Image Analysis and Processing – ICIAP 2022

Table of Contents

1. Frontmatter

2. Brave New Ideas

   1. Frontmatter

   2. A Lightweight Model for Satellite Pose Estimation

      Pierluigi Carcagnì, Marco Leo, Paolo Spagnolo, Pier Luigi Mazzeo, Cosimo Distante

      Abstract

      In this work, a study on computer vision techniques for automating rendezvous manoeuvres in space has been carried out. A lightweight algorithm pipeline for achieving the 6 degrees of freedom (DOF) object pose estimation, i.e. relative position and attitude, of a spacecraft in a non-cooperative context using a monocular camera has been studied. In particular, the considered lite architecture has been never exploited for space operations and it allows to be compliant with operational constraints, in terms of payload and power, of small satellite platforms. Experiments were performed on a benchmark Satellite Pose Estimation Dataset of synthetic and real spacecraft imageries specifically introduced for the challenging task of the 6DOF object pose estimation in space. Extensive comparisons with existing approaches are provided both in terms of reliability/accuracy and in terms of model size that ineluctably affect resource requirements for deployment on space vehicles.

   3. Imitation Learning for Autonomous Vehicle Driving: How Does the Representation Matter?

      Antonio Greco, Leonardo Rundo, Alessia Saggese, Mario Vento, Antonio Vicinanza

      Abstract

      Autonomous vehicle driving is gaining ground, by receiving increasing attention from the academic and industrial communities. Despite this considerable effort, there is a lack of a systematic and fair analysis of the input representations by means of a careful experimental evaluation on the same framework. To this aim, this work proposes the first comprehensive, comparative analysis of the most common inputs that can be processed by a conditional imitation learning (CIL) approach. With more details, we considered the combinations of raw and processed data—namely RGB images, depth (D) images and semantic segmentation (S)—to be assessed as inputs of the well-established Conditional Imitation Learning with ResNet and Speed prediction (CILRS) architecture. We performed a benchmark analysis, endorsed by statistical tests, on the CARLA simulator to compare the considered configurations. The achieved results showed that RGB outperformed the other monomodal inputs, in terms of success rate on the most popular benchmark NoCrash. However, RGB did not generalize well when tested on different weather conditions; overall, the best multimodal configuration was a combination of the RGB image and semantic segmentation inputs (i.e., RGBS) compared to the others, especially in regular and dense traffic scenarios. This confirms that an appropriate fusion of multimodal sensors is an effective approach in autonomous vehicle driving.

   4. LessonAble: Leveraging Deep Fakes in MOOC Content Creation

      Ciro Sannino, Michela Gravina, Stefano Marrone, Giuseppe Fiameni, Carlo Sansone

      Abstract

      This paper introduces LessonAble, a pipelined methodology leveraging the concept of Deep Fakes for generating MOOC (Massive Online Open Course) visual contents directly from a lesson narrative. To achieve this, the proposed pipeline consists of three main modules: audio generation, video generation and lip-syncing. In this work, we use the NVIDIA Tacotron2 Text-to-Speech model to generate custom speech from text, adapt the famous First Order Motion Model to generate the video sequence from different driving sequences and target images, and modify the Wav2Lip model to deal with lip-syncing. Moreover, we introduce some novel strategies to support the use of markdown-like formatting to guide the pipeline in the generation of expression aware (i.e. curious, happy, etc.) contents. Despite the use and adaptation of third parties modules, developing such a pipeline presented interesting challenges, all analysed and reported in this work. The result is an extremely intuitive tool to support MOOC content generation.

   5. An Intelligent Scanning Vehicle for Waste Collection Monitoring

      Georg Waltner, Malte Jaschik, Alfred Rinnhofer, Horst Possegger, Horst Bischof

      Abstract

      While many industries have adopted digital solutions to improve ecological footprints and optimize services, new technologies have not yet found broad acceptance in waste management. In addition, past efforts to motivate households to improve waste separation have shown limited success. To reduce greenhouse gas emissions as part of a greater plan for fighting climate change, institutions like the European Union (EU) undertake strong efforts. In this context, developing intelligent digital technologies for waste management helps to increase the recycling rate and as a consequence reduces greenhouse gas emissions. Within this work, we propose an innovative computer vision system that is able to assess the residential waste in real-time and deliver individual feedback to the households and waste management companies with the aim of increasing recycling rates and thus reducing emissions. It consists of two core components: A compact scanning hardware designed specifically for rugged environments like the innards of a garbage truck and an intelligent software that applies a convolutional neural network (CNN) to automatically identify the composition of the waste which was dumped into the truck and subsequently delivers the results to a web portal for further analysis and communication. We show that our system can impact household separation behavior and result in higher recycling rates leading to noticeable reduction of CO₂ emissions in the long term.

   6. Morphological Galaxies Classification According to Hubble-de Vaucouleurs Diagram Using CNNs

      Pier Luigi Mazzeo, Antonio Rizzo, Cosimo Distante

      Abstract

      Galaxies morphology classification is a crucial task for studying their physical properties, formation and evolutionary histories. The large-scale surveys on universe has boosted the need to develop techniques for automated galaxies morphological classification. This paper proposes a system able to classify automatically galaxies according to the Hubble De Vaucouleurs diagram. We introduce a novel CNN architectures that for the first time was trained to automatically classify galaxies according to 26-classes Hubble-De Vaucouleurs scheme. We use Galaxy Zoo dataset, using the decision tree, to extract a labeled examples containing an even amount of images of each 26-classes. We also compared different CNN Backbones in order to assess obtained galaxies classification results. We obtain a balanced multi-class accuracy (BCA) of more than 80% in classifying all 26 Hubble-De Vaucouleurs galaxy categories.