Parallel hardware becomes a commonly used approach to satisfy the intensive computation demands of computervision systems. A multiprocessor architecture based on hypercube interconnecting digital signal processors (D...
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ISBN:
(纸本)9780819485830
Parallel hardware becomes a commonly used approach to satisfy the intensive computation demands of computervision systems. A multiprocessor architecture based on hypercube interconnecting digital signal processors (DSPs) is described to exploit the temporal and spatial parallelism. This paper presents a parallel implementation of low level vision algorithms designed on multi-DSP system. The convolution operation has been parallelized by using redundant boundary partitioning. Performance of the parallel convolution operation is investigated by varying the image size, mask size and the number of processors. Experimental results show that the speedup is close to the ideal value. However, it can be found that the loading imbalance of processor can significantly affect the computation time and speedup of the multi-DSP system.
A novel method called height from motion (HFM) is developed to estimate the motion and structure under planar motion. By using this method both translational and rotational motion (three degrees of freedom) can be tre...
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ISBN:
(纸本)0818620579
A novel method called height from motion (HFM) is developed to estimate the motion and structure under planar motion. By using this method both translational and rotational motion (three degrees of freedom) can be treated in a unified manner. Based on the HFM method, the correspondence problem becomes easy to deal with, especially under translational motion. Experiments of real scene image sequences and the error analysis (theoretically and experimentally) have shown the efficiency and robustness of the method.
This paper experiences a novel approach of object description using a combination of two well-known descriptors "LBP: Local Binary Patterns" and "DTMs: Discrete Tchebychev Moments". With their pros...
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ISBN:
(纸本)9781538642382
This paper experiences a novel approach of object description using a combination of two well-known descriptors "LBP: Local Binary Patterns" and "DTMs: Discrete Tchebychev Moments". With their pros and cons, the LBP and Tchebychev are widely and successfully used in the computervision community. LBP is a local based descriptor while the DTMs is a global based descriptor seem to makes the combination difficult, however, we managed in this paper to propose an interesting approach that takes advantage of their pros while imitating their cons and outperform their weaknesses. The proposed approach is tested on the COIL dataset and return very interesting results going up to 89,98% of well classified objects.
In this paper, we explain the bag of words representation from a soft computing perspective. The traditional Bag of word representation describes an image as a bag of discrete visual codewords. Where histogram of the ...
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The authors formulate the segmentation task as a search for a set of descriptions which minimally encodes a scene. A novel framework for cooperative robust estimation is used to estimate descriptions that locally prov...
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ISBN:
(纸本)0818620579
The authors formulate the segmentation task as a search for a set of descriptions which minimally encodes a scene. A novel framework for cooperative robust estimation is used to estimate descriptions that locally provide the most savings in encoding an image. A modified Hopfield-Tank network finds the subset of these descriptions which best describes an entire scene, accounting for occlusion and transparent overlap among individual descriptions. Using a part-based 3-D shape model the authors have implemented a system that is able to successfully segment images into their constituent structure.
The accessibility of public spaces for visually impaired individuals is a major concern. One of the challenges faced by blind people in public spaces is the difficulty in locating vacant seats. The presented paper dis...
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An approach to computational vision that is based on multiple levels of interpretation is presented. The step between each level is seen as taking place in three stages--parsing (in which features and groups of featur...
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ISBN:
(纸本)0818620579
An approach to computational vision that is based on multiple levels of interpretation is presented. The step between each level is seen as taking place in three stages--parsing (in which features and groups of features in an image are given labels), interpreting (in which several interpretations are built, assuring that each feature is given at most one explanation in terms of a higher-level label), and pruning (in which some interpretations are discarded because of global constraints). The parsing and pruning steps are guided by multirelational grammars, a generalization of ordinary attribute grammars and of graph grammars. A bottom-up parsing algorithm for this class of grammars is presented, and their usefulness in image interpretation is illustrated by examples using both synthetic and real-world data.
The information of vehicle is very important for maintaining traffic order under the present complex traffic environments. The image of vehicle is captured in various ways;fixed camera, movable camera, and vehicle-loa...
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images captured using consumer grade cameras are missing viable information of image which is depth. This also makes the image to be considered as a two dimensional image. The real life activities like recognition of ...
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ISBN:
(纸本)9781479933587
images captured using consumer grade cameras are missing viable information of image which is depth. This also makes the image to be considered as a two dimensional image. The real life activities like recognition of objects by measuring the distance between objects become very complex and sometimes impossible. Stereo vision imaging, 3-D modeling techniques help to minimize these shortcomings. In this paper we have analyzed various novel works which used the depth information gathered from RGB-D cameras. Microsoft Kinect, a low cost RGB-D sensor has proved to be the most effective of its kind. This paper can provide a clear idea about the extensive research using these sensors that have been going on in the field of augmented Human computer interaction. The exceptional features of these depth sensors can be used to develop more sophisticated applications in the field of computervision in near future.
A geometrical learning system is described which constructs automatically the geometric model of indoor environments from multiple stereo views. The produced model includes not only 3-D segments initially provided by ...
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ISBN:
(纸本)0818620579
A geometrical learning system is described which constructs automatically the geometric model of indoor environments from multiple stereo views. The produced model includes not only 3-D segments initially provided by a stereo vision system, but also 3-D feature groups. Feature grouping helps to predict the position of a given view with respect to the current model and allows one to correct noisy features by the geometric constraints of feature groups. This 3-D feature grouping is based on a monocular process working on the 2-D segments associated with one source image of a stereo view. Experimental results are presented.
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