Automatic photointerpretation and target location

Holmes, W. S.

doi:10.1109/proc.1966.5249

Cited by 18 publications

(4 citation statements)

References 3 publications

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“…images, (2) Holmes double-gated detector [10,11], and (3) rejection of "large" targets. Since two of the three targets are not visible in most of the images for the latter case, the probability of detection cannot be much higher than 1/3.…”

Section: Spreading Errorsmentioning

confidence: 99%

Priorization of region-of-interest (ROI) using embedded coding of wavelet coefficients

Martel

Zaccarin²

1998

SPIE Proceedings

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This paper addresses the problem of prioritizing, i.e., preserve with higher fidelity, region-of-interest during image compression. Regions-of-interest are found, for example, in medical imagery where only a small area is useful for diagnostic, or in surveillance images where targets have to be identified and tracked. These ROl are often characterized by their fine details which therefore need to be preserved if the image is to be of any use after it is decompressed. Wavelet-based image compression is appropriate for such tasks because of its localization property. We present an algorithm, based on Shapiro's popular EZW (Embedded image coding using Zerotree ofWavelet coefficients) to prioritize region-of-interest. A non-uniform quantizer with smaller steps for smaller coefficients is used on the coefficients of the ROl. This allows to transmit initially the fine details of the ROl and to use successive approximation quantization to reduce the quantization error on larger coefficients of the image, ROT or non-ROl. Simulation results show that this approach allows to efficiently preserve the fine details of the ROl.

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Section: Spreading Errorsmentioning

confidence: 99%

Priorization of region-of-interest (ROI) using embedded coding of wavelet coefficients

Martel

Zaccarin²

1998

SPIE Proceedings

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“…Blank cells are ignored. This type of filter was originally successfully used for detection of aircraft in aerial photographs (Holmes, 1966).…”

Section: 6)mentioning

confidence: 99%

Development of data enhancement and display techniques for stream-sediment data collected in the national uranium resource evaluation program of the United States Department of Energy

Koch¹,

Howarth²,

Carpenter³

et al. 1979

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We spent a substantial amount of time in assembling the data base for this report. These data bases are large in comparison to those most common in scientific computing, and we needed to rearrange the data in an efficient form in order to make our calculations at reasonable costs. 11 SECTION 2. TECHNIQUES OF DATA ANALYSIS In this section, we discuss statistical/cartographic mapping techniques, a study of high uranium values using box plots, methods for basic statistical analyses, and a mineralogical model to calculate residual uranium from original uranium. 2.1 STATISTICAL/CARTOGRAPHIC MAPPING TECHNIQUES TABLE 3.1.1. (Continued) Geologic Type Number of samples Kings Mountain Belt 157 Metamorphic rocks, undifferentiated Granite High Shoals granitic gneiss 15 Charlotte Belt 1088 Undifferentiated Granites, age unknown Granites, 300 M.Y. Granites, 400 M.Y. Metagranites Gabbro, 413 M.Y.

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“…For example, converting a continuous-tone picture into a set of black blobs on a white background, with the black blobs approximating a connected range of the gray scale, often requires a combination of convolutions and thresholding operations [1]. The outlining and smoothing of the edges of the blobs are also usually implemented by sequences of thrcsholded convolutions [2,3].…”

Section: Inh'oductionmentioning

confidence: 99%

Thresholded Convolutions Operations

Sklansky

1970

J. ACM

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Thresholded convolution operations occur frequently in picture-processing algorithms and in special pict ure-processing hardware, such as image storage tubes. A number of theorems revealing some of the effects of combining the thresholding operation with tlle convolution operation are derived. In particular it is shown that a discrete convolution operation takes binary-valued functions into binary-valued functions if and only if it is a translation or identically zero. It is also shown that no thresholded nonzero convolution (i.e. a nonzero convolution followed by a thresholding) is equivalent to an unthresholded discrete convolution.KEY WORDS AND PHRASES: thresholded operations, convolution, discrete linear operations, correlation, t)icture-proccssing, image storage lube, edge enhancement, image-processing, optical-processing, matched tilter, pattern recognition CR CATEGORI.ES: 3.63, 3.69, 5.39, 6.22 Inh'oductionThresholded convolution operations, of which simple threshohling is a special case, occur frequently in picture-processing by machine. For example, converting a continuous-tone picture into a set of black blobs on a white background, with the black blobs approximating a connected range of the gray scale, often requires a combination of convolutions and thresholding operations [1]. The outlining and smoothing of the edges of the blobs are also usually implemented by sequences of thrcsholded convolutions [2,3]. An electronic picture-processing device, called the "image storage tube," is particularly suited to implementing thresholded convolutions [4][5][6].In the present paper we derive a number of theorems that reveal some of the significance of the thresholding operation. In particular we show that no thresholded operation (i.e. an operation followed by a thresholding) is equivalent to a convolution.The work here suggests a new direction of research into picture-processing by parallel operations. Nolation and Basic Definitions

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Automatic photointerpretation and target location

Cited by 18 publications

References 3 publications

Priorization of region-of-interest (ROI) using embedded coding of wavelet coefficients

Priorization of region-of-interest (ROI) using embedded coding of wavelet coefficients

Development of data enhancement and display techniques for stream-sediment data collected in the national uranium resource evaluation program of the United States Department of Energy

Thresholded Convolutions Operations

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