SPARSE POINT CLOUD FILTERING BASED ON COVARIANCE FEATURES

Abstract. This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.</p

Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Optimal Window and Lattice in Gabor Transform Application to Audio Analysis

This article deals with the use of optimal lattice and optimal window in Discrete Gabor Transform computation. In the case of a generalized Gaussian window, extending earlier contributions, we introduce an additional local window adaptation technique for non-stationary signals. We illustrate our approach and the earlier one by addressing three time-frequency analysis problems to show the improvements achieved by the use of optimal lattice and window: close frequencies distinction, frequency estimation and SNR estimation. The results are presented, when possible, with real world audio signals

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations

Geometric feature analysis for the classification of cultural heritage point clouds

In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations

Generalized Many-Way Few-Shot Video Classification

Few-shot learning methods operate in low data regimes. The aim is to learn with few training examples per class. Although significant progress has been made in few-shot image classification, few-shot video recognition is relatively unexplored and methods based on 2D CNNs are unable to learn temporal information. In this work we thus develop a simple 3D CNN baseline, surpassing existing methods by a large margin. To circumvent the need of labeled examples, we propose to leverage weakly-labeled videos from a large dataset using tag retrieval followed by selecting the best clips with visual similarities, yielding further improvement. Our results saturate current 5-way benchmarks for few-shot video classification and therefore we propose a new challenging benchmark involving more classes and a mixture of classes with varying supervision

On-Off Intermittency in Time Series of Spontaneous Paroxysmal Activity in Rats with Genetic Absence Epilepsy

Dynamic behavior of complex neuronal ensembles is a topic comprising a streamline of current researches worldwide. In this article we study the behavior manifested by epileptic brain, in the case of spontaneous non-convulsive paroxysmal activity. For this purpose we analyzed archived long-term recording of paroxysmal activity in animals genetically susceptible to absence epilepsy, namely WAG/Rij rats. We first report that the brain activity alternated between normal states and epilepsy paroxysms is the on-off intermittency phenomenon which has been observed and studied earlier in the different nonlinear systems.Comment: 11 pages, 6 figure

QUALITY FEATURES FOR THE INTEGRATION OF TERRESTRIAL AND UAV IMAGES

The paper presents an innovative approach for improving the orientation results when terrestrial and UAV images are jointly processed. With the existing approaches, the processing of images coming from different platforms and sensors leads often to noisy and inaccurate 3D reconstructions, due to the different nature and properties of the acquired images. In this work, a photogrammetric pipeline is proposed to filter and remove bad computed tie points, according to some quality feature indicators. A completely automatic procedure has been developed to filter the sparse point cloud, in order to improve the orientation results before computing the dense point cloud. We report some tests and results on a dataset of about 140 images (Modena cathedral, Italy). The effectiveness of the filtering procedure was verified using some internal quality indicators, external checks (ground truth data) and qualitative visual analyses

Treatment with human growth hormone in patients with Prader-Labhart-Willi syndrome reduces body fat and increases muscle mass and physical performance

Twelve children with documented Prader-Labhart-Willi syndrome were treated with human growth hormone (24 U/m2/week) during 1 year. The children were divided into three groups: group 1: overweight and prepubertal (n = 6, age 3.8-7.0 years); group 2: underweight and prepubertal (n = 3, age 0.6-4.1 years); group 3: pubertal (n = 3, age 9.2-14.6 years). In group 1, height increased from -1.7 SD to -0.6 SD, while weight decreased from 1.1 SD to 0.4 SD, with a dramatic drop in weight for height from 3.8 SD to 1.2 SD. Hand length increased from -1.5 SD to -0.4 SD and foot length from -2.5 SD to -1.4 SD. Body fat, measured by dual X-ray energy absorptiometry, dropped by a third, whereas muscle mass increased by a fourth. Physical capability (Wingate test) improved considerably. The children were reported to be much more active and capable. In group 2, similar changes were seen, but weight for height increased, probably because muscle mass increase exceeded fat mass decrease. Changes in group 3 were similar as in group 1, even though far less distinct. Conclusion: Growth hormone treatment in Prader-Labhart-Willi syndrome led to dramatic changes: distinct increase in growth velocity, height and muscle mass, as well as an improvement in physical performance. Fat mass and weight for height decreased in the initially overweight children, and weight for height increased in underweight childre