Object detection in color images using nonparametric Bayes classification and orthogonal functions

1998 ◽  
Author(s):  
Mehmet Celenk ◽  
Yuan Shao
Keyword(s):  
Object Detection ◽  
Color Images ◽  
Nonparametric Bayes ◽  
Orthogonal Functions

scholarly journals AUTOMATIC SHADOW DETECTION IN AERIAL AND TERRESTRIAL IMAGES

2017 ◽  
Vol 23 (4) ◽  
pp. 578-590 ◽  
Author(s):  
Vander Luis de Souza Freitas ◽  
Barbara Maximino da Fonseca Reis ◽  
Antonio Maria Garcia Tommaselli
Keyword(s):  
Object Detection ◽  
Image Matching ◽  
Ground Truth ◽  
Position Estimation ◽  
Color Images ◽  
The Other ◽  
Object Size ◽  
Atmospheric Scattering ◽  
Spectrum Ratio ◽  
Almost All

Abstract: Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect.

scholarly journals Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

2021 ◽  
Vol 7 (9) ◽  
pp. 187
Author(s):  
Seena Joseph ◽  
Oludayo O. Olugbara
Keyword(s):  
Object Detection ◽  
Saliency Detection ◽  
Contrast Ratio ◽  
Salient Object Detection ◽  
Color Images ◽  
Color Histogram ◽  
Detection Methods ◽  
Color Contrast ◽  
Salient Object ◽  
Selection Of

Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

Reconstruction of Objects from their Projections Using Orthogonal Functions

1973 ◽  
Vol 31 ◽  
pp. 268-269
Author(s):  
Elrnar Zeitler
Keyword(s):  
Unit Area ◽  
Three Dimensional ◽  
One Dimension ◽  
Spatial Density ◽  
Dimensional Representation ◽  
Orthogonal Functions ◽  
Object A ◽  
Plane Normal ◽  
Dimensional Object ◽  
Spatial Density Distribution

Considering any finite three-dimensional object, a “projection” is here defined as a two-dimensional representation of the object's mass per unit area on a plane normal to a given projection axis, here taken as they-axis. Since the object can be seen as being built from parallel, thin slices, the relation between object structure and its projection can be reduced by one dimension. It is assumed that an electron microscope equipped with a tilting stage records the projectionWhere the object has a spatial density distribution p(r,ϕ) within a limiting radius taken to be unity, and the stage is tilted by an angle 9 with respect to the x-axis of the recording plane.

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