Histogram-based template matching for object detection in images with varying contrast

Schrider, Christina D.; Skipper, Julie A.; Repperger, D.W.

doi:10.1117/12.703651

Cited by 4 publications

(3 citation statements)

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“…The ridge histogram and the target histogram will be different, and histogram matching with a standard template will result in improved output response. 2 The algorithm was also tested with F-15 aircraft models as targets to check for versatility in detecting other objects. Even though the aircraft body blended in well with the background, the wings and the cockpit cast a shadow that formed a discernible edge.…”

Section: Resultsmentioning

confidence: 99%

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Power spectrum weighted edge analysis for straight edge detection in images

Karvir

Skipper

2007

Visual Information Processing XVI

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Most man-made objects provide characteristic straight line edges and, therefore, edge extraction is a commonly used target detection tool. However, noisy images often yield broken edges that lead to missed detections, and extraneous edges that may contribute to false target detections. 1 We present a sliding-block approach for target detection using weighted power spectral analysis. In general, straight line edges appearing at a given frequency are represented as a peak in the Fourier domain at a radius corresponding to that frequency, and a direction corresponding to the orientation of the edges in the spatial domain. 1 Knowing the edge width and spacing between the edges, a band-pass filter is designed to extract the Fourier peaks corresponding to the target edges and suppress image noise. These peaks are then detected by amplitude thresholding. The frequency band width and the subsequent spatial filter mask size are variable parameters to facilitate detection of target objects of different sizes under known imaging geometries. Many military objects, such as trucks, tanks and missile launchers, produce definite signatures with parallel lines and the algorithm proves to be ideal for detecting such objects. Moreover, shadow-casting objects generally provide sharp edges and are readily detected. The block operation procedure offers advantages of significant reduction in noise influence, improved edge detection, faster processing speed and versatility to detect diverse objects of different sizes in the image. With Scud missile launcher replicas as target objects, the method has been successfully tested on terrain board test images under different backgrounds, illumination and imaging geometries with cameras of differing spatial resolution and bit-depth.

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Section: Resultsmentioning

confidence: 99%

“…Note the ridges in the image and certain areas in the shrubs are detected as potential targets due to comparable power spectrum output. These false detections could be removed using other image processing techniques such as histogram matching 2. Fig.…”

mentioning

confidence: 99%

Power spectrum weighted edge analysis for straight edge detection in images

Karvir

Skipper

2007

Visual Information Processing XVI

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“…Linear combination of Haarlike template binary features is used [23] [ 24] Scale Invariant Feature Transform…”

Section: Referencementioning

confidence: 99%

Assessments Of Different Speeded Up Robust Features (SURF) Algorithm Resolution For Pose Estimation Of UAV

Sheta¹

2012

IJCSES

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The UAV industry is growing rapidly in an attempt to serve both military and commercial applications. A crucial aspect in the development of UAVs is the reduction of navigational sensor costs while maintaining accurate navigation. Advances in visual sensor solutions with traditional navigation sensors are proving to be significantly promising in replacing traditional IMU or GPS systems for many mission scenarios. The basic concept behind Vision Based Navigation (VBN) is to find the matches between a set of features in real-time captured images taken by the imaging sensor on the UAV and database images. A scale and rotation invariant image matching algorithm is a key element for VBN of aerial vehicles. Matches between the geo-referenced database images and the new real-time captured ones are determined by employing the fast Speeded Up Robust Features (SURF) algorithm. The SURF algorithm consists mainly of two steps: the first is the detection of points of interest and the second is the creation of descriptors for each of these points. In this research paper, two major factors are investigated and tested to efficiently create the descriptors for each point of interest. The first factor is the dimension of the descriptor for a given point of interest. The dimension is affected by the number of descriptor sub-regions which consequently affects the matching time and the accuracy. SURF performance has been investigated and tested using different dimensions of the descriptor.The second factor is the number of sample points in each sub-region which are used to build the descriptor of the point of interest. SURF performance has been investigated and tested by changing the number of sample points in each sub-region where the matching accuracy is affected.Assessments of the SURF performance and consequently on UAV VBN are investigated.

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