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Computational complexity of fractal image compression algorithm

IET IMAGE PROCESSING 2020年第17期14卷 4425-4434页

作者： Gupta, Richa Mehrotra, Deepti Tyagi, Rajesh Kumar Amity Univ Comp Sci Engn Dept Noida Uttar Pradesh India Amity Univ Informat & Theory Dept Noida Uttar Pradesh India Amity Univ Informat & Theory Dept Gurgaon Haryana India

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.

关键词： data compression computational complexity fractals image coding computational complexity fractal image compression algorithm fractal encoder CPU time decoder computational cost fractal encoding FIC algorithm time complexity automated baseline fractal compression algorithm compression ratio baseline algorithm

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Online single target tracking in WAMI: benchmark and evaluation

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第9期77卷 10939-10960页

作者： Wang, Dong Yi, Meng Yang, Fan Blasch, Erik Sheaff, Carolyn Chen, Genshe Ling, Haibin Temple Univ Comp & Informat Sci Dept Philadelphia PA 19122 USA Air Force Res Lab Informat Directorate Rome NY USA Intelligent Fus Technol Inc Germantown MD USA

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.

关键词： Online single target tracking Wide area motion imagery Benchmark evaluation baseline algorithm

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Human running detection: Benchmark and baseline

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COMPUTER VISION AND IMAGE UNDERSTANDING 2016年 153卷 143-150页

作者： Lao, Shihong Wang, Dong li, Fu Zhang, Haihong SenseTime Japan Ltd Kyoto Japan Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China OMRON Social Solut Co Ltd Shiga Japan

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.

关键词： Running detection Benchmark database baseline algorithm

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Making adjustments to event annotations for improved biological event extraction

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JOURNAL OF BIOMEDICAL SEMANTICS 2016年第1期7卷 55-55页

作者： Baek, Seung-Cheol Park, Jong C. Korea Adv Inst Sci & Technol Dept Comp Sci 291 Daehak Ro Daejeon South Korea Agcy Def Dev Daejeon South Korea

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.

关键词： Event Trigger Training Corpus Event Extraction baseline algorithm Gene Expression Event

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The Mountain Habitats Segmentation and Change Detection Dataset 15

The Mountain Habitats Segmentation and Change Detection Data...

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IEEE Winter Conference on Applications of Computer Vision (WACV 2015)

作者： Jean, Frederic Albu, Alexandra Branzan Capson, David Higgs, Eric Fisher, Jason T. Starzomski, Brian M. Univ Victoria Victoria BC Canada

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.

关键词： ecology geophysical image processing image classification image colour analysis image resolution image segmentation image texture learning (artificial intelligence) Canadian Rocky Mountains baseline algorithm classifier training color information ecological change detection dataset expert manual ground-truth image segmentation high resolution image pairs machine learning techniques mountain habitat segmentation photographic image segmentation texture analysis Feature extraction Histograms Image color analysis Image segmentation Manuals Training Vectors image colour analysis Canadian Rocky Mountains image segmentation chromatic information texture analysis image resolution image texture histograms Image classification Feature extraction Historic segmentation Photographs Functional training ecology

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A baseline algorithm for face detection and tracking in video

A baseline algorithm for face detection and tracking in vide...

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Conference on Optics and Photonics for Counterterrorism and Crime Fighting III

作者： Manohar, Vasant Soundararajan, Padmanablian Korzhova, Valentina Boonstra, Matthew Goldgof, Dmitry Kasturi, Rangachar Univ S Florida Tampa FL 33620 USA

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.

关键词： baseline algorithm face detection & tracking broadcast news performance evaluation

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Hybrid phase-diverse phase retrieval algorithm in wavefront sensing for segmented optics - art. no. 66241M

Hybrid phase-diverse phase retrieval algorithm in wavefront ...

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International Conference on Photoelectronic Detection and Imaging

作者： Mao Heng Wang Xiao Zhao Dazun Beijing Inst Technol Sch Informat Sci & Technol Beijing 100081 Peoples R China

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.

关键词： segmented optics co-phasing error figure error phase-diverse phase retrieval baseline algorithm Levenburg-Marquardt algorithm

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