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High-dimensional data is commonly encountered in various applications, including genomics, as well as image and video processing. Analyzing, computing, and visualizing such data pose significant challenges. Feature extraction methods are crucial in addressing these challenges by obtaining compressed representations that are suitable for analysis and downstream tasks. One effective technique along these lines is sparse coding, which involves representing data as a sparse linear combination of a set of exemplars. In this study, we propose a local sparse coding framework within the context of a classification problem. The objective is to predict the label of a given data point based on labeled training data. The primary optimization problem encourages the representation of each data point using nearby exemplars. We leverage the optimized sparse representation coefficients to predict the label of a test data point by assessing its similarity to the sparse representations of the training data. The proposed framework is computationally efficient and provides interpretable sparse representations. To illustrate the practicality of our proposed framework, we apply it to agriculture for the classification of crop diseases.
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A Semi-Automated Technique for Transcribing Accurate Crowd Motions
We present a novel technique for transcribing crowds in video scenes that allows extracting the positions of moving objects in video frames. The technique can be used as a more precise alternative to image processing methods, such as background-removal or automated pedestrian detection based on feature extraction and classification. By manually projecting pedestrian actors on a two-dimensional plane and translating screen coordinates to absolute real-world positions using the cross ratio, we provide highly accurate and complete results at the cost of increased processing time. We are able to completely avoid most errors found in other automated annotation techniques, resulting from sources such as noise, occlusion, shadows, view angle or the density of pedestrians. It is further possible to process scenes that are difficult or impossible to transcribe by automated image processing methods, such as low-contrast or low-light environments. We validate our model by comparing it to the results of both background-removal and feature extraction and classification in a variety of scenes.
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- Award ID(s):
- 1718139
- PAR ID:
- 10205877
- Date Published:
- Journal Name:
- International Journal of Image and Graphics
- Volume:
- 20
- Issue:
- 02
- ISSN:
- 0219-4678
- Page Range / eLocation ID:
- 2050012
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S0219467820500126
