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35 results on '"Baraldi, Lorenzo"'

24. Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling.

Author

Bolelli, Federico, Allegretti, Stefano, Baraldi, Lorenzo, and Grana, Costantino

Subjects
DIRECTED acyclic graphs, COMPUTER vision, IMAGE processing, SPAGHETTI, FOREST reserves, FOOD labeling
Abstract

Connected Components Labeling is an essential step of many Image Processing and Computer Vision tasks. Since the first proposal of a labeling algorithm, which dates back to the sixties, many approaches have optimized the computational load needed to label an image. In particular, the use of decision forests and state prediction have recently appeared as valuable strategies to improve performance. However, due to the overhead of the manual construction of prediction states and the size of the resulting machine code, the application of these strategies has been restricted to small masks, thus ignoring the benefit of using a block-based approach. In this paper, we combine a block-based mask with state prediction and code compression: the resulting algorithm is modeled as a Directed Rooted Acyclic Graph with multiple entry points, which is automatically generated without manual intervention. When tested on synthetic and real datasets, in comparison with optimized implementations of state-of-the-art algorithms, the proposed approach shows superior performance, surpassing the results obtained by all compared approaches in all settings. [ABSTRACT FROM AUTHOR]

Published
2020
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33. Recognizing and Presenting the Storytelling Video Structure With Deep Multimodal Networks.

Author

Baraldi, Lorenzo, Grana, Costantino, and Cucchiara, Rita

Abstract

In this paper, we propose a novel scene detection algorithm which employs semantic, visual, textual, and audio cues. We also show how the hierarchical decomposition of the storytelling video structure can improve retrieval results presentation with semantically and aesthetically effective thumbnails. Our method is built upon two advancements of the state of the art: first is semantic feature extraction which builds video-specific concept detectors; and second is multimodal feature embedding learning that maps the feature vector of a shot to a space in which the Euclidean distance has task specific semantic properties. The proposed method is able to decompose the video in annotated temporal segments which allow us for a query specific thumbnail extraction. Extensive experiments are performed on different data sets to demonstrate the effectiveness of our algorithm. An in-depth discussion on how to deal with the subjectivity of the task is conducted and a strategy to overcome the problem is suggested. [ABSTRACT FROM PUBLISHER]

Published
2017
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35. Gesture recognition in ego-centric videos using dense trajectories and hand segmentation

Author

Lorenzo Baraldi, Luca Benini, Giuseppe Serra, Francesco Paci, Rita Cucchiara, Baraldi, Lorenzo, Paci, Francesco, Serra, Giuseppe, Benini, Luca, and Cucchiara, Rita

Subjects
Monocular, Gesture recognition, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wearable computer, Segmentation, Computer vision, Artificial intelligence, gesture recognition, image classification, image segmentation, image sensors, video signal processing, embedded computer vision, business, Gesture
Abstract

We present a novel method for monocular hand gesture recognition in ego-vision scenarios that deals with static and dynamic gestures and can achieve high accuracy results using a few positive samples. Specifically, we use and extend the dense trajectories approach that has been successfully introduced for action recognition. Dense features are extracted around regions selected by a new hand segmentation technique that integrates superpixel classification, temporal and spatial coherence. We extensively testour gesture recognition and segmentation algorithms on public datasets and propose a new dataset shot with a wearable camera. In addition, we demonstrate that our solution can work in near real-time on a wearable device.

Published
2014

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