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

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

IMAGE ORIENTATION DETECTION

An orientation detection system sets an orientation of an image or a video based on the detection of an orientation of objects present within the image or video. The system detects the presence of one or more objects within the image or a frame of the video. The system determines spatial orientation parameters for the detected objects in the image. The system then constructs a histogram with a plurality of bins based on the determined spatial orientation parameters. The system counts the number of objects in each of the bins. The system determines the highest number of objects (M) in a single bin from the plurality of bins. Thereafter, the system determines whether there is only one bin with a number of objects greater than M/2. The system then determines the orientation of the image based on spatial orientation parameters for the bin with the number of objects greater than M/2. If there are multiple bins with a count of objects greater than M/2, the system does not change the orientation of the image

THz QCL - Based active imaging applied to composite materials diagnostic

This paper presents a CW raster-scanning THz imaging setup, used to perform Non-Destructive Testing of Kevlar and carbon fibre samples. The setup uses a 2.5 THz Quantum Cascade Laser as a source. Delamination defect in a Kevlar sample was detected showing a sensitivity to laser polarization orientation. Detection of a break in a carbon/epoxy sample was also performed

Political Orientation Detection & Machine Learning

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1069/thumbnail.jp

An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform

With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations

Pre-classification for automatic image orientation

In this paper, we propose a novel method for automatic orientation of digital images. The approach is based on exploiting the properties of local statistics of natural scenes. In this way, we address some of the difficulties encountered in previous works in this area. The main contribution of this paper is to introduce a pre-classification step into carefully defined categories in order to simplify subsequent orientation detection. The proposed algorithm was tested on 9068 images and compared to existing state of the art in the area. Results show a significant improvement over previous work

DEVICE ORIENTATION DETECTION THROUGH SIGNAL FEATURES

Techniques are described herein for using Radio Frequency (RF) signal features at multiple Access Points (APs) to detect the orientation of a client device. These orientation-related RF signal features may be used over time and over multiple APs to perform device tracking/authentication and pattern recognition in 5G or Internet of Things (IoT) networks

Automatic Photo Orientation Detection with Convolutional Neural Networks

We apply convolutional neural networks (CNN) to the problem of image orientation detection in the context of determining the correct orientation (from 0, 90, 180, and 270 degrees) of a consumer photo. The problem is especially important for digitazing analog photographs. We substantially improve on the published state of the art in terms of the performance on one of the standard datasets, and test our system on a more difficult large dataset of consumer photos. We use Guided Backpropagation to obtain insights into how our CNN detects photo orientation, and to explain its mistakes