Cylinders extraction in non-oriented point clouds as a clustering problem

Finding geometric primitives in 3D point clouds is a fundamental task in many engineering applications such as robotics, autonomous-vehicles and automated industrial inspection. Among all solid shapes, cylinders are frequently found in a variety of scenes, comprising natural or man-made objects. Despite their ubiquitous presence, automated extraction and fitting can become challenging if performed ”in-the-wild”, when the number of primitives is unknown or the point cloud is noisy and not oriented. In this paper we pose the problem of extracting multiple cylinders in a scene by means of a Game-Theoretic inlier selection process exploiting the geometrical relations between pairs of axis candidates. First, we formulate the similarity between two possible cylinders considering the rigid motion aligning the two axes to the same line. This motion is represented with a unitary dual-quaternion so that the distance between two cylinders is induced by the length of the shortest geodesic path in SE(3). Then, a Game-Theoretical process exploits such similarity function to extract sets of primitives maximizing their inner mutual consensus. The outcome of the evolutionary process consists in a probability distribution over the sets of candidates (ie axes), which in turn is used to directly estimate the final cylinder parameters. An extensive experimental section shows that the proposed algorithm offers a high resilience to noise, since the process inherently discards inconsistent data. Compared to other methods, it does not need point normals and does not require a fine tuning of multiple parameters

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

Unsupervised Semantic Discovery Through Visual Patterns Detection

We propose a new fast fully unsupervised method to discover semantic patterns. Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where previous methods fail. Through the modeling of what is a visual pattern in an image, we introduce the notion of “semantic levels" and devise a conceptual framework along with measures and a dedicated benchmark dataset for future comparisons. Our algorithm is composed by two phases. A filtering phase, which selects semantical hotsposts by means of an accumulator space, then a clustering phase which propagates the semantic properties of the hotspots on a superpixels basis. We provide both qualitative and quantitative experimental validation, achieving optimal results in terms of robustness to noise and semantic consistency. We also made code and dataset publicly available

A Survey on Text Classification Algorithms: From Text to Predictions

In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models

A New Proposal of Cellulosic Ethanol to Boost Sugarcane Biorefineries: Techno-Economic Evaluation

Commercial simulator Aspen Plus was used to simulate a biorefinery producing ethanol from sugarcane juice and second generation ethanol production using bagasse fine fraction composed of parenchyma cells (P-fraction). Liquid hot water and steam explosion pretreatment technologies were evaluated. The processes were thermal and water integrated and compared to a biorefinery producing ethanol from juice and sugarcane bagasse. The results indicated that after thermal and water integration, the evaluated processes were self-sufficient in energy demand, being able to sell the surplus electricity to the grid, and presented water intake inside the environmental limit for São Paulo State, Brazil. The processes that evaluated the use of the bagasse fine fraction presented higher economic results compared with the use of the entire bagasse. Even though, due to the high enzyme costs, the payback calculated for the biorefineries were higher than 8 years for all cases that considered second generation ethanol and the net present value for the investment was negative. The reduction on the enzyme load, in a way that the conversion rates could be maintained, is the limiting factor to make second generation ethanol competitive with the most immediate uses of bagasse: fuel for the cogeneration system to surplus electricity production

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features

A new proposal of cellulosic ethanol to boost sugarcane biorefineries: techno-economic evaluation

Commercial simulator Aspen Plus was used to simulate a biorefinery producing ethanol from sugarcane juice and second generation ethanol production using bagasse fine fraction composed of parenchyma cells (P-fraction). Liquid hot water and steam explosion pretreatment technologies were evaluated. The processes were thermal and water integrated and compared to a biorefinery producing ethanol from juice and sugarcane bagasse. The results indicated that after thermal and water integration, the evaluated processes were self-sufficient in energy demand, being able to sell the surplus electricity to the grid, and presented water intake inside the environmental limit for São Paulo State, Brazil. The processes that evaluated the use of the bagasse fine fraction presented higher economic results compared with the use of the entire bagasse. Even though, due to the high enzyme costs, the payback calculated for the biorefineries were higher than 8 years for all cases that considered second generation ethanol and the net present value for the investment was negative. The reduction on the enzyme load, in a way that the conversion rates could be maintained, is the limiting factor to make second generation ethanol competitive with the most immediate uses of bagasse: fuel for the cogeneration system to surplus electricity production2014sem informaçãosem informaçãoThe authors acknowledge the financial support granted by the Agreement Unicamp-Brazil Shell Petroleum Lt