Analysis of a biologically-inspired system for real-time object recognition

We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object models. To detect objects in a novel scene, these features are located in the image, and each detected feature votes for all objects that are consistent with its presence. Due to the sharing of features between object models our approach is more scalable to large object databases than traditional methods. To demonstrate the utility of this approach, we train our system to recognize any of 50 objects in everyday cluttered scenes with substantial occlusion. Without further optimization we also demonstrate near-perfect recognition on a standard 3-D recognition problem. Our system has an interpretation as a sparsely connected feed-forward neural network, making it a viable model for fast, feed-forward object recognition in the primate visual system

A low-cost head and eye tracking system for realistic eye movements in virtual avatars

A virtual avatar or autonomous agent is a digital representation of a human being that can be controlled by either a human or an artificially intelligent computer system. Increasingly avatars are becoming realistic virtual human characters that exhibit human behavioral traits, body language and eye and head movements. As the interpretation of eye and head movements represents an important part of nonverbal human communication it is extremely important to accurately reproduce these movements in virtual avatars to avoid falling into the well-known ``uncanny valley''. In this paper we present a cheap hybrid real-time head and eye tracking system based on existing open source software and commonly available hardware. Our evaluation indicates that the system of head and eye tracking is stable and accurate and can allow a human user to robustly puppet a virtual avatar, potentially allowing us to train an A.I. system to learn realistic human head and eye movements

An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.Comment: 15 pages and 5 figures, Submitted to the international conference on Contemporary issues in Data Science (CiDaS 2019), Learn more about this project at https://iasbs.ac.ir/~ansari/fara

Lipohipertrofia: conhecimento e educação ao tratamento com insulina na diabete mellitus

Introduction: Diabetes mellitus type 1 is a chronic disease that, once diagnosed, generates changes in the patient's lifestyle. In order to achieve optimal treatment and avoid subsequent complications, such as lipohypertrophy caused by the increase in the use of insulin, it is vital that patients and medical personnel receive adequate education, so that they acquire knowledge and skills related to the application of the insulin injection. Lipohypertrophy occurs due to the accumulation of subcutaneous fat at the site where insulin is repeatedly injected. Objective: To describe lipohy subject. Methodology: A review of the literature in the databases SciELO, Ovid, Medline, PubMed, ScienceDirect, and Elsevier was carried out, from which 71 articles we identified, and of which 50 met the criteria and were relevant to the search. Results: The most complete and pertinent information was selected from the databases regarding lipohypertrophy, knowledge, education, and insulin treatment in diabetes mellitus. Conclusion: This research allowed us to determine that lipohypertrophy, a secondary complication of insulin treatment in diabetes mellitus, is caused by factors such as an inadequate application technique and inadequate rotation of the injection sites, as well as the lack of knowledge that patients have regarding the treatment, and the lack of education of health personnel when it comes to the use of insulin application techniques.Introducción: la diabetes mellitus tipo 1 es una enfermedad crónica que, una vez diagnósticada, genera cambios en el estilo de vida del paciente. Para lograr un tratamiento óptimo y evitar complicaciones posteriores, como lipohipertrofia por el aumento en el uso de la insulina, es de vital importancia que se brinde a los pacientes y el personal médico una adecuada educación, para que adquieran conocimientos y habilidades en la aplicación de la inyección de insulina. La lipohipertrofia se presenta por acumulación de grasa subcutánea en el sitio donde se inyecta constantemente la insulina. Objetivo: describir la lipohipertrofia en pacientes con diabetes mellitus, los conocimientos y educación que debe adquirir con respecto al tema. Metodología: se realizó una revisión de literatura en las bases de datos SciELO, Ovid, Medline, PubMed, ScienceDirect y Elsevier, que permitió identificar 71 artículos, de los cuales 50 cumplían con los criterios y pertinencia de la búsqueda. Resultados: se seleccionó la información más completa y pertinente de las bases de datos, respecto a la lipohipertrofia, conocimiento, educación y tratamiento con insulina en la diabetes Mellitus. Conclusión: esta revisión permitió determinar que la lipohipertrofia, una complicación secundaria del tratamiento con insulina en la diabetes Mellitus; se origina en factores tales como la técnica de aplicación y rotación inadecuadas de los puntos de inyección, el escaso conocimiento de los pacientes acerca del tratamiento y la falta de educación del personal sanitario con respecto al uso de las técnicas de aplicación de la insulina.Introdução. A diabetes mellitus tipo 1 é uma doença crônica que, uma vez diagnosticada, gera mudanças no estilo de vida do paciente. Para obter um tratamento ótimo e evitar complicações posteriores, como lipohipertrofia pelo aumento no uso da insulina, é de vital importância proporcionar aos pacientes e o pessoal médico uma adequada educação, para que adquiram conhecimentos e habilidades na aplicação da injeção de insulina. A lipohipertrofia se apresenta por acumulação de gordura subcutânea no lugar onde se injeta constantemente a insulina. Objetivo: descrever a lipohipertrofia em pacientes com diabetes mellitus, os conhecimentos e educação que deve adquirir com respeito ao tema. Metodologia: se realizou una revisão de literatura nas bases de dados SciELO, Ovid, Medline, PubMed, ScienceDirect, Elsevier, que permitiu identificar 71 artigos, dos quais 50 cumpriam com os critérios e pertinência da busca. Resultados: selecionou-se a informação mais completa e pertinente das bases de dados, com relação à lipohipertrofia, conhecimento, educação e tratamento com insulina na diabetes Mellitus. Conclusão: esta revisão permitiu determinar que a lipohipertrofia, uma complicação secundária do tratamento com insulina na diabetes Mellitus, origina-se em fatores tais como a técnica de aplicação e rotação inadequadas dos pontos de injeção, o escasso conhecimento dos pacientes sobre o tratamento e a falta de educação do pessoal sanitário com relação ao uso das técnicas de aplicação da insulina

