Prototypicality effects in global semantic description of objects

In this paper, we introduce a novel approach for semantic description of object features based on the prototypicality effects of the Prototype Theory. Our prototype-based description model encodes and stores the semantic meaning of an object, while describing its features using the semantic prototype computed by CNN-classifications models. Our method uses semantic prototypes to create discriminative descriptor signatures that describe an object highlighting its most distinctive features within the category. Our experiments show that: i) our descriptor preserves the semantic information used by the CNN-models in classification tasks; ii) our distance metric can be used as the object's typicality score; iii) our descriptor signatures are semantically interpretable and enables the simulation of the prototypical organization of objects within a category.Comment: Paper accepted in IEEE Winter Conference on Applications of Computer Vision 2019 (WACV2019). Content: 10 pages (8 + 2 reference) with 7 figure

Fast-Forward Video Based on Semantic Extraction

Thanks to the low operational cost and large storage capacity of smartphones and wearable devices, people are recording many hours of daily activities, sport actions and home videos. These videos, also known as egocentric videos, are generally long-running streams with unedited content, which make them boring and visually unpalatable, bringing up the challenge to make egocentric videos more appealing. In this work we propose a novel methodology to compose the new fast-forward video by selecting frames based on semantic information extracted from images. The experiments show that our approach outperforms the state-of-the-art as far as semantic information is concerned and that it is also able to produce videos that are more pleasant to be watched.Comment: Accepted for publication and presented in 2016 IEEE International Conference on Image Processing (ICIP

ATLANTIC-PRIMATES: a dataset of communities and occurrences of primates in the Atlantic Forests of South America

Primates play an important role in ecosystem functioning and offer critical insights into human evolution, biology, behavior, and emerging infectious diseases. There are 26 primate species in the Atlantic Forests of South America, 19 of them endemic. We compiled a dataset of 5,472 georeferenced locations of 26 native and 1 introduced primate species, as hybrids in the genera Callithrix and Alouatta. The dataset includes 700 primate communities, 8,121 single species occurrences and 714 estimates of primate population sizes, covering most natural forest types of the tropical and subtropical Atlantic Forest of Brazil, Paraguay and Argentina and some other biomes. On average, primate communities of the Atlantic Forest harbor 2 ± 1 species (range = 1–6). However, about 40% of primate communities contain only one species. Alouatta guariba (N = 2,188 records) and Sapajus nigritus (N = 1,127) were the species with the most records. Callicebus barbarabrownae (N = 35), Leontopithecus caissara (N = 38), and Sapajus libidinosus (N = 41) were the species with the least records. Recorded primate densities varied from 0.004 individuals/km 2 (Alouatta guariba at Fragmento do Bugre, Paraná, Brazil) to 400 individuals/km 2 (Alouatta caraya in Santiago, Rio Grande do Sul, Brazil). Our dataset reflects disparity between the numerous primate census conducted in the Atlantic Forest, in contrast to the scarcity of estimates of population sizes and densities. With these data, researchers can develop different macroecological and regional level studies, focusing on communities, populations, species co-occurrence and distribution patterns. Moreover, the data can also be used to assess the consequences of fragmentation, defaunation, and disease outbreaks on different ecological processes, such as trophic cascades, species invasion or extinction, and community dynamics. There are no copyright restrictions. Please cite this Data Paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. © 2018 by the The Authors. Ecology © 2018 The Ecological Society of Americ

Robotic exploration of material and kinematic properties of objects

The physical interaction with unstructured environments requires that robotic systems have the ability to extract material and kinematic properties of objects around them. The goal of this research is to design a robotic system that actively explores and extracts the material properties, including thermal, hardness and mass properties and of kinematic properties, such as mobility and geometric parameters of objects and their parts. To accomplish this objective, we invoke the paradigms of active perception and exploratory procedures. We develop methodologies for the design of such procedures a well as sensors which support their use in the robotic domain and demonstrate their effectiveness. The system is composed of a control module which coordinates the visual and the haptic sub-modules. Vision is implemented via an agile laser range-scanner which is able to acquire different views of the desired object. Global volumetric models of the object are recovered by fitting super-ellipsoids to the 2${1\over 2}$ D range image. The haptic module uses the geometric information of the object to perform several tests based on non-destructive techniques. For exploring thermal properties, a new approach for the design and modeling of thermal sensors for robotics is presented. A model of this sensor is developed and its validity is experimentally verified with different materials. Mass density is estimated by the weight evaluation procedure. Hardness is evaluated by means of stress vs. strain tests. Compression and tension tests are performed to determine this property. The kinematic characteristics of the object are explored by the mobility procedure. We describe a novel methodology, based on screw theoretic results which enables the identification of the mobility of the object. This is accomplished by forming a closed kinematic chain with the manipulator and the unknown object. The number of degrees of freedom present in the object as well as the geometric parameters of its links are then extracted. The design and implementation of the robotic haptic architecture testbed where all of the above concepts were smoothly integrated into a working system is also described. The architecture controls and coordinates the two robot manipulators, the instrumented parallel-jaw gripper and the mobile laser range-scanner

A Methodology for 3D Registration of Range Images for Object Visualization

The main goal of this paper is to describe a method of image description in VRML, using a device for tridimensional image acquisition. The information obtained from the capture of each object face is organized and a description of the scanned object is made in VRML format. This paper presents a study of the minimum number of captures necessary to get the complete description of the object, shows the problem of face coherence, superposition of points and occluded faces, and discusses the solutions for these problems