When the scene to be recovered is static and contains unstructured illumination change, temporal stereo produces significantly better depth estimates than does traditional spatial stereo.
To show the practical utility of the framework, we develop two new algorithms for depth estimation: depth from unstructured illumination change and depth estimation in dynamic scenes.
In real applications, illumination change is one of the most important factors that significantly affect the performance of face recognition algorithms.
The problem is challenging owing to failures arising because of variation in illumination, change in pose, size or scale, camera motion and partial or full occlusion.
The network jointly detects multi-view faces and estimates head pose even under poor environment conditions such as illumination change, vibration, large pose change, and occlusion.
To overcome the difficulties typically observed in this scenario, such as illumination change and object deformation, an online ensemble learning algorithm, which is adaptive to the non-stationary environment, is adopted.
Experiments are carried out on several challenging sequences and results show that our method performs well in terms of object tracking, even in the presence of occlusion, deformation, illumination change, moving camera and spatial distracter.
In this paper, focusing on the illumination change challenge, we propose a deep network model which takes both visible light image and near-infrared image into account to perform face recognition.
The experimental results demonstrate that the model is very effective in real-world scenarios and perform much better in terms of illumination change than other state-of-the-art models.
Most of the Conventional FET based photosensors that are available utilizes threshold voltage as the parameter for sensitivity comparison but in this proposed sensor, under illumination change in the conductance resulting in variation of the subthreshold current is considered to be the sensitivity parameter.
In this paper, we report a new technique which: (i) is robust against illumination change, (ii) offers discrimination ability to detect color change between faces having similar shape, and (iii) is specifically designed to detect red colored stains (i.e. facial bleeding).
