In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we e...

In this paper, a new hierarchical block matching algorithm using mean and difference pyramids is presented. The detection of motion vectors at each level of the pyramid is accomplished by selectively eliminating the candidate motion vectors that cannot provide the best match at the next lower level. The remaining motion vectors at each level are propagated and used as the initial motion vectors...

Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species identification method for wildlife pictures captured by remote camera traps. Our process starts with ima...

Face recognition is a challenging task due to the large variety of appearance that a face may exhibit under variations in illumination and viewing position as well as variations in facial expression. Many of the more successful approaches use Gabor wavelets as an image descriptor, resulting in relatively high computational cost. In this work, we have explored the use of Gaussian derivative feat...

The spatial pyramid and its variants have been among the most popular and successful models for object recognition. In these models, local visual features are coded across elements of a visual vocabulary, and then these codes are pooled into histograms at several spatial granularities. We introduce spatially local coding, an alternative way to include spatial information in the image model. Ins...

Detecting pedestrians in images and videos plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we presents a novel feature, termed pyramid center-symmetric local binary/ternary patterns (pyramid CS-LBP/LTP), fo...

In order to achieve a multiple resolution with multiple directional feature analysis, a Laplacian pyramid is combined with directional filter bank. This requires the analysis on the design of both the filter in the Laplacian pyramid and the overall directional decomposition structure. Our analysis shows that the energy distribution of the filter in the Laplacian pyramid should match with the an...

Accurately detecting pedestrians in images plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we present novel features, termed dense center-symmetric local binary patterns (CS-LBP) and pyramid center-symmetri...