A simple and effective relevance-based point sampling for 3D shapes

The surface of natural or human-made objects usually comprises a collection of distinct regions characterized by different features. While some of them can be flat or can exhibit a constant curvature, others may provide a more mixed landscape, abundant with high frequency information. Depending on the task to be performed, individual region properties can be helpful or harmful. For instance, surface registration can be eased by a set of non-coplanar smooth areas, while distinctive points with high curvature can be key for object recognition. For this reason, it is often critical to perform a surface sub-sampling that is suitable to the actual processing goal. To this end, most of the shape processing pipelines found in literature come bundled with one or more sampling rules, designed to boost their performance and accuracy. In this paper we introduce a sampling method for 3D surfaces that aims to be general enough to be useful for a wide range of tasks. The main idea of our method is to exploit the extent of the region around each point that exhibits limited local changes, granting higher relevance to points contained in compact neighborhoods. The effectiveness of the proposed method is experimented through its adoption as a point sampler within three very different shape processing scenarios

Pseudomode treatment of strong-coupling quantum thermodynamics

The treatment of quantum thermodynamic systems beyond weak coupling is of increasing relevance, yet extremely challenging. The evaluation of thermodynamic quantities in strong-coupling regimes requires a nonperturbative knowledge of the bath dynamics, which in turn relies on heavy numerical simulations. To tame these difficulties, considering thermal bosonic baths linearly coupled to the open system, we derive expressions for heat, work, and average system-bath interaction energy that only involve the autocorrelation function of the bath and two-time expectation values of system operators. We then exploit the pseudomode approach, which replaces the physical continuous bosonic bath with a small finite number of damped, possibly interacting, modes, to numerically evaluate these relevant thermodynamic quantities. We show in particular that this method allows for an efficient numerical evaluation of thermodynamic quantities in terms of one-time expectation values of the open system and the pseudomodes. We apply this framework to the investigation of two paradigmatic situations. In the first instance, we study the entropy production for a two-level system ( TLS) coupled to an ohmic bath, simulated via interacting pseudomodes, allowing for the presence of time-dependent driving. Secondly, we consider a quantum thermal machine composed of a TLS interacting with two thermal baths at different temperatures, showing that an appropriate sinusoidal modulation of the coupling with the cold bath only is enough to obtain work extraction

Calibration of a Telecentric Structured-light Device for Micrometric 3D Reconstruction

Structured-light 3D reconstruction techniques are employed in a wide range of applications for industrial inspection. In particular, some tasks require micrometric precision for the identification of microscopic surface irregularities. We propose a novel calibration technique for structured-light systems adopting telecentric lenses for both camera and projector. The device exploits a fixed light pattern (striped-based) to perform accurate microscopic surface reconstruction and measurements. Our method employs a sphere with a known radius as calibration target and takes advantage of the orthographic projection model of the telecentric lenses to recover the bundle of planes originated by the projector. Once the sheaf of parallel planes is properly described in the camera reference frame, the triangulation of the surface’s object hit by the light stripes is immediate. Moreover, we tested our technique in a real-world scenario for industrial surface inspection by implementing a complete pipeline to recover the intersections between the projected planes and the surface. Experimental analysis shows the robustness of the proposed approach against synthetic and real-world test data

Pseudomode treatment of strong-coupling quantum thermodynamics

The treatment of quantum thermodynamic systems beyond weak coupling is of increasing relevance, yet extremely challenging. The evaluation of thermodynamic quantities in strong-coupling regimes requires a nonperturbative knowledge of the bath dynamics, which in turn relies on heavy numerical simulations. To tame these difficulties, considering thermal bosonic baths linearly coupled to the open system, we derive expressions for heat, work, and average system-bath interaction energy that only involve the autocorrelation function of the bath and two-time expectation values of system operators. We then exploit the pseudomode approach, which replaces the physical continuous bosonic bath with a small finite number of damped, possibly interacting, modes, to numerically evaluate these relevant thermodynamic quantities. We show in particular that this method allows for an efficient numerical evaluation of thermodynamic quantities in terms of one-time expectation values of the open system and the pseudomodes. We apply this framework to the investigation of two paradigmatic situations. In the first instance, we study the entropy production for a two-level system coupled to an ohmic bath, simulated via interacting pseudomodes, allowing for the presence of time-dependent driving. Secondly, we consider a quantum thermal machine composed of a two-level system interacting with two thermal baths at different temperatures, showing that an appropriate sinusoidal modulation of the coupling with the cold bath only is enough to obtain work extraction.Comment: 23 pages, 5 figure

Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on

A Light Source Calibration Technique for Multi-camera Inspection Devices

Industrial manufacturing processes often involve a visual control system to detect possible product defects during production. Such inspection devices usually include one or more cameras and several light sources designed to highlight surface imperfections under different illumination conditions (e.g. bumps, scratches, holes). In such scenarios, a preliminary calibration procedure of each component is a mandatory step to recover the system’s geometrical configuration and thus ensure a good process accuracy. In this paper we propose a procedure to estimate the position of each light source with respect to a camera network using an inexpensive Lambertian spherical target. For each light source, the target is acquired at different positions from different cameras, and an initial guess of the corresponding light vector is recovered from the analysis of the collected intensity isocurves. Then, an energy minimization process based on the Lambertian shading model refines the result for a pr ecise 3D localization. We tested our approach in an industrial setup, performing extensive experiments on synthetic and real-world data to demonstrate the accuracy of the proposed approach

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

Learning Computer Vision through the development of a Camera-trackable Game Controller

The trade-off between the available classroom time and the complexity of the proposed task is central to the design of any Computer Science laboratory lecture. Special care must be taken to build up an experimental setup that allows the students to get the most significant information from the experience without getting lost in the details. This is especially true when teaching Computer Vision concepts to prospective students that own little or no previous background in programming and a strongly diversified knowledge with respect to mathematics. In this chapter, the authors describe a setup for a laboratory lecture that has been administered through several years to prospective students of the Computer Science course at the University of Venice. The goal is to teach basic concepts such as color spaces or image transforms through a rewarding task, which is the development of a vision-based game controller similar in spirit to the recent human-machine interfaces adopted by the current generation of game consoles

A Practical Setup for Projection-based Augmented Maps

Projected Augmented Reality is a human-computer interaction scenario where synthetic data, rather than being rendered on a display, are directly projected on the real world. Differening from screen-based approaches, which only require the pose of the camera with respect to the world, this setup poses the additional hurdle of knowing the relative pose between capturing and projecting devices. In this chapter, the authors propose a thorough solution that addresses both camera and projector calibration using a simple fiducial marker design. Specifically, they introduce a novel Augmented Maps setup where the user can explore geographically located information by moving a physical inspection tool over a printed map. Since the tool presents both a projection surface and a 3D-localizable marker, it can be used to display suitable information about the area that it covers. The proposed setup has been evaluated in terms of accuracy of the calibration and ease of use declared by the users