Varying Illumination Environment Research Articles

Automated camera-exposure control for robust localization in varying illumination environments

Automated camera-exposure control for robust localization in varying illumination environments

Approach to model human appearance based on sparse representation for human tracking in surveillance

In human tracking, sparse representation successfully localises the human in a video with minimal reconstruction error using target templates. However, the state-of-the-art approaches use colour and local appearance of a human to discriminate the human from the background regions, and hence fail when the human is occluded and appears in the varying illumination environment. In this study, a robust tracking algorithm is proposed that utilises gradient orientation and fine and coarse sparse representation of the target template. Sparse representation-based human appearance model utilises weighted gradient orientation that is insensitive to illumination variation. Coarse and fine representation of sparse code facilitates tracking under varying scales. Subspace learning from image gradient orientation is enforced with occlusion detection during the dictionary updation stage to capture the visual characteristics of the local human appearance that supports tracking under partial occlusion with lesser tracking error. The proposed human tracking algorithm is evaluated on various datasets and shows efficient human tracking performance when compared to the other state-of-the-art approaches. Furthermore, the proposed human tracking algorithm is suitable for surveillance applications.

English

English

Anisotropic Superpixel Generation Based on Mahalanobis Distance

AbstractSuperpixels have been widely used as a preprocessing step in various computer vision tasks. Spatial compactness and color homogeneity are the two key factors determining the quality of the superpixel representation. In this paper, these two objectives are considered separately and anisotropic superpixels are generated to better adapt to local image content. We develop a unimodular Gaussian generative model to guide the color homogeneity within a superpixel by learning local pixel color variations. It turns out maximizing the log‐likelihood of our generative model is equivalent to solving a Centroidal Voronoi Tessellation (CVT) problem. Moreover, we provide the theoretical guarantee that the CVT result is invariant to affine illumination change, which makes our anisotropic superpixel generation algorithm well suited for image/video analysis in varying illumination environment. The effectiveness of our method in image/video superpixel generation is demonstrated through the comparison with other state‐of‐the‐art methods.

Development of Correspondence Algorithm for Illumination Varying Dynamic Image Sequence using Symbolic Data Analysis: A Novel Approach

This paper presents a novel symbolic data analysis based correspondence algorithm to identify the correspondence of illumination varying multiple object image sequence for the dynamic image analysis. The correspondence is the matching of the similar data sets or entities or the features such as the region based global features, points, corners, edges between a set of images. The Correspondence of illumination varying multispectral, temporal multiple objects using symbolic data analysis of objects in the dynamic images is a highly challenging problem. The determining Correspondence of features is a high dimensional NP-hard problem, because of their exhaustive search for the features in the sequence of image frames. The current algorithm works on the basis of successive frame difference based segmentation. The minimum boundary rectangle constructed for segmented moving object region. Mean of the rectangle is considered as one of the numeric feature. Next the sequence converted to binary sequence based on the threshold and 8-connectivity of perimeter is another non numeric feature. Finally establish the correspondence using canonical symbolic data analysis. This work proposes the rectangular correspondence window, generated dynamically which depends on the size and the number of objects in the current frame. Here we have considered mean of minimum boundary rectangle and perimeter of the segmented region as the features. The experiment is conducted with single object in the scene and multiple objects in the scene of illumination varying, noisy image sequence. This experiment is carried out on the binary, gray and RGB color dynamic image sequence. This correspondence algorithm is especially suitable for tracking in indoor images illuminated by unconstrained multiple light sources, objects in the varying illumination environment, noisy image sequence, video surveillance and slow moving dynamic image sequence for indoor scenes of the constant background.

Adaptive feature representation for robust face recognition using context-aware approach

In this paper, we investigate how to preprocess and accurately identify features from both normal and bad input images, for robust face recognition under uneven illumination environments. Bad illumination is the most challenging problem when implementing robust face recognition systems. From extensive experiments, we found that the performance of individual filtering methods for image enhancement is highly dependent upon application environments. For example, retinex provides good performance under bad illumination. However, it provides very poor performance under normal illumination. On the other hand, histogram equalization provides sufficiently good performance for normal images, however, performance falls dramatically under bad illumination. Since no prior knowledge of the system-working environment can be assumed, the proposed method tries to provide adaptive preprocessing as well as feature representation configuration by exploring the filter combination based on illumination context-awareness. The proposed method has been tested on Inha and FERET image datasets at the preprocessing and feature representation stages for robust face recognition under uneven illumination. Extensive experiments show the proposed system can achieve exceptional performance in varying illumination environments.