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This paper presents an empirical Bayesian method to estimate regularisation parameters in imaging inverse problems. The method calibrates regularisation parameters directly from the observed data by maximum marginal likelihood estimation, and is useful for inverse problems that are convex. A main novelty is that maximum likelihood estimation is performed efficiently by using a stochastic proximal gradient algorithm that is driven by two proximal Markov chain Monte Carlo samplers, intimately combining modern optimisation and sampling techniques.

A new local image enhancement technique is proposed.
It is based on a logarithmic mapping function
that is adapted to the luminance
characteristics of the neighborhood of each pixel.
The method permits to simultaneously increase the luminance in dark regions
and decrease it in bright regions. The result is an image with an overall improved
contrast.

In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights.

Image aesthetic assessment is important for finding well taken and appealing photographs but is challenging due to the ambiguity and subjectivity of aesthetic criteria. We develop the pairwise aesthetic comparison network (PAC-Net), which consists of two parts: aesthetic feature extraction and pairwise feature comparison. To alleviate the ambiguity and subjectivity, we train PAC-Net to learn the relative aesthetic ranks of two images by employing a novel loss function, called aesthetic-adaptive cross entropy loss.

Traditional methods for multiple object tracking usually consider features at image level and reason about simple space and time constraints. However, in this paper we propose a multiple object tracker based on LSTM network to learn temporally correlated features. Our tracker learns features on velocity, position and appearance aspects of the objects to improve tracking accuracy. In order to deal with occlusion problems, velocity model is also used to predict the lost detections for the occluded identities.

In this work, we propose a novel, multi-threshold method for lip contour extraction from high-resolution static lip images acquired in an uncontrolled environment and emphasizing on the contour details.
The introduced method is a “Divide and conquer” approach. We broke the problem of lip contour extraction into two base sub-problems of locating the upper and lower lip contours using a novel threshold selection algorithm and combining them into the solution for the original lip contour.

Prostate cancer is one of the types of cancer with the highest incidence in humans. In particular, prostate cancer is the main cause of death from cancer in men over 70 years of age. The automatic analysis of histological images is nowadays a key factor for helping doctors in the diagnosis task. In this paper, we present granulometries as a novel image descriptor to identify abnormal patterns in the prostatic tissue. The morphological alteration suffered by the main structures of pathological glands are registered by the proposed descriptor and achieved in a feature vector.