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Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method considers pixel synthesis for the interpolated frame as local convolution over two input...
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive...
Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation...
Application of the benefits of modern computing technology to improve the efficiency of agricultural fields is inevitable with growing concerns about increasing world population and limited food resources. Computing technology is crucial not only to industries related to food production but also to environmentalists and other related authorities. It is expected to increase the productivity, contribute...
Traffic sign recognition plays an important role in autonomous vehicles as well as advanced driver assistance systems. Although various methods have been developed, it is still difficult for the state-of-the-art algorithms to obtain high recognition precision with low computational costs. In this paper, based on the investigation on the influence that color spaces have on the representation learning...
Social behavior and many cultural etiquettes are influenced by gender. There are numerous potential applications of automatic face gender recognition such as human-computer interaction systems, content based image search, video surveillance and more. The immense increase of images that are uploaded online has fostered the construction of large labeled datasets. Recently, impressive progress has been...
In this paper, we present a system for sketch classification and similarity search. We used deep convolution neural networks (ConvNets), state of the art in the field of image recognition. They enable both classification and medium/highlevel features extraction. We make use of ConvNets features as a basis for similarity search using k-Nearest Neighbors (kNN). Evaluation are performed on the TU-Berlin...
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are...
In this paper, we propose an efficient unsupervised object segmentation algorithm that provides effective and robust segmentation of color images by incorporating the advantages of Mean Shift (MS) and GrowCut (GC) methods. In the first stage, the image is divided into different segments using MS algorithm and the generated segments are labeled using Mahalanobis distance. Then, the labeled segments...
Road Sign Detection is a major goal of Advanced Driving Assistance Systems (ADAS). Since the dawn of this discipline, much work based on different techniques has been published which shows that traffic signs can be first detected and then classified in video sequences in real time. While detection is usually performed using classical computer vision techniques based on color and/or shape matching,...
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