Quantitative Analysis of Saliency Models

Previous saliency detection research required the reader to evaluate performance qualitatively, based on renderings of saliency maps on a few shapes. This qualitative approach meant it was unclear which saliency models were better, or how well they compared to human perception. This paper provides a quantitative evaluation framework that addresses this issue. In the first quantitative analysis of 3D computational saliency models, we evaluate four computational saliency models and two baseline models against ground-truth saliency collected in previous work.Comment: 10 page

Automatic Landmarking for Non-cooperative 3D Face Recognition

This thesis describes a new framework for 3D surface landmarking and evaluates its performance for feature localisation on human faces. This framework has two main parts that can be designed and optimised independently. The first one is a keypoint detection system that returns positions of interest for a given mesh surface by using a learnt dictionary of local shapes. The second one is a labelling system, using model fitting approaches that establish a one-to-one correspondence between the set of unlabelled input points and a learnt representation of the class of object to detect. Our keypoint detection system returns local maxima over score maps that are generated from an arbitrarily large set of local shape descriptors. The distributions of these descriptors (scalars or histograms) are learnt for known landmark positions on a training dataset in order to generate a model. The similarity between the input descriptor value for a given vertex and a model shape is used as a descriptor-related score. Our labelling system can make use of both hypergraph matching techniques and rigid registration techniques to reduce the ambiguity attached to unlabelled input keypoints for which a list of model landmark candidates have been seeded. The soft matching techniques use multi-attributed hyperedges to reduce ambiguity, while the registration techniques use scale-adapted rigid transformation computed from 3 or more points in order to obtain one-to-one correspondences. Our final system achieves better or comparable (depending on the metric) results than the state-of-the-art while being more generic. It does not require pre-processing such as cropping, spike removal and hole filling and is more robust to occlusion of salient local regions, such as those near the nose tip and inner eye corners. It is also fully pose invariant and can be used with kinds of objects other than faces, provided that labelled training data is available

Generating detailed saliency maps using model-agnostic methods

The emerging field of Explainable Artificial Intelligence focuses on researching methods of explaining the decision making processes of complex machine learning models. In the field of explainability for Computer Vision, explanations are provided as saliency maps, which visualize the importance of individual pixels of the input w.r.t. the model's prediction. In this work we focus on a perturbation-based, model-agnostic explainability method called RISE, elaborate on observed shortcomings of its grid-based approach and propose two modifications: replacement of square occlusions with convex polygonal occlusions based on cells of a Voronoi mesh and addition of an informativeness guarantee to the occlusion mask generator. These modifications, collectively called VRISE (Voronoi-RISE), are meant to, respectively, improve the accuracy of maps generated using large occlusions and accelerate convergence of saliency maps in cases where sampling density is either very low or very high. We perform a quantitative comparison of accuracy of saliency maps produced by VRISE and RISE on the validation split of ILSVRC2012, using a saliency-guided content insertion/deletion metric and a localization metric based on bounding boxes. Additionally, we explore the space of configurable occlusion pattern parameters to better understand their influence on saliency maps produced by RISE and VRISE. We also describe and demonstrate two effects observed over the course of experimentation, arising from the random sampling approach of RISE: "feature slicing" and "saliency misattribution". Our results show that convex polygonal occlusions yield more accurate maps for coarse occlusion meshes and multi-object images, but improvement is not guaranteed in other cases. The informativeness guarantee is shown to increase the convergence rate without incurring a significant computational overhead.Comment: 85 pages, 70 figures, Master's thesis, defended on 2021-12-23 (Gdansk University of Technology

Learning Dense 3D Models from Monocular Video

Reconstructing dense, detailed, 3D shape of dynamic scenes from monocular sequences is a challenging problem in computer vision. While robust and even real-time solutions exist to this problem if the observed scene is static, for non-rigid dense shape capture current systems are typically restricted to the use of complex multi-camera rigs, taking advantage of the additional depth channel available in RGB-D cameras, or dealing with specific shapes such as faces or planar surfaces. In this thesis, we present two pieces of work for reconstructing dense generic shapes from monocular sequences. In the first work, we propose an unsupervised approach to the challenging problem of simultaneously segmenting the scene into its constituent objects and reconstructing a 3D model of the scene. The strength of our approach comes from the ability to deal with real-world dynamic scenes and to handle seamlessly different types of motion: rigid, articulated and non-rigid. We formulate the problem as a hierarchical graph-cuts based segmentation where we decompose the whole scene into background and foreground objects and model the complex motion of non-rigid or articulated objects as a set of overlapping rigid parts. To validate the capability of our approach to deal with real-world scenes, we provide 3D reconstructions of some challenging videos from the YouTube Objects and KITTI dataset, etc. In the second work, we propose a direct approach for capturing the dense, detailed 3D geometry of generic, complex non-rigid meshes using a single camera. Our method makes use of a single RGB video as input; it can capture the deformations of generic shapes; and the depth estimation is dense, per-pixel and direct. We first reconstruct a dense 3D template of the shape of the object, using a short rigid sequence, and subsequently perform online reconstruction of the non-rigid mesh as it evolves over time. In our experimental evaluation, we show a range of qualitative results on novel datasets and quantitative comparison results with stereo reconstruction