This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines va...
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This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines various factors that impact the encoder and its computational cost. Many researchers have dedicated themselves to the field of fractal encoding to overcome the computational cost of the FIC algorithm. Here, this study offers a look over the approaches in the aspect of time complexity. The automated baseline fractal compression algorithm is studied to demonstrate the understanding of delay in the encoder. The study establishes how various approaches trade-off between the quality of decoder, compression ratio, and CPU time. The experiment section shows the bargain between fidelity criteria of the baseline algorithm.
Online single target tracking (OSTT) is a prominent topic in normal surveillance environments for security and transportation applications. However, OSTT comparative analysis is seriously under-investigated in the con...
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Online single target tracking (OSTT) is a prominent topic in normal surveillance environments for security and transportation applications. However, OSTT comparative analysis is seriously under-investigated in the context of wide area motion imagery (WAMI) although its importance keeps rising with the popularity of the unmanned aerial vehicles. In this work, we make several efforts toward WAMI tracking analysis. First, we propose a new WAMI OSTT benchmark dataset, named WAMI-226, which consists of 100 image frames and 226 targets. This new benchmark dataset brings together research challenges including low frame rate, low resolution, and low contrast. Second, we evaluate 20 existing online trackers for WAMI tracking scenarios. Third, by combining the basic appearance model, background subtraction and high-order motion (HoM) affinity, we develop a novel normalized cross correlation HoM (NCC-HoM) tracking algorithm for WAMI OSTT. The experimental results show that the proposed NCC-HoM method achieves significant improvements for both target initialization and online tracking. Thus, NCC-HoM serves as a new baseline algorithm for the WAMI-226 benchmark.
Detection of running behavior, the specific anomaly from common walking, has been playing a critical rule in practical surveillance systems. However, only a few works focus on this particular field and the lack of a c...
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Detection of running behavior, the specific anomaly from common walking, has been playing a critical rule in practical surveillance systems. However, only a few works focus on this particular field and the lack of a consistent benchmark with reasonable size limits the persuasive evaluation and comparison. In this paper, for the first time, we propose a standard benchmark database with diversity of scenes and groundtruth for human running detection, and introduce several criteria for performance evaluation in the meanwhile. In addition, a baseline running detection algorithm is presented and extensively evaluated on the proposed benchmark qualitatively and quantitatively. The main purpose of this paper is to lay the foundation for further research in the human running detection domain, by making experimental evaluation more standardized and easily accessible. All the benchmark videos with groundtruth and source codes will be made publicly available online. (C) 2016 Elsevier Inc. All rights reserved.
Background: Current state-of-the-art approaches to biological event extraction train statistical models in a supervised manner on corpora annotated with event triggers and event-argument relations. Inspecting such cor...
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Background: Current state-of-the-art approaches to biological event extraction train statistical models in a supervised manner on corpora annotated with event triggers and event-argument relations. Inspecting such corpora, we observe that there is ambiguity in the span of event triggers (e.g., "transcriptional activity" vs. 'transcriptional'), leading to inconsistencies across event trigger annotations. Such inconsistencies make it quite likely that similar phrases are annotated with different spans of event triggers, suggesting the possibility that a statistical learning algorithm misses an opportunity for generalizing from such event triggers. Methods: We anticipate that adjustments to the span of event triggers to reduce these inconsistencies would meaningfully improve the present performance of event extraction systems. In this study, we look into this possibility with the corpora provided by the 2009 BioNLP shared task as a proof of concept. We propose an Informed Expectation-Maximization (EM) algorithm, which trains models using the EM algorithm with a posterior regularization technique, which consults the gold-standard event trigger annotations in a form of constraints. We further propose four constraints on the possible event trigger annotations to be explored by the EM algorithm. Results: The algorithm is shown to outperform the state-of-the-art algorithm on the development corpus in a statistically significant manner and on the test corpus by a narrow margin. Conclusions: The analysis of the annotations generated by the algorithm shows that there are various types of ambiguity in event annotations, even though they could be small in number.
In this paper, we present a challenging dataset for the purpose of segmentation and change detection in photographic images of mountain habitats. We also propose a baseline algorithm for habitats segmentation to allow...
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ISBN:
(纸本)9781479966837
In this paper, we present a challenging dataset for the purpose of segmentation and change detection in photographic images of mountain habitats. We also propose a baseline algorithm for habitats segmentation to allow for performance comparison. The dataset consists of high resolution image pairs of historic and repeat photographs of mountain habitats acquired in the Canadian Rocky Mountains for ecological surveys. With a time lapse of 70 to 100 years between the acquisition of historic and repeat images, these photographs contain critical information about ecological change in the Rockies. The challenging aspects of analyzing these image pairs come mostly from the perspective (oblique) view of the photographs and the lack of color information in the historic photographs. The baseline algorithm that we propose here is based on texture analysis and machine learning techniques. Classifier training and results validation are made possible by the availability of expert manual ground-truth segmentation for each image. The results obtained with the baseline algorithm are promising and serve as a reference for new and improved segmentation and change detection algorithms.
Establishing benchmark datasets, performance metrics and baseline algorithms have considerable research significance in gauging the progress in any application domain. These primarily allow both users and developers t...
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ISBN:
(纸本)9780819468994
Establishing benchmark datasets, performance metrics and baseline algorithms have considerable research significance in gauging the progress in any application domain. These primarily allow both users and developers to compare the performance of various algorithms on a common platform. In our earlier works, we focused on developing performance metrics and establishing a substantial dataset with ground truth for object detection and tracking tasks (text and face) in two video domains - broadcast news and meetings. In this paper, we present the results of a face detection and tracking algorithm on broadcast news videos with the objective of establishing a baseline performance for this task-domain pair. The detection algorithm uses a statistical approach that was originally developed by Viola and Jones and later extended by Lienhart. The algorithm uses a feature set that is Haar-like and a cascade of boosted decision tree classifiers as a statistical model. In this work, we used the Intel Open Source Computer Vision Library (OpenCV) implementation of the Haar face detection algorithm. The optimal values for the tunable parameters of this implementation were found through an experimental design strategy commonly used in statistical analyses of industrial processes. Tracking was accomplished as continuous detection with the detected objects in two frames mapped using a greedy algorithm based on the distances between the centroids of bounding boxes. Results on the evaluation set containing 50 sequences (approximate to 2.5 mins.) using the developed performance metrics show good performance of the algorithm reflecting the state-of-the-art which makes it an appropriate choice as the baseline algorithm for the problem.
Wavefront sensing of the segmented optics usually includes two steps. The first step is to sense and correct each segment's position error (co-phasing error) and figure error (aberration), and the second step is t...
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ISBN:
(纸本)9780819467669
Wavefront sensing of the segmented optics usually includes two steps. The first step is to sense and correct each segment's position error (co-phasing error) and figure error (aberration), and the second step is to sense the residual wavefront error over the whole aperture. However, due to the limited correction capability and accuracy of the figure sensing and co-phasing sensing in the first step, the original fine wavefront sensing (WFS) algorithm in the second step, the Modified baseline algorithm, would be failed once the absolute figure difference at nearby edges of nearby segments exceeds lambda/2. Thus a Hybrid Phase-diverse Phase Retrieval (HPDPR) algorithm, which adds the Levenburg-Marquardt (LM) algorithm to eliminate each segment's residual position error for decreasing the absolute figure difference, is proposed. Simulations have proven the validity of the HPDPR algorithm, and shown its robustness and high WFS accuracy.
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