Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

Comparative evaluation of salivary zinc concentration in autistic and healthy children in mixed dentition age group-pilot study

Context: Autism prevalence is increasing, with current estimates at 1/68–1/50 individuals diagnosed with autism. Diagnosis is based on behavioral assessments. Early diagnosis and intervention are known to greatly improve functional outcomes in people with autism. Diagnosis, treatment monitoring, and prognosis of autistic children's symptoms could be facilitated with biomarkers to complement behavioral assessments. Aims: The aim of this study is to compare and evaluate salivary zinc concentration in saliva samples of autistic and healthy children in mixed dentition age group. Settings and Design: Cross-sectional comparative study carried out in dental college and special child school. Unstimulated whole saliva collected for finding a biomarker. Subjects and Methods: Unstimulated whole saliva sample was collected from 10 autistic and 10 healthy children in mixed dentition age group. Diluted saliva sample was then subjected to inductively coupled plasma emission spectroscopy for the estimation of salivary zinc concentration. Statistical Analysis Used: Mann–Whitney U-test. Results: In children with autism salivary zinc concentration showed a linear equation when compared to healthy children. Conclusions: The low salivary zinc concentration in autistic children can reveal the pathogenesis of autism