mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

Abstract

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

Vibration signal diagnosis and conditional health monitoring of motor used in biomedical applications using Internet of Things environment

“…Sometimes in circumstances, the reaction of the shock and/or vibration environment will play a significant part in the overall damage. These dynamic effects can cause the structure to resist substantial amplitudes and/or acceleration levels, whether generated by the environment, manufacture, shipping, handling, operation, or any other source [13,14]. As a result, the issue of shielding PCBs against the effects of acute vibration should be addressed.…”

Refining disparity maps using deep learning and edge-aware smoothing filter

“…Figure 1. The stages of the stereo vision framework [8] Local approaches utilise disparity based on the relationship between pixel intensities (grayscale, RGB colours, texture patterns) inside a certain local support window based on the connections between pixels in close proximity to one another in the corresponding image [12]. These methods are sometimes referred to as window-based or region-based approaches.…”