Maximal similarity based region classification method through local image region descriptors and Bhattacharyya coefficient-based distance: Application to horizon line detection using wide-angle camera

A precise bare simulation approach to the minimization of some distances. Foundations

“…There is a vast literature on very recent applications of the Bhattacharyya coefficient, for instance it appears exemplarily in Peng & Li [289] for object tracking from successive video frames, Ayed et al [26] for efficient graph cut algorithms, Patra et al [287] for collaborative filtering in sparse data, El Merabet et al [119] for region classification in intelligent transport systems in order to compensate the lack of performance of Global Navigation Satellites Systems, Chiu et al [86] for the design of interactive mobile augmented reality systems, Noh et al [274] for dimension reduction in interacting fluid flow models, Bai et al [29] for material defect detection through ultrasonic array imaging, Dixit & Jain [115] for the design of recommender systems on highly sparse context aware datasets, Guan et al [143] for visible light positioning methods based on image sensors, Lin et al [220] for probabilistic representation of color image pixels, Chen et al [80] for distributed compressive video sensing, Jain et al [162] for the enhancement of multistage user-based collaborative filtering in recommendation systems, Pascuzzo et al [285] for brain-diffusion-MRI based early diagnosis of the sporadic Creutzfeldt-Jakob disease, Sun et al [351] for the design of automatic detection methods multitemporal (e.g. landslide) point clouds, Valpione et al [377] for the investigation of T cell dynamics in immunotherapy, Wang et al [387] for the tracking and prediction of downbursts from meteorological data, Xu et al [403] for adaptive distributed compressed video sensing for coal mine monitoring, Zhao et al [424] for the shared sparse machine learning of the affective content of images, Chen et al [82] for image segmentation and domain partitioning, De Oliveira et al [105] for the prediction of cell-penetrating peptides, Eshaghi et al [122] for the identification of multiple sclerosis subtypes through machine learning of brain MRI scans, Feng et al [125] for improvements of MRI-based detection of epilepsy-causing cortical malformations, Hanli et al [153] for designing pilot protection schemes for transmission lines, Jiang et al [170] for flow-assisted visual tracking through event cameras, Lysiak & Szmajda …”

An Improved Kernelized Correlation Filter Based Visual Tracking Method

“…If the KCF tracker still cannot accurately track the target after the update, then template matching is performed. In this paper, the Bhattacharyya coefficient ( , ) is used to calculate similarity between the target and the candidate model during the template matching process, namely [26],…”

Local Gradient Dual Coding Book (LGDCB) Framework for Effective Texture Classification

“…Texture analysis and texture classification are widely used in computer vision, image processing, and pattern recognition domains and it is one of the long standing problems of the research. The applications like object and sense recognition [2], pedestrian detection [3], sky extraction from fishy images [5], computer assisted diagnosis [6], motion and activity analysis and many more falls in the category of texture analysis. The texture feature extraction is to be carried out more precisely to extract significant texture features.…”