Dario Cazzato

Randomized circle detection with isophotes curvature analysis

Circle detection is a critical issue in image analysis and object detection. Although Hough transform based solvers are largely used, randomized approaches, based on the iterative sampling of the edge pixels, are object of research in order to provide solutions less computationally expensive. This work presents a randomized iterative work-flow, which exploits geometrical properties of isophotes in the image to select the most meaningful edge pixels and to classify them in subsets of equal isophote curvature. The analysis of candidate circles is then performed with a kernel density estimation based voting strategy, followed by a refinement algorithm based on linear error compensation. The method has been applied to a set of real images on which it has also been compared with two leading state of the art approaches and Hough transform based solutions. The achieved results show how, discarding up to 57% of unnecessary edge pixels, it is able to accurately detect circles within a limited number of iterations, maintaining a sub-pixel accuracy even in the presence of high level of noise. HighlightsAn iterative randomized circle detection algorithm is proposed.Curvature of isophotes is used to reduce the number of necessary iterations.Edges on circumference(s) are selected by a Kernel density estimation strategy.Sub-pixel accuracy was maintained on real images even in presence of high noise levels.Isophotes analysis reduces the influence of the used edge map on the results.

An Investigation on the Feasibility of Uncalibrated and Unconstrained Gaze Tracking for Human Assistive Applications by Using Head Pose Estimation

This paper investigates the possibility of accurately detecting and tracking human gaze by using an unconstrained and noninvasive approach based on the head pose information extracted by an RGB-D device. The main advantages of the proposed solution are that it can operate in a totally unconstrained environment, it does not require any initial calibration and it can work in real-time. These features make it suitable for being used to assist human in everyday life (e.g., remote device control) or in specific actions (e.g., rehabilitation), and in general in all those applications where it is not possible to ask for user cooperation (e.g., when users with neurological impairments are involved). To evaluate gaze estimation accuracy, the proposed approach has been largely tested and results are then compared with the leading methods in the state of the art, which, in general, make use of strong constraints on the people movements, invasive/additional hardware and supervised pattern recognition modules. Experimental tests demonstrated that, in most cases, the errors in gaze estimation are comparable to the state of the art methods, although it works without additional constraints, calibration and supervised learning.

Unsupervised approach for the accurate localization of the pupils in near-frontal facial images

Abstract. A new method to automatically locate pupils in images (even with low resolution) containing near-frontal human faces is presented. In particular, pupils are localized by an unsupervised procedure consisting of two steps: at first, self-similarity information is extracted by considering the appearance variability of local regions, and then it is combined with an estimator of circular shapes based on a modified version of the circular Hough transform. Experimental evidence of the effectiveness of the method was achieved on challenging databases and video sequences containing facial images acquired under different lighting conditions and with different scales and poses.

Visual Interaction Including Biometrics Information for a Socially Assistive Robotic Platform

This work introduces biometrics as a way to improve human-robot interaction. In particular, gender and age estimation algorithms are used to provide awareness of the user biometrics to a humanoid robot (Aldebaran NAO), in order to properly react with a specific gender/age behavior. The system can also manage multiple persons at the same time, recognizing the age and gender of each participant. All the estimation algorithms employed have been validated through a k-fold test and successively practically tested in a real human-robot interaction environment, allowing for a better natural interaction. Our system is able to work at a frame rate of 13 fps with 640\(\times \)480 images taken from NAO’s embedded camera. The proposed application is well-suited for all assisted environments that consider the presence of a socially assistive robot like therapy with disable people, dementia, post-stroke rehabilitation, Alzheimer disease or autism.

Real-Time Gender Based Behavior System for Human-Robot Interaction

This work introduces a real-time system able to lead humanoid robot behavior depending on the gender of the interacting person. It exploits Aldebaran NAO humanoid robot view capabilities by applying a gender prediction algorithm based on the face analysis. The system can also manage multiple persons at the same time, recognizing if the group is composed by men, women or is a mixed one and, in the latter case, to know the exact number of males and females, customizing its response in each case. The system can allow for applications of human-robot interaction requiring an high level of realism, like rehabilitation or artificial intelligence.

