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Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation. [ABSTRACT FROM AUTHOR]

Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation.

With the decrease of agricultural labors and the increase in production costs, harvesting robots have become a research hotspot in recent years. To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions, an image segmentation algorithm based on superpixel was proposed. Efficient simple linear iterative clustering (SLIC) algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels. The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks (BPNN) classifier in the next step. The adjacency information of superpixels was considered by calculating the global-local binary pattern (LBP) in R component images when extracting texture features. To accelerate the classification process, the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels. These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77% and pixel-level accuracy of 94.96%, while the average time to segment one image was 0.4778 s. Therefore, the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently. [ABSTRACT FROM AUTHOR]

• We propose an RGB-T salient object detection network with salient-illumination awareness. • We propose a salient-illumination awareness estimator (SIAE) and an illumination semantic complement module (ISCM). • We propose an illumination perception fusion module that fuses different features under the guidance of illumination. • We construct a variable illumination dataset VI-RGBT3500 to verify the validity and rationality of our method. RGB-thermal salient object detection (RGB-T SOD) has rapidly developed and achieved excellent detection results. Unfortunately, the significant impact of illumination on salient object detection has not yet been adequately addressed, which results in mediocre detection performance of current methods in variable illumination scenes. To overcome the influence of illumination in variable illumination scenes, we present an RGB-T salient object detection network with salient-illumination awareness. Firstly, we propose a salient-illumination awareness estimator (SIAE) to evaluate the illumination condition of the RGB-T images. To make the generated illumination representation more comprehensively measure the quality of the images affected by illumination, we use saliency supervision and illumination semantic complement module (ISCM) to add salient awareness and semantic information to the illumination representation, respectively. Then, under the guidance of illumination representation, we use an illumination perception fusion module (IPFM) to complete the fusion of visible light and thermal infrared modality and predict salient objects. Next, to verify the rationality of our design idea and the validity of our proposed method, we construct a variable illumination dataset VI-RGBT3500 for experimental verification. We conduct many experiments on the VI-RGBT3500 dataset as well as existing datasets. The experimental results show that our method can achieve excellent detection results in variable illumination scenes. Moreover, excellent detection results can also be achieved in normal illumination scenes. The dataset and code are available at: https://github.com/VDT-2048/SIA. [ABSTRACT FROM AUTHOR]

We consider a unified variational PDEs model to solve the optic flow problem for large displacements and varying illumination. Although, the energy functional is nonconvex and severely nonlinear, we show that the model offers a well suited framework to extend the efficient methods we used for small displacements. In particular, we resort to an adaptive control of the diffusion and the illumination coefficients which allows us to preserve the edges and to obtain a sparse vector field. We develop a combined space-time parallel programming strategy based on a Schwarz domain decomposition method to speed up the computations and to handle high resolution images, and the parareal algorithm, to enhance the speedup and to achieve a lowestenergy local minimum. This full parallel method gives raise to several iterative schemes and allows us to obtain a good balance between several objectives, e.g. accuracy, cost reduction, time saving and achieving the "best" local minimum. We present several numerical simulations to validate the different algorithms and to compare their performances. [ABSTRACT FROM AUTHOR]

Background subtraction is a key prerequisite for intelligent video surveillance, but most of the methods employed are still affected by dynamic changes in the illumination conditions, e.g., shadows cast by passing clouds occur frequently in outdoor scenes. To resolve this problem, a novel approach based on the underlying structure of the difference image is introduced in this study. In particular, local binary patterns (LBPs) are computed based on the frame differencing result, i.e., moving LBP, and then compared with the background model, which is updated according to an online interpolation scheme, in order to determine whether the current pixel belongs to the background. An important advantage of the proposed method is that it efficiently smoothes unexpected noise between frames while also preserving the boundaries of the moving objects by using an edge-aware filtering technique. Experimental results obtained using two benchmark data sets demonstrated that the proposed method is more robust to variable illumination in outdoor scenes compared with previously proposed approaches. [ABSTRACT FROM AUTHOR]

When an unknown object with Lambertian reflectance is viewed orthographically, there is an implicit ambiguity in determining its 3-d structure: we show that the object's visible surface f(x, y) is indistinguishable from a “generalized bas-relief” transformation of the object's geometry, $$ {\bar f} $$ (x, y) = λf(x, y) + μx + νy, and a corresponding transformation on the object's albedo. For each image of the object illuminated by an arbitrary number of distant light sources, there exists an identical image of the transformed object illuminated by similarly transformed light sources. This result holds both for the illuminated regions of the object as well as those in cast and attached shadows. Furthermore, neither small motion of the object, nor of the viewer will resolve the ambiguity in determining the flattening (or scaling) λ of the object's surface. Implications of this ambiguity on structure recovery and shape representation are discussed. [ABSTRACT FROM AUTHOR]

The appearance of an object depends on both the viewpoint from which it is observed and the light sources by which it is illuminated. If the appearance of two objects is never identical for any pose or lighting conditions, then–in theory–the objects can always be distinguished or recognized. The question arises: What is the set of images of an object under all lighting conditions and pose? In this paper, we consider only the set of images of an object under variable illumination, including multiple, extended light sources and shadows. We prove that the set of n-pixel images of a convex object with a Lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in IRn and that the dimension of this illumination cone equals the number of distinct surface normals. Furthermore, the illumination cone can be constructed from as few as three images. In addition, the set of n-pixel images of an object of any shape and with a more general reflectance function, seen under all possible illumination conditions, still forms a convex cone in IRn. Extensions of these results to color images are presented. These results immediately suggest certain approaches to object recognition. Throughout, we present results demonstrating the illumination cone representation. [ABSTRACT FROM AUTHOR]