It has been pointed out that any heuristic for problem solving - weak or strong - is based on the recognition of a unary relation (subset) or a relation of higher arity on the set of states of the problem. Since the r...
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It has been pointed out that any heuristic for problem solving - weak or strong - is based on the recognition of a unary relation (subset) or a relation of higher arity on the set of states of the problem. Since the recognition of such relations from examples (often obtained by gedanken experiments on the problem) is a necessary part of the development of such heuristics, one can effectively develop heuristics automatically by using patternrecognition techniques. This thesis is supported by simple examples of problems solved by heuristics developed by a computor program.
When radar and optical images are examined in detail, it is often found that the most distinguishable features of the two types of images are the shapes of the objects in the scenes. Therefore, edges can be used to ad...
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When radar and optical images are examined in detail, it is often found that the most distinguishable features of the two types of images are the shapes of the objects in the scenes. Therefore, edges can be used to advantages in the recognition and matching of objects. An edge extraction technique was developed and used to extract the salient outlines of objects of interest. This method also removes many of the edges extracted from the background and shadows around the objects.
This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a ...
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ISBN:
(纸本)9781665448994
This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a smoother transformation from the augmented input space to the latent space. Inspired by the fact that ANF can offer greater expressivity by stacking multiple variational autoencoders (VAE), we generalize the popular VAE-based compression framework by the autoencoding transforms of ANF. When evaluated on Kodak dataset, our ANF-based model provides 3.4% higher BD-rate saving as compared with a VAE-based baseline that implements hyper-prior with mean prediction. Interestingly, it benefits even more from the incorporation of a post-processing.network, showing 11.8% rate saving as compared to 6.0% with the baseline plus post-processing.
In a previous paper, the authors present an O(kl multiplied by k2) depth-first tree search algorithm to find a montonic transformation such that the histogram of a transformed picture is closest to the desired histogr...
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In a previous paper, the authors present an O(kl multiplied by k2) depth-first tree search algorithm to find a montonic transformation such that the histogram of a transformed picture is closest to the desired histogram of a given picture in the L1 norm sense (least sum of absolute differences). The same problem using Ln norm is investigated in this paper. A breadth-first tree search algorithm with cut-off threshold is described, which has time complexity 0(k1 multiplied by k2).
Low-level motion estimators based on differential, tensor, and phase methods are reformulated in a unified way as filter methods in the continuous space-time domain. This approach allows inherent conceptual deficits t...
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ISBN:
(纸本)0818658258
Low-level motion estimators based on differential, tensor, and phase methods are reformulated in a unified way as filter methods in the continuous space-time domain. This approach allows inherent conceptual deficits to be distinguished from those related to the implementation in discrete space. A detailed analytical error analysis is performed. All techniques yield unbiased motion estimates for areas of constant velocity with any type of gray value structure in continuous space. Errors are only introduced by an inadequate discrete implementation. Further investigated are the influence of zero-mean normal distributed noise, spatially and temporally (accelerated) varying motion, motion discontinuities, and illumination changes. The main results of the paper are also illustrated with real-world image sequences.
An approach to feature detection in image approximation networks is presented. The network is an approximation of the image data surface. The extraction of global image features from the network is described. Primitiv...
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An approach to feature detection in image approximation networks is presented. The network is an approximation of the image data surface. The extraction of global image features from the network is described. Primitive features such as peaks and valleys are located, then ridges and valley lines are traced by iteratively exploring neighboring points of detected features. These topographical features provide a region segmentation of the image. The region boundaries represent global charcteristics of the image data.
A picture digitization grid based on logarithmic spirals rather than Cartesian coordinates is presented. Expressing this curvilinear grid as a conformal exponential mapping reveals useful imageprocessing.properties. ...
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A picture digitization grid based on logarithmic spirals rather than Cartesian coordinates is presented. Expressing this curvilinear grid as a conformal exponential mapping reveals useful imageprocessing.properties. The mapping induces a computational simplification that suggests parallel architectures in which most geometric transformations are effected by data shifting in memory rather than arithmetic on coordinates. These include fast, parallel noise-free rotation, scaling, and some projective transformations of pixel defined images. Conformality of the mapping preserves local picture-processing.operations such as edge detection.
The objective of computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This study proposes a new paradigm for a modular computer vision system which is both d...
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The objective of computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This study proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long term memory (LTM) contains a detailed model of the scene under consideration. A collection of analysis processors, each of which is specialized for a particular task can communicate with both of these memories.
Iris recognition is a powerful biometrics for personal identification, but it is difficult to acquire good-quality iris images in real time. For making iris recognition more convenient to use, we design an iris recogn...
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ISBN:
(纸本)9781424441990
Iris recognition is a powerful biometrics for personal identification, but it is difficult to acquire good-quality iris images in real time. For making iris recognition more convenient to use, we design an iris recognition system at a distance about 3 meters. There are many key issues to design such a system, including iris image acquisition, human-machine-interface and imageprocessing. In this paper, we respectively introduce how we deal with these problems and accomplish the engineering design. Experiments show that our system is convenient to use at the distance of 3 meters and the recognition rate is not worse than the state-of-the-art close-range systems.
An analysis is given of the Moire pattern formation in scanning and reproducing halftone pictures. The analysis includes the effects of the aperture size and shape of the reproduction printing process. Also the effect...
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An analysis is given of the Moire pattern formation in scanning and reproducing halftone pictures. The analysis includes the effects of the aperture size and shape of the reproduction printing process. Also the effects of parallelogram grid sampling as well as square grid sampling have been taken into consideration. A simulation program has been written to synthesize the Moire patterns based upon the Fourier transform of the output picture. Several observations will be discussed and among two of the more important ones are: (a) Moire fringes can result from beating together of clusters of Fourier spatial frequencies and (b) fixed value thresholding intensifies Moire patterns.
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