Social Robots in Postural Education: A New Approach to Address Body Consciousness in ASD Children

Autism Spectrum Disorders (ASD) represent one of the most prevalent developmental disorders among children with different level of impairments in social relationships, communication and imagination. In addition, impaired movement is also observed in individuals with ASD and recent studies consider this factor as a limitation for fully engagement in the social environment. In the present work, we propose a new approach to promote postural education in autistic children with the involvement of a humanoid social robot and the therapist in a triadic interaction environment to better understand their motor development and body consciousness.

Automatic Joint Attention Detection During Interaction with a Humanoid Robot

Joint attention is an early-developing social-communicative skill in which two people (usually a young child and an adult) share attention with regards to an interesting object or event, by means of gestures and gaze, and its presence is a key element in evaluating the therapy in the case of autism spectrum disorders. In this work, a novel automatic system able to detect joint attention by using completely non-intrusive depth camera installed on the room ceiling is presented. In particular, in a scenario where a humanoid-robot, a therapist (or a parent) and a child are interacting, the system can detect the social interaction between them. Specifically, a depth camera mounted on the top of a room is employed to detect, first of all, the arising event to be monitored (performed by an humanoid robot) and, subsequently, to detect the eventual joint attention mechanism analyzing the orientation of the head. The system operates in real-time, providing to the therapist a completely non-intrusive instrument to help him to evaluate the quality and the precise modalities of this predominant feature during the therapy session.

Assistive Robot, RGB-D Sensor and Graphical User Interface to Encourage Communication Skills in ASD Population

The main feature of Autism Spectrum Disorders (ASDs) is the difficulty in communicating with others and struggling to maintain a functional contact with the environment. This work presents the implementation of a Graphical User Interface (GUI) for Digital PECS Therapy that will enable ASD population to overcome their impairments. The GUI was integrated with a depth sensor, to recognize hand gestures of autistic subjects, a monitor, where specific tools have been displayed, and a humanoid robot (Aldebaran Robotics NAO), used as a medium that will allow people with ASD to communicate their needs. Subjects can select the displayed pictures they want with hand movements, and the robot pronounces the represented objects. The system has been validated during therapeutic sessions with autistic subjects and the results are here reported and discussed supporting the idea that the presence of the robot helps to elicit triadic interactions in ASD.

Pervasive Retail Strategy Using a Low-Cost Free Gaze Estimation System

This paper proposes a pervasive retail architecture based on a free human gaze estimation system. The main aim of the paper is to investigate the possibility to automatically understand the behavior of the persons looking at a shop window: this is done by a gaze estimation technique that uses a RGB-D device in order to extract head pose information from which a fast geometric technique then evaluates the focus of attention of the persons in the scene (even more persons at the same time). The main contribution concerns with the application into this challenging research field of a gaze estimation working without any initial calibration and, in spite of this, able to properly deal with completely unaware persons moving in unconstrained environments. Preliminary experiments were conducted in our lab in order to quantitative validate the accuracy of the gaze estimation on different benchmarks of persons. Then the qualitative evaluation of the effectiveness of the proposed architecture was conducted in a real shop window demonstrating the ability to deal with real challenging environmental conditions.

Soft Biometrics for a Socially Assistive Robotic Platform

Abstract In thiswork, a real-time system able to automatically recognize soft-biometric traits is introduced and used to improve the capability of a humanoid robot to interact with humans. In particular the proposed system is able to estimate gender and age of humans in images acquired from the embedded camera of the robot. This knowledge allows the robot to properly react with customized behaviors related to the gender/age of the interacting individuals. The system is able to handle multiple persons in the same acquired image, recognizing the age and gender of each person in the robot’s field of view. These features make the robot particularly suitable to be used in socially assistive applications.