Occlusion-aware 3D Morphable Models and an Illumination Prior for Face Image Analysis

Faces in natural images are often occluded by a variety of objects. We propose a fully automated, probabilistic and occlusion-aware 3D morphable face model adaptation framework following an analysis-by-synthesis setup. The key idea is to segment the image into regions explained by separate models. Our framework includes a 3D morphable face model, a prototype-based beard model and a simple model for occlusions and background regions. The segmentation and all the model parameters have to be inferred from the single target image. Face model adaptation and segmentation are solved jointly using an expectation-maximization-like procedure. During the E-step, we update the segmentation and in the M-step the face model parameters are updated. For face model adaptation we apply a stochastic sampling strategy based on the Metropolis-Hastings algorithm. For segmentation, we apply loopy belief propagation for inference in a Markov random field. Illumination estimation is critical for occlusion handling. Our combined segmentation and model adaptation needs a proper initialization of the illumination parameters. We propose a RANSAC-based robust illumination estimation technique. By applying this method to a large face image database we obtain a first empirical distribution of real-world illumination conditions. The obtained empirical distribution is made publicly available and can be used as prior in probabilistic frameworks, for regularization or to synthesize data for deep learning methods

Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets

The development of a video retrieval system using a clinician-led approach

Patient video taken at home can provide valuable insights into the recovery progress during a programme of physical therapy, but is very time consuming for clinician review. Our work focussed on (i) enabling any patient to share information about progress at home, simply by sharing video and (ii) building intelligent systems to support Physical Therapists (PTs) in reviewing this video data and extracting the necessary detail. This paper reports the development of the system, appropriate for future clinical use without reliance on a technical team, and the clinician involvement in that development. We contribute an interactive content-based video retrieval system that significantly reduces the time taken for clinicians to review videos, using human head movement as an example. The system supports query-by-movement (clinicians move their own body to define search queries) and retrieves the essential fine-grained movements needed for clinical interpretation. This is done by comparing sequences of image-based pose estimates (here head rotations) through a distance metric (here Fréchet distance) and presenting a ranked list of similar movements to clinicians for review. In contrast to existing intelligent systems for retrospective review of human movement, the system supports a flexible analysis where clinicians can look for any movement that interests them. Evaluation by a group of PTs with expertise in training movement control showed that 96% of all relevant movements were identified with time savings of as much as 99.1% compared to reviewing target videos in full. The novelty of this contribution includes retrospective progress monitoring that preserves context through video, and content-based video retrieval that supports both fine-grained human actions and query-by-movement. Future research, including large clinician-led studies, will refine the technical aspects and explore the benefits in terms of patient outcomes, PT time, and financial savings over the course of a programme of therapy. It is anticipated that this clinician-led approach will mitigate the reported slow clinical uptake of technology with resulting patient benefit