Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation

This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images. The architecture can exploit structural domain constraints such as geometric relationships between body joint locations. We show that joint training of these two model paradigms improves performance and allows us to significantly outperform existing state-of-the-art techniques

MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems

Efficient Object Localization Using Convolutional Networks

Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introduce invariance and prevent over-training. These benefits of pooling come at the cost of reduced localization accuracy. We introduce a novel architecture which includes an efficient `position refinement' model that is trained to estimate the joint offset location within a small region of the image. This refinement model is jointly trained in cascade with a state-of-the-art ConvNet model to achieve improved accuracy in human joint location estimation. We show that the variance of our detector approaches the variance of human annotations on the FLIC dataset and outperforms all existing approaches on the MPII-human-pose dataset.Comment: 8 pages with 1 page of citation

Evaluation of outcome of posterior decompression and instrumented fusion in thoracolumbar fractures

Background: The fractures of the thoracolumbar junction are the most common injuries of the vertebral column. Fall from a height and road traffic accidents are the main causes of injury. The present study aims to evaluate the functional, neurological and radiological outcome of the posterior decompression and instrumented fusion in operated patients with thoracolumbar fractures.Methods: In this retrospective and prospective study, a cohort of 30 patients with thoracolumbar fractures, classified by thoracolumbar injury classification and severity (TLICS) scoring system, underwent posterior decompression and pedicle screw fixation from January 2013 to August 2018 were included. Patients were assessed functionally (ODI score), neurologically (MRC grading) and radiologically (kyphotic angle) preoperatively and at 6 weeks, 3 months, 6 months and 12 months post-operatively.Results: The mean ODI score improved from 87.40 pre-operatively to 13.33 at final follow-up (p value 0.001). The mean kyphotic angle decreased from 24.37 degrees preoperatively to 9.87 degrees postoperatively (p value 0.001) with mean loss of correction of 1.16 degrees at final follow-up. Hip flexors and knee extensors improved from a mean preoperative value of 2.60 to 4.83 at final follow-up (p value 0.001). Similarly, ankle dorsiflexors, long toe extensors and ankle plantar flexors improved from mean preoperative value of 2.53, 2.50 and 2.60 to 3.93, 3.80 and 4.73 at final follow-up, respectively (p value 0.001).Conclusions: Posterior decompression and instrumented fusion is a safe and effective surgical option in patients with thoracolumbar fractures. TLICS scoring system has a prognostic value and helps in determining the prognosis in these patients

Learning Human Pose Estimation Features with Convolutional Networks

This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models. Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows significant improvement over the current state-of-the-art results. The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to outperform all existing traditional architectures on this task. The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on features that might even just cover a few pixels in the image. Higher-level spatial models improve somewhat the overall result, but to a much lesser extent then expected. Many researchers previously argued that the kinematic structure and top-down information is crucial for this domain, but with our purely bottom up, and weak spatial model, we could improve other more complicated architectures that currently produce the best results. This mirrors what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced

Factors determining failure of intertrochanteric fracture fixation with a dynamic hip screw: a retrospective analysis

Background: The intertrochanteric fractures are extra capsular fractures of proximal femur in the trochanteric region. Different fixation techniques were tried for intertrochanteric fractures, with variety of implants but the dynamic hip screw fixation is most widely accepted treatment. However, several authors have concluded that sliding compression screws may be associated with several complications such as perforation of the femoral head, loss of reduction caused by excessive sliding of the lag screw, non-union, shortening of the affected limb and pain. This study was carried out to ascertain the factors that contributed to mechanical failure at our institute.Methods: We retrospectively reviewed 92 patients with unilateral intertrochanteric fracture treated with a sliding hip screw between July 2015 and April 2017. Postoperative radiographs were studied for any loss of reduction, which was defined as a varus deformity greater than 10°, perforation of the femoral head, extrusion of the lag screw of more than 20 mm, or metal failure. The Pearson chi-square test was used to assess the relationship between failure and osteoporosis. A p value of less than 0.05 was considered to be significant.Results: Results revealed a significant relationship between failure and osteoporosis. A possible relationship between the stability of the fracture on Evans’ classification and osteoporosis on Singh’s index was investigated which revealed a high positive correlation between the failure rates of unstable fractures with osteoporosis.Conclusions: An unstable fracture combined with osteoporosis, has higher percentage of fixation failure leading to other methods of treatment like hemiarthroplasty.

Data-driven methods for interactive visual content creation and manipulation

Software tools for creating and manipulating visual content --- be they for images, video or 3D models --- are often difficult to use and involve a lot of manual interaction at several stages of the process. Coupled with long processing and acquisition times, content production is rather costly and poses a potential barrier to many applications. Although cameras now allow anyone to easily capture photos and video, tools for manipulating such media demand both artistic talent and technical expertise. However, at the same time, vast corpuses with existing visual content such as Flickr, YouTube or Google 3D Warehouse are now available and easily accessible. This thesis proposes a data-driven approach to tackle the above mentioned problems encountered in content generation. To this end, statistical models trained on semantic knowledge harvested from existing visual content corpuses are created. Using these models, we then develop tools which are easy to learn and use, even by novice users, but still produce high-quality content. These tools have intuitive interfaces, and enable the user to have precise and flexible control. Specifically, we apply our models to create tools to simplify the tasks of video manipulation, 3D modeling and material assignment to 3D objects.Softwarewerkzeuge zum Erstellen und Bearbeiten von visuellen Inhalten --- seien es Bilder, Videos oder 3D-Modelle --- sind häufig schwierig zu bedienen und erfordern viel manuelle Interaktion an verschiedenen Stellen des Verfahrens. In Verbindung mit langen Bearbeitungs- und Erfassungszeiten ist die Erzeugung von Inhalten eher aufwendig und stellt ein potentielles Hindernis für viele Anwendungen dar. Obwohl heute Kameras jedem Anwender auf einfache Art und Weise erlauben Bilder und Videos aufzunehmen, erfordern Werkzeuge zur Bearbeitung dieser sowohl künstlerisches Talent, als auch technische Kompetenz. Gleichzeitig sind riesige Korpora mit bereits vorhandenen visuellen Inhalten, wie zum Beispiel Flickr, Youtube oder Google 3D Warehouse, verfügbar und leicht zugänglich. Diese Arbeit stellt einen datengetriebenen Ansatz vor, der die erwähnten Probleme der Inhaltserzeugung behandelt. Zu diesem Zweck werden statistische Modelle erzeugt, die auf semantischem Wissen trainiert worden sind, welches aus bestehenden Korpora von visuellen Inhalten gesammelt worden ist. Durch die Verwendung dieser Modelle ist es möglich Werkzeuge zu entwickeln, die sogar von unerfahrenen Anwendern einfach zu erlernen und zu benutzen sind, aber dennoch qualitativ hochwertige Inhalte produzieren. Diese Werkzeuge haben intuitive Benutzeroberflächen und geben dem Benutzer eine präzise und flexible Kontrolle. Insbesondere werden die Modelle eingesetzt, um Werkzeuge zu erzeugen, die Aufgaben Videobearbeitung, 3D-Modellerstellung und Materialzuweisung zu 3D-Modellen vereinfachen