Geometrical Method for Shadow Detection of Static Images

2018 ◽  
pp. 152-173
Author(s):  
Manoj K. Sabnis ◽  
Kavita ◽  
Manoj Kumar Shukla
Keyword(s):  
Shadow Detection ◽  
Geometrical Method ◽  
Static Images

Model-Based Approach for Shadow Detection of Static Images

2016 ◽  
pp. 49-73
Author(s):  
Manoj K Sabnis ◽  
Manoj Kumar Shukla
Keyword(s):  
Shadow Detection ◽  
Model Based ◽  
Static Images

A Comparative Approach for Standard Shadow Detection Methods

2018 ◽  
Vol 6 (8) ◽  
pp. 302
Author(s):  
Kavita . ◽  
Manoj K Sabnis
Keyword(s):  
Background Subtraction ◽  
Qualitative Evaluation ◽  
Shadow Detection ◽  
Detection Methods ◽  
Comparative Approach ◽  
Single Frame ◽  
Standard Level ◽  
Static Images ◽  
Shape Mapping ◽  
Object Dynamic

False tracking is the biggest problem identified in tracking. The reasons for this is identified as shadow of the object to be tracked which have their shape mapping to the shape of the object.  Dynamic shadow detection is the field in which videos are used. Dynamic shadow detection is found to be more exposed in literature due to the possibility of comparison, frame differentiation, background subtraction. All this not being possible in case of static images as they represent a single frame and are not used to that extent. Taking this as a challenge this paper presents static shadow detection in which the static shadow detection methods are mapped with dynamic images within the domain of image processing.The results so obtained are then authenticated from the user side. Every user may have different views, so as to bring the evaluation at a standard level this qualitative evaluation is quantified so as to be represented in form of tables and graphs for further analysis.

Metacognition of Emotion Recognition: Beyond Static Images

2012 ◽  
Author(s):  
Karen J. Kelly ◽  
Janet Metcalfe
Keyword(s):  
Emotion Recognition ◽  
Static Images

Behind the Geometrical Method

2020 ◽  
Author(s):  
EDWIN CURLEY
Keyword(s):  
Geometrical Method

scholarly journals Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

2021 ◽  
Vol 13 (4) ◽  
pp. 699
Author(s):  
Tingting Zhou ◽  
Haoyang Fu ◽  
Chenglin Sun ◽  
Shenghan Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Mean Shift ◽  
Color Space ◽  
Compensation Method ◽  
Shadow Detection ◽  
Processing Unit ◽  
Shadow Region ◽  
Remote Sensing Images ◽  
Shadow Compensation

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

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