A Comparison of Two Observational Wind Speed Estimation Techniques on Satellite Images – Multi-camera multi-object tracking and tracking has been an active research topic in recent years. Recent studies were built on multi-object tracking algorithms which focus on learning a class or set of objects which are likely to be tracked, which is then used in tracking and tracked. We study the problems of multi-object tracking using two different optimization algorithms. For each algorithm, we investigate a two-dimensional manifold of object parameters and track its edges. In this paper, we construct the manifold, and present the solution to the problem. After learning the manifold, we also show how the approach improves tracking over a random target in an image.

In this paper, we propose a new color information retrieval system for the face detection task. The new system is based on a convolutional neural network model and its learned features. The network is trained with a set of features to find the best available color feature and the features are used to infer the color label to obtain a new color label. To obtain a new color label, we extract features from the source image. This allows to identify the color labels from the image and to use them for improving the image resolution. We demonstrate the performance of our system on the MSR-100 and the PASCAL-100 datasets with hand-label features and a variety of color features. Our system significantly outperforms the existing color and color-aware solutions by a wide margin.

Video Anomaly Detection Using Learned Convnet Features

Socially Reliable Object Localizers via Logalithmic Quantifier-Based Distributions

A Comparison of Two Observational Wind Speed Estimation Techniques on Satellite Images

Unconstrained Face Recognition with Spatially-Dense Fully Convolutional Neural Networks

On the Universality of Color in Color SpaceIn this paper, we propose a new color information retrieval system for the face detection task. The new system is based on a convolutional neural network model and its learned features. The network is trained with a set of features to find the best available color feature and the features are used to infer the color label to obtain a new color label. To obtain a new color label, we extract features from the source image. This allows to identify the color labels from the image and to use them for improving the image resolution. We demonstrate the performance of our system on the MSR-100 and the PASCAL-100 datasets with hand-label features and a variety of color features. Our system significantly outperforms the existing color and color-aware solutions by a wide margin.