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Reliable collision avoidance is one of the main requirements for autonomous driving.
Hence, it is important to correctly estimate the states of an unknown number of static and dynamic objects in real-time.
Here, data association is a major challenge for every multi-target tracker.
We propose a novel multi-target tracker called Greedy Dirichlet Process Filter (GDPF) based on the non-parametric Bayesian model called Dirichlet Processes and the fast posterior computation algorithm Sequential Updating and Greedy Search (SUGS).

We evaluate the performance of objective quality metrics for high dynamic range (HDR) image coding that uses the transfer function (TF) of the Hybrid Log-Gamma (HLG) method. Previous evaluations of objective metrics for HDR image coding have studied which of them are reliable predictors of perceived quality; however, in those tests, all the non-linear transforms used both for encoding and by the best-performing metrics are essentially very similar and based on visual perception data of detection thresholds for lightness variations.

In this paper, we propose a single image super-resolution with limited number of filters based on RAISR. RAISR is well known as rapid and accurate super-resolution method which utilizes 864 filters for upscaling. This super-resolution idea utilizes the filter learned with sufficient training set. To get low cost of calculation and comparable image quality with other highly accurate super-resolution methods, the patch of input image is classified into classes by simple hash calculation.

Classification subnetwork and box regression subnetwork are
essential components in deep networks for object detection.
However, we observe a contradiction that before NMS, some
better localized detections do not correspond to higher classification confidences, and vice versa. This contradiction exists because classification confidences can not fully reflect the
localization-quality (loc-quality) of each detection. In this
work, we propose the Localization-quality Estimation embedded Detector abbreviated as LED, and a corresponding

In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter.
In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources.

We propose a deep learning framework for few-shot image classification, which exploits information across label semantics and image domains, so that regions of interest can be properly attended for improved classification. The proposed semantics-guided attention module is able to focus on most relevant regions in an image, while the attended image samples allow data augmentation and alleviate possible overfitting during FSL training. Promising performances are presented in our experiments, in which we consider both closed and open-world settings.

In recent years, light fields have become a major research topic and their applications span across the entire spectrum of classical image processing. Among the different methods used to capture a light field are the lenslet cameras, such as those developed by Lytro. While these cameras give a lot of freedom to the user, they also create light field views that suffer from a number of artefacts. As a result, it is common to ignore a significant subset of these views when doing high-level light field processing.