We demonstrate the use of our active object recognition algorithms in a mechanical assembly task. The algorithms are used to classify and estimate the pose of parts of the assembly in different stable rest positions a...
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We demonstrate the use of our active object recognition algorithms in a mechanical assembly task. The algorithms are used to classify and estimate the pose of parts of the assembly 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 used in determining both the final object class and pose estimate. A feature space trajectory (FST) in eigenspace is used to represent 3-D distorted views of each object. The FSTs are analyzed off-line to determine the camera positions that best resolve ambiguities. We also describe methods for rejecting untrained objects and adding new parts to an existing set of FSTs using a new feature update method.
A method for locating the doors in an image is presented. The method integrates an edge map with color information known about the doors to provide better results than methods based purely on color or edges. The Hough...
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A method for locating the doors in an image is presented. The method integrates an edge map with color information known about the doors to provide better results than methods based purely on color or edges. The Hough transform is used to find lines and then a set of heuristics is used to find possible door regions. The door regions are tested by determining whether the region is the correct color. The combination of color and edge data allowed the system to successfully identify doors within a complex environment at different scales, orientations, lighting conditions, and using different cameras.
At present, in computervision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both...
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At present, in computervision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both biologically inspired and conventional approaches. Evidently, new algorithms and system architectures based on advanced biological motivation should be developed for solution of computational problems related to this visual task. Basic problems that should be solved for creation of effective artificial visual system to process real world images are a search for new algorithms of low-level image processing that, in a great extent, determine system performance. In the present paper, the results of psychophysical experiments and several advanced biologically motivated algorithms for low-level processing are presented. These algorithms are based on local space-variant filter, context encoding visual information presented in the center of input window, and automatic detection of perceptually important image fragments. The core of latter algorithm (the cascade method) are using local feature conjunctions such as noncolinear oriented segment and composite feature map formation. Developed algorithms were integrated into foveal active vision model, the MARR. It is supposed that proposed algorithms may significantly improve model performance while real world image processing during memorizing, search, and recognition.
An new concept for visually guided manipulator control is introduced. It eliminates the need for a calibration of the manipulator as well as of the vision system and comprises an automatic adaptation to changing param...
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An new concept for visually guided manipulator control is introduced. It eliminates the need for a calibration of the manipulator as well as of the vision system and comprises an automatic adaptation to changing parameters. A key point of the concept is the achievement of a complex and elaborate desired goal by activating an appropriate sequence of rather simple elementary behaviors. Contrary to conventional stereo vision methods it uses an calibration-free camera system and allows a direct transition from image coordinates to motion control commands of a robot. By this approach, the abstract coordinate transformations have been avoided, instead, image data are used directly to control the behavior of the robot, or the interactions of the robot with physical objects. Thus, it makes knowledge of many hard-to-measure optical and mechanical system unnecessary;moreover, it lends itself to the realization of learning and adaptive robots. The concept has been successfully realized and tested in real-word experiments with an visually guided calibration-free 5 degree of freedom (DOF) manipulator involving the grasping of various objects with nearly any shape in arbitrary position (standing, lying, sloping) in the robot' 3-D work space.
Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items...
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Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. We discuss the use of clustering (multiple clusters per class) and how it is vital to achieve useful classification. New clustering methods using class identity and new cluster classes are advanced and shown to be of use for this application. Radial basis function neural net classifiers are emphasized. We expect our results to be of use for other classifiers and applications.
This work demonstrates a vision-based control technique that does not require robot or vision system calibration. There are two distinct advantages: first, the approach is generic and can be applied to a variety of sy...
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This work demonstrates a vision-based control technique that does not require robot or vision system calibration. There are two distinct advantages: first, the approach is generic and can be applied to a variety of systems;second, calibration is unnecessary after a reconfiguration or disturbance to the robotic workcell. It has the potential to provide a low-cost, low-maintenance automation solution for unstructured industries and environments. The robot end-effector tracks a moving target using a novel dynamic quasi-Newton control law formulated in the image plane and on-line Jacobian estimation using either a dynamic Broyden's method or a dynamic recursive least squares algorithm. Experimental results demonstrate convergent and stable control of an uncalibrated manipulator tracking a moving target. The method is shown to be robust to system reconfiguration such as modifications to the position and orientation of the camera.
This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surface representation scheme. This representation scheme captures the 3-D curvature information of any free-...
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This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surface representation scheme. This representation scheme captures the 3-D curvature information of any free-form surface and encodes it into a 2-D image corresponding to a certain point on the surface. This image is unique for this point and is independent from the object translation or orientation in space. For this reason we called this image `Surface Point Signature' (SPS). This scheme can be used as a global representation of the surface as well as a local one and also in a scale independent surface matching. It performs faster registration than existing registration approaches.
We present here a review of scientific theoretical researches performed during last five years at our Institute. Specific attention is paid to active optical vision systems and related effects that have not been treat...
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We present here a review of scientific theoretical researches performed during last five years at our Institute. Specific attention is paid to active optical vision systems and related effects that have not been treated earlier from a viewpoint of modern radiative transfer approaches and techniques. This is just that is meant as `nontraditional' features. All these features formed a basis for evaluating somewhat surprising, but easily physically treated properties of images provided by active vision systems operating through a turbid medium. Specific topics are: (i) effects of free backscattering pattern of coarse aerosols, such as fog or cloud droplets, on optical interference and the computational consequences of these effects;(ii) image contrast and ultimate visibility range of targets with different reflective properties and;in particular, possible improving of the visibility of a target as it is sinking into a turbid medium;(iii) imaging of non-Lambertian objects and peculiarities in their images;(iv) applications to assessing visibility quality of a car driver under poor weather conditions and some ways to optimize the visibility and to enhance the ultimate visibility range. We succeed in treating all these topics by rather simple analytical expressions requiring no sophisticated software to be dealt with.
This paper addresses the problem of architecture optimization, when implementing an image matching primitive in reconfigurable circuits (Xilinx FPGA, for example). Circuit spatial organization is optimized in terms of...
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This paper addresses the problem of architecture optimization, when implementing an image matching primitive in reconfigurable circuits (Xilinx FPGA, for example). Circuit spatial organization is optimized in terms of processing time, and circuit volume, in order to suit well for real time on board applications. This optimized adaptive spatial and scalable organization of the μPD circuit dedicated to image matching reduces by one order the spatial and temporal performance (compared to a sequential implementation), without altering the quality of matching. The μPD circuit has been validated with the minimal 22 elementary cells architecture with Xilinx 4010 XL circuit working at 12 MHz and occupying 92% of the circuit CLB. It performs the pyramidal 256×256 image matching in less than 1 s.
Pose characterization refers to the unique representation of an object's pose through a set of image features. In this regard, one of the prerequisites for using visual images in representing an object's pose ...
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Pose characterization refers to the unique representation of an object's pose through a set of image features. In this regard, one of the prerequisites for using visual images in representing an object's pose is a set of view-dependant image features that is unique to a single position and orientation (pose) of the object relative to the camera. Here, we propose an enhancement to the global feature extraction method. It utilizes an active illumination that projects a set of orthogonal grid patterns on the object. The deformations of the grid pattern incident on the complex target object carries important information about the surface structure of the target. This grid-encoded image can be characterized using global descriptors. Results indicate that this illumination increases the sensitivity of the global features to changes in object pose.
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