Automatic Detection of Targets Using Center-Surround Difference and Local Thresholding

Sun, Sun-Gu; Kwak, Dong-Min

doi:10.4304/jmm.1.1.16-23

Cited by 14 publications

(2 citation statements)

References 28 publications

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“…Summary of target detection techniques and its drawbacks (Shao et al, 2012;Sun and Kwak, 2006;Parker, 1991) Thresholding is defined based on pixel intensity and adjacency.…”

Section: Tablementioning

confidence: 99%

Adaptive pedestrian detection in infrared images using fuzzy enhancement and top-hat transform

Soundrapandiyan

Mouli

2017

IJCVR

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Infrared imaging is the order of the day. Infrared imaging finds its applications extensively in defence related areas. Pedestrian detection in IR image is a challenging problem. This paper addresses this problem using fuzzy-based enhancement followed by top-hat transform. The proposed method is adaptive in the sense that the parameters required for the process are calculated adaptively based on the image characteristics. Experimental results on the standard dataset show the robustness of the proposed method with 90% detection rate of pedestrians under different conditions and outperform the other existing methods.

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“…Summary of target detection techniques and its drawbacks (Shao et al, 2012;Sun and Kwak, 2006;Parker, 1991) Thresholding is defined based on pixel intensity and adjacency.…”

Section: Tablementioning

confidence: 99%

Adaptive pedestrian detection in infrared images using fuzzy enhancement and top-hat transform

Soundrapandiyan

Mouli

2017

IJCVR

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“…There are also methods for target detection which are based on the differences [6]. In addition, several methods have been presented based on the statistical regression [7], in which complex background clutter is predicted and deleted according to the regression model.…”

Section: Introductionmentioning

confidence: 99%

Small target detection and tracking based on the background elimination and Kalman filter

Dehghani

Pourmohammad

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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The problem of small target detection in infrared images is one of the most important areas of research in passive defense systems. This detection method is classified in the Electro optic systems group. Generally, the challenges of the field are divided into two parts: detection and tracking. 1) Due to long detection distance, the amplitude of target signal compared with heavy background clutter is weak. On the other hand, targets appear with few pixels, so that there is no obvious and usable structural and contextual information. 2) Another challenge in tracking small targets is partial obstruction or closeness of background's brightness level to brightness level of the desired target (fading). In this paper, first background is removed by subtracting row mean, then the target are tracking using morphological filtering, thresholding the identified targets and finally by Kalman filter.

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Automatic Detection and Severity Assessment of Crop Diseases Using Image Pattern Recognition

Han

Haleem

Taylor

2016

Studies in Computational Intelligence

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Disease diagnosis and severity assessment are necessary and critical for predicting the likely crop yield losses, evaluating the economic impact of the disease, and determining whether preventive treatments are worthwhile or particular control strategies could be taken. In this work, we propose to make advances in the field of automatic detection and diagnosis and severity assessment of crop diseases using image pattern recognition. We have developed a two-stage crop disease pattern recognition system which can automatically identify crop diseases and assess sevrity based on combination of marker-controlled watershed segmentation, superpixel based feature analysis and classification. We have conducted experimental evaluation using different feature selection and classification methods. The experimental result shows that the proposed approach can accurately detect crop diseases (i.e. Septoria and Yellow rust, which are the two most important and major types of wheat diseases in UK and across the world) and assess the disease severity with efficient processing speed.

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Automatic Detection of Targets Using Center-Surround Difference and Local Thresholding

Cited by 14 publications

References 28 publications

Adaptive pedestrian detection in infrared images using fuzzy enhancement and top-hat transform

Adaptive pedestrian detection in infrared images using fuzzy enhancement and top-hat transform

Small target detection and tracking based on the background elimination and Kalman filter

Automatic Detection and Severity Assessment of Crop Diseases Using Image Pattern Recognition

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