Jawadul H. Bappy

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Distributed algorithms have recently gained immense popularity. With regards to computer vision applications, distributed multi-target tracking in a camera network is a fundamental problem. The goal is for all cameras to have accurate state estimates for all targets. Distributed estimation algorithms work by exchanging information between sensors that are(More)
Recent efforts in computer vision consider joint scene and object classification by exploiting mutual relationships (often termed as context) between them to achieve higher accuracy. On the other hand, there is also a lot of interest in online adaptation of recognition models as new data becomes available. In this paper, we address the problem of how models(More)
In computer vision, object detection is addressed as one of the most challenging problems as it is prone to localization and classification error. The current best-performing detectors are based on the technique of finding region proposals in order to localize objects. Despite having very good performance, these techniques are computationally expensive due(More)
In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem. Furthermore, it is also useful to reduce the annotation cost, as it is time consuming to annotate unlabeled samples. In this paper, motivated by the theories in data compression, we propose(More)
Several works have shown that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these interrelationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling(More)
Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian(More)
The decreasing cost and size of video sensors has led to camera networks becoming pervasive in our lives. However, the ability to analyze these images effectively is very much a function of the quality of the acquired images. In this paper we consider the problem of automatically controlling the fields of view of individual pan, tilt, zoom (PTZ) cameras in(More)
The huge amount of time required to construct a set of labeled images to train a classifier has led researchers to develop algorithms which can identify the most informative training images, such that labelling those will be sufficient to achieve a considerable classification accuracy. In this paper we focus on choosing a subset of the most informative and(More)
Posting pictures is a necessary part of advertising a home for sale. Agents typically sort through dozens of images from which to pick the most complimentary ones. This is a manual effort involving annotating images accompanied by descriptions (bedroom, bathroom, attic, etc.). When volumes are small, manual annotation is not a problem, but there is a point(More)
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