Deep Bilateral Learning for Real-Time Image Enhancement

Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga

Role of fiberoptic bronchoscopy in sputum smear negative suspected cases of pulmonary tuberculosis: a study conducted in Southern part of Rajasthan

Background: Sputum smear negative pulmonary tuberculosis is a common problem faced by clinicians. Fiberoptic bronchoscopy may be very useful in diagnosing these cases which have no sputum or whose sputum smear is negative for acid fast bacilli. Objective of the current study was to assess the role of fiberoptic bronchoscopy in sputum smear negative under NTEP and radiologically suspected cases of pulmonary tuberculosis.Methods: Clinico-radiological suspected cases of pulmonary tuberculosis patients in whom two sputum smear for acid fast bacilli by Ziehl Neelsen stain under NTEP was negative were included in the study. Fiberoptic bronchoscopy was performed in all these patients and samples taken were sent for investigations.Results: Fiberoptic bronchoscopy was performed in 250 patients of suspected pulmonary tuberculosis whose sputum for AFB smear was negative. Cough was the most predominant symptom. Radiologically, right side disease was more common and upper zone was most commonly involved and infiltrates were common radiological finding. During bronchoscopy, congestion and hyperaemia (36%) and mucopurulent/mucoid secretions (32%) was seen in maximum number of cases. BAL was positive in 200 patients (80%), post bronchoscopy sputum was positive in 70 cases (28%) and biopsy was positive in 12 patients out of 16 performed biopsies (75%). The total TB positive cases after combining all the methods were 215 making the overall diagnostic yield of 86%.Conclusions: Fiberoptic bronchoscopy and post bronchoscopy sputum can be very useful for diagnosing sputum for AFB smear negative but clinico-radiological suspected cases of pulmonary tuberculosis patients

A study of bronchial asthma in school going children in Southern part of Rajasthan

Background: Asthma is a chronic and common inflammatory disease involving mainly large airways of lungs. Childhood asthma is common chronic illness among school going children and is usually underdiagnosed and undertreated. The aim of the present study was to find out of the prevalence of Bronchial asthma in school going children of age group 6-12 years in southern part of Rajasthan (India), and its relation with gender, socio-economic status and heredity.Methods: A questionnaire-based study has been carried out in 1500 children of 6 to 12 years age group in four schools of Udaipur city (Rajasthan, India) with a response rate of 60.23% (904/1500).Results: The overall prevalence of asthma observed is 4.75% (43/904). The prevalence is higher among boys (5.55%) as compared to girls (3.75%). Further the prevalence is higher in upper (7.18%) and upper middle class (7.14%) children as compared to lower middle (4.84%) and upper lower class (2.01%) socioeconomic status. The children with positive family history of asthma also have higher prevalence (26.31%) of asthma.Conclusions: The prevalence of childhood asthma in Udaipur city is relatively lower and supports the already reported relation with gender, socioeconomic status and heredity.

Using context to enhance the understanding of face images

Faces are special objects of interest. Developing automated systems for detecting and recognizing faces is useful in a variety of application domains including providing aid to visually-impaired people and managing large-scale collections of images. Humans have a remarkable ability to detect and identify faces in an image, but related automated systems perform poorly in real-world scenarios, particularly on faces that are difficult to detect and recognize. Why are humans so good? There is general agreement in the cognitive science community that the human brain uses the context of the scene shown in an image to solve the difficult cases of detection and recognition. This dissertation focuses on emulating this approach by using different kinds of contextual information for improving the performance of various approaches for face detection and face recognition. For the face detection problem, we describe an algorithm that employs the easy-to-detect faces in an image to find the difficult-to-detect faces in the same image. For the face recognition problem, we present a joint probabilistic model for image-caption pairs. This model solves the difficult cases of face recognition in an image by using the context generated from the caption associated with the same image. Finally, we present an effective solution for classifying the scene shown in an image, which provides useful context for both of the face detection and recognition problems

EE 672 Computer Vision and document processing Term Project Human face classification

Automated identification of humans is one of the most sought after need of the current time. Many biometric techniques can be used for this purpose. Identificatio