To address the sequential changes of images including poses, in this paper we propose a recurrent regression neural network(RRNN) framework to unify two classic tasks of cross-pose face recognition on still images and video-based face recognition. To imitate the changes of images, we explicitly construct the potential dependencies of sequential images so as to regularize the final learning mode...

This thesis presents a series of experiments on recognizing animals in complex scenes. Unlike usual objects used for the recognition task (cars, airplanes, ...) animals appear in a variety of poses and shapes in outdoor images. To perform this task a dataset of outdoor images should be provided. Among the available datasets there are some animal classes but as discussed in this thesis these dat...

Biological images are critical components for a detailed understanding of the structure and functioning of cells and proteins. Image processing and analysis tools increasingly play a significant role in better harvesting this vast amount of data, most of which is currently analyzed manually and qualitatively. A number of image analysis tools have been proposed to automatically extract the image...

Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenario. To solve this challenge, content-based image retrieval (CBIR) attempts to characterize images (or image regions) with invariant content information in order to facilitate im...

We propose a novel approach based on deep Convolutional Neural Networks (CNN) to recognize human actions in still images by predicting the future motion, and detecting the shape and location of the salient parts of the image. We make the following major contributions to this important area of research: (i) We use the predicted future motion in the static image (Walker et al., 2015) as a means o...

Simple, short, and compact hashtags cover a wide range of information on social networks. Although many works in the field of natural language processing (NLP) have demonstrated the importance of hashtag recommendation, hashtag recommendation for images has barely been studied. In this paper, we introduce the HARRISON dataset, a benchmark on hashtag recommendation for real world images in socia...

This article presents an efficient method for weakly-supervised organ segmentation. It consists in over-segmenting the images into objectlike supervoxels. A single joint forest classifier is then trained on all the images, where (a) the supervoxel indices are used as labels for the voxels, (b) a joint node optimisation is done using training samples from all the images, and (c) in each leaf nod...

This dataset contains 154 images in an urban environment originally obtained from the KITTI dataset (see [1]). The images show well a demarcated (white lines) two lane highway road. The detection algorithm/method is requried to only consider the lane the recording platform was driving on (i.e the right lane). Apart from this other challenges include, shadows, variations in lane-markings and pre...