Incorrectly setting the camera's exposure can have a significant negative effect on a photograph. Over-exposing photographs causes pixels to exhibit unpleasant artifacts due to saturation of the sensor. Saturation removal typically involves user intervention to adjust the color values, which is tedious and time-consuming. This paper discusses how saturation can be automatically removed without compromising the essential details of the image. Our method is based on a smoothness prior: neighboring pixels have similar channel ratios and color values. We demonstrate that high quality saturation-free photos can be obtained from a simple but effective approach.
An important aspect in interactive, action-based interfaces is the latency in recognizing the action. High latency will cause the system's feedback to lag behind user actions, reducing the overall quality of the user experience. This paper presents a novel dataset and algorithms for reducing the latency in recognizing the action. Latency in classification is minimized with a classifier based on logistic regression that uses canonical poses to identify the action. The classifier is trained from the dataset using a learning formulation that makes it possible to train the classifier to reduce latency. The classifier is compared against both a Bag of Words and a Conditional Random Field classifier and is found to be superior in both pre-segmented and on-line classification tasks.
In this paper, we present an action recognition system that automatically locates discriminative regions within a video and then uses information from these regions to classify the action being performed. The system is trained in a weakly supervised manner where the training data is annotated with only the action label i.e. no annotation of discriminative regions is provided.
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