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In this paper, a Hessian matrix based multi-focus image fusion method is proposed. First, the integral map is introduced for fast compute the Hessian matrix of source images at different scales, and the multi-scale Hessian matrix of source image is obtained. Second, the multi-scale Hessian matrix is used to decompose each source image into two kinds of regions: the feature and background regions.

Collective activity recognition, which tells what activity a group of people is performing, is a cutting-edge research topic in computer vision. Different from action performed by individuals, collective activity needs to consider the complex interactions among different people. However, most previous works require exhaustive annotations such as accurate label information of individual actions, pairwise interactions, and poses, which could not be easily available in practice. 

The prevailing characteristics of micro-videos result in the less descriptive power of each modality. The micro-video representations, several pioneer efforts proposed, are limited in implicitly exploring the consistency between different modality information but ignore the complementarity.

A problem deeply investigated by multimedia forensics researchers is that of detecting which device has been used to capture a video. This enables us to trace down the owner of a video sequence, which proves extremely helpful to solve copyright infringement cases as well as to fight distribution of illicit material (e.g., child exploitation clips and terroristic threats).

The challenges of real world applications of the laser detection and ranging (Lidar) three-dimensional (3-D) imaging require specialized algorithms. In this paper, a new reconstruction algorithm for single-photon 3-D Lidar images is presented that can deal with multiple tasks. 

Binary tomography is concerned with the recovery of binary images from a few of their projections (i.e., sums of the pixel values along various directions). To reconstruct an image from noisy projection data, one can pose it as a constrained least-squares problem.

Automatic evaluation of singing quality can be done with the help of a reference singing or the digital sheet music of the song. However, such a standard reference is not always available. In this article, we propose a framework to rank a large pool of singers according to their singing quality without any standard reference.