Fusing spatial and temporal components for real-time depth data enhancement of dynamic scenes

The depth images from consumer depth cameras (e.g., structured-light/ToF devices) exhibit a substantial amount of artifacts (e.g., holes, flickering, ghosting) that needs to be removed for real-world applications. Existing methods cannot entirely remove them and perform slow. This thesis proposes a new real-time spatio-temporal depth image enhancement filter that completely removes flickering and ghosting, and significantly reduces holes. This thesis also presents a novel depth-data capture setup and two data reduction methods to optimize the performance of the proposed enhancement method

The Politics of Language Contact in the Himalaya

"This highly original and timely collection brings together case studies from salient areas of the Himalayan region to explore the politics of language contact. Promoting a linguistically and historically grounded perspective, The Politics of Language Contact in the Himalaya offers nuanced insights into language and its relation to power in this geopolitically complex region. Edited by respected scholars in the field, the collection comprises five new research contributions by established and early-career researchers who have been significantly engaged in the Himalayan region. Grounded in a commitment to theoretically informed area studies, and covering Tibet (China), Assam (India), and Nepal, each case study is situated within contemporary debates in sociolinguistics, political science, and language policy and planning. Bridging disciplines and transcending nation-states, the volume offers a unique contribution to the study of language contact and its political implications. The Politics of Language Contact in the Himalaya is essential reading for researchers in the fields of language policy and planning, applied linguistics, and language and literary education. The detailed introduction and concluding commentary make the collection accessible to all social scientists concerned with questions of language, and the volume as a whole will be of interest to scholars in anthropology, sociolinguistics, political science and Asian studies.

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise