3D Visual Saliency: An Independent Perceptual Measure or a Derivative of 2D Image Saliency?

While 3D visual saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and has been well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art 3D visual saliency methods remain poor at predicting human fixations. Cues emerging prominently from these experiments suggest that 3D visual saliency might associate with 2D image saliency. This paper proposes a framework that combines a Generative Adversarial Network and a Conditional Random Field for learning visual saliency of both a single 3D object and a scene composed of multiple 3D objects with image saliency ground truth to 1) investigate whether 3D visual saliency is an independent perceptual measure or just a derivative of image saliency and 2) provide a weakly supervised method for more accurately predicting 3D visual saliency.

Quality Metric Guided Portrait Line Drawing Generation From Unpaired Training Data.

Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired training data, which is costly and time-consuming to obtain. In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features; i.

Association of violence with urban points of interest.

The association between alcohol outlets and violence has long been recognised, and is commonly used to inform policing and licensing policies (such as staggered closing times and zoning). Less investigated, however, is the association between violent crime and other urban points of interest, which while associated with the city centre alcohol consumption economy, are not explicitly alcohol outlets. Here, machine learning (specifically, LASSO regression) is used to model the distribution of violent crime for the central 9 km2 of ten large UK cities.

Learning on 3D Meshes With Laplacian Encoding and Pooling.

3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, requiring careful design to capture relations in the data.

Line Drawings for Face Portraits From Photos Using Global and Local Structure Based GANs.

Despite significant effort and notable success of neural style transfer, it remains challenging for highly abstract styles, in particular line drawings. In this paper, we propose APDrawingGAN++, a generative adversarial network (GAN) for transforming face photos to artistic portrait drawings (APDrawings), which addresses substantial challenges including highly abstract style, different drawing techniques for different facial features, and high perceptual sensitivity to artifacts. To address these, we propose a composite GAN architecture that consists of local networks (to learn effective representations for specific facial features) and a global network (to capture the overall content).

Designing an Herbarium Digitisation Workflow with Built-In Image Quality Management.

Digitisation of natural history collections has evolved from creating databases for the recording of specimens' catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows.

Sparse Graph Regularized Mesh Color Edit Propagation.

Mesh color edit propagation aims to propagate the color from a few color strokes to the whole mesh, which is useful for mesh colorization, color enhancement and color editing, etc. Compared with image edit propagation, luminance information is not available for 3D mesh data, so the color edit propagation is more difficult on 3D meshes than images, with far less research carried out. This paper proposes a novel solution based on sparse graph regularization.

Structure-Preserving Neural Style Transfer.

State-of-the-art neural style transfer methods have demonstrated amazing results by training feed-forward convolutional neural networks or using an iterative optimization strategy. The image representation used in these methods, which contains two components: style representation and content representation, is typically based on high-level features extracted from pretrained classification networks. Because the classification networks are originally designed for object recognition, the extracted features often focus on the central object and neglect other details.

Self-Paced Balance Learning for Clinical Skin Disease Recognition.

Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less training samples than others. Most existing approaches aim to remedy the imbalanced number of instances among categories by resampling the majority and minority classes accordingly.

Subspace Clustering via Good Neighbors.

Finding the informative subspaces of high-dimensional datasets is at the core of numerous applications in computer vision, where spectral-based subspace clustering is arguably the most widely studied method due to its strong empirical performance. Such algorithms first compute an affinity matrix to construct a self-representation for each sample using other samples as a dictionary. Sparsity and connectivity of the self-representation play important roles in effective subspace clustering.

Automatic Example-based Image Colourisation using Location-Aware Cross-Scale Matching.

Given a reference colour image and a destination grayscale image, this paper presents a novel automatic colourisation algorithm that transfers colour information from the reference image to the destination image. Since the reference and destination images may contain content at different or even varying scales (due to changes of distance between objects and the camera), existing texture matching based methods can often perform poorly. We propose a novel cross-scale texture matching method to improve the robustness and quality of the colourisation results.

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship.

