Sorry, you need to enable JavaScript to visit this website.

We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates.

Model based methods have gained popularity in the past few decades in reconstruction problems particularly when the measurement data is sparse. In model based inference, apart from a model for the measurements, there exists a model for the unknown signal to be reconstructed, called the prior model. Model based methods tend to do very well when the prior model is accurate and representative of real world behavior of the unknown signal.

Feature-based SLAM (Simultaneous Localization and Mapping) techniques rely on low-level contrast information extracted from images to detect and track keypoints. This process is known to be sensitive to changes in illumination of the environment that can lead to tracking failures. This paper proposes a multi-layered image representation (MLI) that computes and stores different contrast-enhanced versions of an original image. Keypoint detection is performed on each layer, yielding better robustness to light changes.

Camera-equipped drones have recently revolutionized aerial cinematography, allowing easy acquisition of impressive footage. Although they are currently manually operated, autonomous functionalities based on machine learning and computer vision are becoming popular. However, the emerging area of autonomous UAV filming has to face several challenges, especially when visually tracking fast and unpredictably moving targets. In the latter case, an important issue is how to determine the shot types that are achievable without risking failure of the 2D visual tracker.

Loop filters are used in video coding to remove artifacts or improve performance. Recent advances in deploying convolutional neural network (CNN) to replace traditional loop filters show large gains but with problems for practical application.First, different model is used for frames encoded with different quantization parameter (QP), respectively. It is expensive for hardware. Second, float points operation in CNN leads to inconsistency between encoding and decoding across different platforms. Third, redundancy within CNN model consumes precious computational resources.