this feature. To `sharpen' the textural co...
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For faithful 3-D reconstruction of objects with smooth surfaces, stereo vision techniques rely heavily on the surface texture. However, most of the real life objects lack this feature. To `sharpen' the textural content of visual surfaces, a structured-light sensing configuration has been used. This technique can be used to enhance the features used in solving the correspondence problem in computational stereo vision. A light pattern is projected to encode the smooth surface of the object. The observed light pattern is then used to compute surface properties. We present a simple design for a trinocular vision system with structured light using off-the-shelf components. The processing pipeline of the system consists of four stages. First, the light pattern is detected in the captured images. Second, the pattern is skeletonized using connected component labeling. Third, a maximum-weighted bipartite technique is used to do the matching. Finally, a global surface fitting technique is used to integrate the reconstruction from different views in one frame and fit a mesh of triangles to the integrated data. Results using real images are promising. The advantages of this system lie in its design simplicity, low cost and the potential for fast and parallel implementation.
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