Automatic Object Detection on Aerial Images Using Local Descriptors and Image Synthesis

Perrotton, Xavier; Sturzel, Marc; Roux, Michel

doi:10.1007/978-3-540-79547-6_29

Cited by 11 publications

(17 citation statements)

References 12 publications

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“…The HDHR descriptors [7] computes a distance between histograms of Haar regions. A descriptor is defined by computing the distance D between histograms f and g associated to Haar regions:…”

Section: Histogram Distance On Haar Regions(hdhr)mentioning

confidence: 99%

Mining families of features for efficient object detection

Perrotton¹,

Sturzel²,

Roux

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Training object detectors aims at choosing specific visual attributes which are efficient and optimal for each learned object. This paper presents a new process which achieves this goal by putting all families of descriptors one wants to consider in a pool of descriptors and by letting the algorithm build a cascade with the most efficient descriptors by introducing management of very large features pools. On the one hand, the selection of a specific family of descriptors for a given application implies a deep experience of the operator on the algorithm behaviour. On the other hand, physical constraints such as computer time and memory requirements prevent us from using all available descriptors. We present here a solution which considers several families of descriptors as a pool of descriptors and builds a cascade with the most efficient descriptors. The idea developed here consists in beginning to build a cascade with one type of descriptors and then introducing new kinds of descriptors when the current descriptor family does not bring enough differentiating information anymore. In this scope, four families of descriptors are studied here: Histogram Distance on Haar Region (HDHR), Edge Orientation Histograms (EOH), Histogram Orientation Gradient (HOG) and Gabor filters. Evaluation on public data sets shows the importance of complementary features, since performances of state of the art methods are improved.

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“…The HDHR descriptors [7] computes a distance between histograms of Haar regions. A descriptor is defined by computing the distance D between histograms f and g associated to Haar regions:…”

Section: Histogram Distance On Haar Regions(hdhr)mentioning

confidence: 99%

Mining families of features for efficient object detection

Perrotton¹,

Sturzel²,

Roux

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

Self Cite

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“…The Haar-like features are extended by [9] and [10] for improved rotational invariance. In [11], the authors further extended Haar-like features into a histogram distance measurement to enhance the discriminality. The Haar-like feature can be considered as an Edge-based feature, because it measures the response of linear edge detectors at different subareas of the input image [4].…”

Section: A Face Detectionmentioning

confidence: 99%

“…In [11], the authors have proposed a new descriptor, the Histogram Distance on Haar Regions (HDHR) for airplane detection. Different from the Haar-like feature being adopted in [2], the HDHR works by first quantize the Haar regions based on gray-level intensity into several bins to form a histogram for each Haar region, then the distance for each bin between those histograms are calculated, the final distance of two histograms is the summation of all distances in each and every bin.…”

Section: Generic Object Detectionmentioning

confidence: 99%

“…Similar to [2] and [18], the authors also adopted AdaBoost supervised learning algorithm to select the most salient HDHR descriptor. In comparison with the Haarlike feature, the HDHR feature descriptor not only calculates the sum of pixel intensities between different Haar regions, but also consider the quantization of intensity values, thus providing more detailed information [11]. Similar to Haar-like feature, the HDHR feature is an edge-based feature.…”

Section: Generic Object Detectionmentioning

confidence: 99%

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A Review of Gradient-Based and Edge-Based Feature Extraction Methods for Object Detection

Sheng

2011

2011 IEEE 11th International Conference on Computer and Information Technology

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In computer vision research, object detection based on image processing is the task of identifying a designated object on a static image or a sequence of video frames. Projects based on such research works have been widely adopted to various industrial and social applications. The fields to which those applications applies includes but not limited to, security surveillance, intelligent transportation system, automated manufactoring, quality control and supply chain management. In this paper, we are going to review a few most popular computer vision methods based on image processing and pattern recognition. Those methods have been extensively studied in various research papers and their significance to computer vision research have been proven by subsequent research works. In general, we categorize those methods into to gradient-based and edge-based feature extraction methods, depending on the low level features they use. In this paper, the definitions for gradient and edge are extended. Because an image can also be considered as a grid of image patches, it is therefore reasonable to incorporate the concept of granules to gradient for a review. The definition for granules can be found in [1].

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“…Compared with other level-set segmentation, the main remodification of the novel approach artfully avoids much redundant computation and pops out the efficiency perceptually. Based on the assumption that targets and backgrounds have different textures, in [14], Xavier et al used a boosting algorithm to select discriminating features and introduced a new descriptor--Histogram Distance on Haar Regions (HDHR), robust to background and target texture variations, to realize automatic object detection on aerial images Observed results prove that it can be trained on adapted simulated data and yet be efficient on real images, compared with several classical descriptors.…”

Section: Introductionmentioning

confidence: 99%

Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting

Cai¹,

Chen²,

Ma³

2011

IJITCS

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Abstract-Automatic aerial image interpretation is one of new rising high-tech application fields, and it's proverbially applied in the military domain. Based on human visual attention mechanism and texture visual perception, this paper proposes a new approach for man-made object detection and marking by extracting texture and geometry structure features. After clustering the texture feature to realize effective image segmentation, geometry structure feature is obtained to achieve final detection and marking. Thus a man-made object detection methodology is designed, by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected. The experiments sustain that the proposed method is effective and rational.

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Automatic Object Detection on Aerial Images Using Local Descriptors and Image Synthesis

Cited by 11 publications

References 12 publications

Mining families of features for efficient object detection

Mining families of features for efficient object detection

A Review of Gradient-Based and Edge-Based Feature Extraction Methods for Object Detection

Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting

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