Makeshift Dictionary Learning on Discrete-valued Texture Pairings – In this paper, we propose a novel methodology to identify and label the regions of the face that are not visible in the real world. Our dataset collected from the Flickr Creative Commons.net dataset, called U3D faces, contains over 2.3 billion images with 3,926,115 textures. Thus, we can automatically classify all images. The dataset contains 8,113,074 textures, and we have collected 2,547,816 images of U3D faces from a database of over 2540 images. In particular, we had collected more than 7,000 textures that are not visible in real images. To further improve the identification, we have made it possible to classify the faces of various facial images by using a convolutional neural network (CNN). To facilitate the recognition of faces, our dataset has been combined with the Flickr Creative Commons dataset. We have used the dataset for the study on Flickr Creative Commons images.

This paper explores a deep neural network based detection technique for object detection in 2D object scene sequences. It is a state-of-the-art method by a large margin in all cases. However, learning to make use of existing model-based detection techniques to improve performance is costly as our current method only uses a finite set of parameters and does not have any prior knowledge about the state of the scene. Therefore, detection of object pose and texture is of high importance for this setting. We provide an effective method using a neural network based detection. Our proposed method is able to perform object detection in an image, which is more robust compared to existing methods such as the deep CNN or Gaussian Network. The method is trained with respect to the object image for each object instance. This approach can be applied in an end-to-end fashion to achieve object detection performance in a more accurate manner.

Linear Sparse Coding via the Thresholding Transform

Bayesian Networks in Naturalistic Reasoning

Makeshift Dictionary Learning on Discrete-valued Texture Pairings

End-to-End Action Detection with Dynamic Contextual Mapping

Safer Sparse LOD Scanning via Sparse Non-linear Support Vector RegressionThis paper explores a deep neural network based detection technique for object detection in 2D object scene sequences. It is a state-of-the-art method by a large margin in all cases. However, learning to make use of existing model-based detection techniques to improve performance is costly as our current method only uses a finite set of parameters and does not have any prior knowledge about the state of the scene. Therefore, detection of object pose and texture is of high importance for this setting. We provide an effective method using a neural network based detection. Our proposed method is able to perform object detection in an image, which is more robust compared to existing methods such as the deep CNN or Gaussian Network. The method is trained with respect to the object image for each object instance. This approach can be applied in an end-to-end fashion to achieve object detection performance in a more accurate manner.