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Accurate and robust visual localization under a wide range of viewing condition variations including season and illumination changes, as well as weather and day-night variations, is the key component for many computer vision and robotics applications. Under these conditions, most traditional methods would fail to locate the camera. In this paper we present a visual localization algorithm that combines structure-based method and image-based method with semantic information.

Spectral methods such as the improved Fourier Mellin Invariant (iFMI) transform have proved to be faster, more robust

Remote detection of the cardiac pulse has a number of applications in sports and medicine, and can be used to determine an individual’s physiological state. Previous approaches to estimate Heart Rate (HR) from video require the subject to remain stationary and employ background information to eliminate illumination interferences. The present research proposes a spectral reflectance-based novel illumination rectification method to eliminate illumination variations in the video.

Deep learning approaches have been established as the main methodology for video classification and recognition. Recently, 3-dimensional convolutions have been used to achieve state-of-the-art performance in many challenging video datasets. Because of the high level of complexity of these methods, as the convolution operations are also extended to an additional dimension in order to extract features from it as well, providing a visualization for the signals that the network interpret as informative, is a challenging task.

Gait recognition is a leading remote-based identification method, suitable for applications in forensic cases, surveillance, and medical studies. We present Glidar3DJ, a model-based gait recognition methodology, using a skeleton model extracted from sequences generated by a single flash lidar camera. Compared with Kinect, a flash lidar camera has a drastically extended range (> 1000 meters) and its performance is not affected in outdoor.

Bacterial segmentation poses significant challenges due to
lack of structure, poor imaging resolution, limited contrast
between touching cells and high density of cells that overlap.
Although there exist bacterial segmentation algorithms in the

existing art, they fail to delineate cells in dense biofilms,
especially in 3D imaging scenarios in which the cells are growing

and subdividing in an unstructured manner. A graph-based