Boosting Object Recognition in Point Clouds by Saliency Detection

Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications. Local descriptors are an amenable choice whenever the 6 DoF pose of recognized objects should also be estimated. However, the pipeline for this kind of descriptors is highly time-consuming. In this work, we propose an update to the traditional pipeline, by adding a preliminary filtering stage referred to as saliency boost. We perform tests on a standard object recognition benchmark by considering four keypoint detectors and four local descriptors, in order to compare time and recognition performance between the traditional pipeline and the boosted one. Results on time show that the boosted pipeline could turn out up to 5 times faster, with the recognition rate improving in most of the cases and exhibiting only a slight decrease in the others. These results suggest that the boosted pipeline can speed-up processing time substantially with limited impacts or even benefits in recognition accuracy.Comment: International Conference on Image Analysis and Processing (ICIAP) 201

Intelligent visual media processing: when graphics meets vision

The computer graphics and computer vision communities have been working closely together in recent years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing tools, such as deep neural networks, provide e�ective ways for learning how to deal with heterogeneous visual data; iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques bene�t computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions

Deep Learning Methods for 3D Aerial and Satellite Data

Recent advances in digital electronics have led to an overabundance of observations from electro-optical (EO) imaging sensors spanning high spatial, spectral and temporal resolution. This unprecedented volume, variety, and velocity is overwhelming our capacity to manage and translate that data into actionable information. Although decades of image processing research have taken the human out of the loop for many important tasks, the human analyst is still an irreplaceable link in the image exploitation chain, especially for more complex tasks requiring contextual understanding, memory, discernment, and learning. If knowledge discovery is to keep pace with the growing availability of data, new processing paradigms are needed in order to automate the analysis of earth observation imagery and ease the burden of manual interpretation. To address this gap, this dissertation advances fundamental and applied research in deep learning for aerial and satellite imagery. We show how deep learning---a computational model inspired by the human brain---can be used for (1) tracking, (2) classifying, and (3) modeling from a variety of data sources including full-motion video (FMV), Light Detection and Ranging (LiDAR), and stereo photogrammetry. First we assess the ability of a bio-inspired tracking method to track small targets using aerial videos. The tracker uses three kinds of saliency maps: appearance, location, and motion. Our approach achieves the best overall performance, including being the only method capable of handling long-term occlusions. Second, we evaluate the classification accuracy of a multi-scale fully convolutional network to label individual points in LiDAR data. Our method uses only the 3D-coordinates and corresponding low-dimensional spectral features for each point. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6\%. Finally, we validate the prediction capability of our neighborhood-aware network to model the bare-earth surface of LiDAR and stereo photogrammetry point clouds. The network bypasses traditionally-used ground classifications and seamlessly integrate neighborhood features with point-wise and global features to predict a per point Digital Terrain Model (DTM). We compare our results with two widely used softwares for DTM extraction, ENVI and LAStools. Together, these efforts have the potential to alleviate the manual burden associated with some of the most challenging and time-consuming geospatial processing tasks, with implications for improving our response to issues of global security, emergency management, and disaster response

Saliency-based approaches for multidimensional explainability of deep networks

In deep learning, visualization techniques extract the salient patterns exploited by deep networks to perform a task (e.g. image classification) focusing on single images. These methods allow a better understanding of these complex models, empowering the identification of the most informative parts of the input data. Beyond the deep network understanding, visual saliency is useful for many quantitative reasons and applications, both in the 2D and 3D domains, such as the analysis of the generalization capabilities of a classifier and autonomous navigation. In this thesis, we describe an approach to cope with the interpretability problem of a convolutional neural network and propose our ideas on how to exploit the visualization for applications like image classification and active object recognition. After a brief overview on common visualization methods producing attention/saliency maps, we will address two separate points: firstly, we will describe how visual saliency can be effectively used in the 2D domain (e.g. RGB images) to boost image classification performances: as a matter of fact, visual summaries, i.e. a compact representation of an ensemble of saliency maps, can be used to improve the classification accuracy of a network through summary-driven specializations. Then, we will present a 3D active recognition system that allows to consider different views of a target object, overcoming the single-view hypothesis of classical object recognition, making the classification problem much easier in principle. Here we adopt such attention maps in a quantitative fashion, by building a 3D dense saliency volume which fuses together saliency maps obtained from different viewpoints, obtaining a continuous proxy on which parts of an object are more discriminative for a given classifier. Finally, we will show how to inject this representations in a real world application, so that an agent (e.g. robot) can move knowing the capabilities of its classifier