RIHOG-BoVWs for Rotation-invariant Human Detection
نویسندگان
چکیده
منابع مشابه
Comparison of Two Flow Cytometric Methods for Detection of Human Invariant Natural Killer T Cells (iNKT)
Background: Invariant natural killer cells (iNKT) are an important immunoregulatory T cell subset. Currently several flow cytometry-based approaches exist for the identifi-cation of iNKT cells, which rely on using the 6B11 monoclonal antibody or a combina-tion of anti-Vα24 and anti-Vβ11 antibodies. Objective: The aim of this study was to compare the ability of two flow cytometry-based methods f...
متن کاملRotation Invariant Neural Network-Based Face Detection
The system (see Figure 2) uses a neural network, called a “router”, to analyze each window of the input before it is processed by a “detector” network. If the window contains a face, the router returns the angle of the face. The window can then be “derotated” to make the face upright. The derotated window is then passed to the detection network, which decides whether a face is present. If a non...
متن کاملA Rotation-Invariant Transform for Target Detection in SAR Images
Rotation of targets poses a great challenge for the design of an automatic image-based target detection system. In this paper, we propose a target detection algorithm that is robust to rotation of targets. Our key idea is to use rotation invariant features as the input for the classifier. For an image in Radon transform space, namely R(b, θ), taking the magnitude of 1-D Fourier transform on θ, ...
متن کاملSHOG - Spherical HOG Descriptors for Rotation Invariant 3D Object Detection
We present a method for densely computing local spherical histograms of oriented gradients (SHOG) in volumetric images. The descriptors are based on the continuous representation of the orientation histograms in the harmonic domain, which we compute very efficiently via spherical tensor products and the fast Fourier transformation. Building upon these local spherical histogram representations, ...
متن کاملDeep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using rando...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2018
ISSN: 1757-899X
DOI: 10.1088/1757-899x/428/1/012030