In this paper, we propose a unified framework to discover the number of clusters and group the data points into different clusters using subspace clustering simultaneously. Real data distributed in a high-dimensional space can be disentangled into a union of low-dimensional subspaces, which can benefit various applications. To explore such intrinsic structure, state-of-the-art subspace clustering approaches often optimize a self-representation problem among all samples, to construct a pairwise affinity graph for spectral clustering.

Measuring Shapes with Desired Convex Polygons.

In this paper we have developed a family of shape measures. All the measures from the family evaluate the degree to which a shape looks like a predefined convex polygon. A quite new approach in designing object shape based measures has been applied.

What's in a Smile? Initial Analyses of Dynamic Changes in Facial Shape and Appearance.

Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model.

Virtual Recovery of Content from X-Ray Micro-Tomography Scans of Damaged Historic Scrolls.

There is a large body of historical documents that are too fragile to be opened or unrolled, making their contents inaccessible. Recent improvements in X-ray scanning technology and computer vision techniques make it possible to perform a "virtual" unrolling of such documents. We describe a novel technique to process a stack of 3D X-ray images to identify the surface of parchment scrolls, unroll them, and create a visualization of their written contents.

When facial expressions do and do not signal minds: The role of face inversion, expression dynamism, and emotion type.

Recent research has linked facial expressions to mind perception. Specifically, Bowling and Banissy (2017) found that ambiguous doll-human morphs were judged as more likely to have a mind when smiling. Herein, we investigate 3 key potential boundary conditions of this "expression-to-mind" effect.

Dynamic Match Kernel With Deep Convolutional Features for Image Retrieval.

For image retrieval methods based on bag of visual words, much attention has been paid to enhancing the discriminative powers of the local features. Although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (CNN). Such images should not be considered as relevant pairs.

Robust Virtual Unrolling of Historical Parchment XMT Images.

We develop a framework to virtually unroll fragile historical parchment scrolls, which cannot be physically unfolded via a sequence of X-ray tomographic slices, thus providing easy access to those parchments whose contents have remained hidden for centuries. The first step is to produce a topologically correct segmentation, which is challenging as the parchment layers vary significantly in thickness, contain substantial interior textures and can often stick together in places. For this purpose, our method starts with linking the broken layers in a slice using the topological structure propagated from its previous processed slice.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit.

The influence of snoring, mouth breathing and apnoea on facial morphology in late childhood: a three-dimensional study.

Objective: To explore the relationship between the prevalence of sleep disordered breathing (SDB) and face shape morphology in a large cohort of 15-year-old children.

Design: Observational longitudinal cohort study

Setting: Avon Longitudinal Study of Parents and Children (ALSPAC), South West of England.

Participants: Three-dimensional surface laser scans were taken for 4784 white British children from the ALSPAC during a follow-up clinic.

Automated Registration of Multimodal Optic Disc Images: Clinical Assessment of Alignment Accuracy.

Purpose: To determine the accuracy of automated alignment algorithms for the registration of optic disc images obtained by 2 different modalities: fundus photography and scanning laser tomography.

Materials And Methods: Images obtained with the Heidelberg Retina Tomograph II and paired photographic optic disc images of 135 eyes were analyzed. Three state-of-the-art automated registration techniques Regional Mutual Information, rigid Feature Neighbourhood Mutual Information (FNMI), and nonrigid FNMI (NRFNMI) were used to align these image pairs.

A three-dimensional analysis of the effect of atopy on face shape.

Three-dimensional (3D) imaging technology has been widely used to analyse facial morphology and has revealed an influence of some medical conditions on craniofacial growth and morphology. The aim of the study is to investigate whether craniofacial morphology is different in atopic Caucasian children compared with controls. Study design included observational longitudinal cohort study.

The influence of asthma on face shape: a three-dimensional study.

Respiratory activity may have an influence on craniofacial development and interact with genetic and environmental factors. It has been suggested that certain medical conditions such as asthma have an influence on face shape. The aim of the study is to investigate whether facial shape is different in individuals diagnosed as having asthma compared with controls.