Performance Evaluation of State-of-the-Art Local Feature Detectors and Descriptors in the Context of Longitudinal Registration of Retinal Images

The main objective of this chapter is to provide an overview of existing speech and audio watermarking technology and to demonstrate the importance of signal processing for the design and evaluation of watermarking algorithms. This chapter describes the factors to be considered while designing speech and audio watermarking algorithms, including the choice of the domain and signal features for watermarking, watermarked signal fidelity, watermark robustness, data payload, security, and watermarking applications. The chapter presents several state-of-the-art speech and audio watermarking algorithms and discusses their advantages and disadvantages. The various applications of watermarking and developments in performance evaluation of watermarking algorithms are also described.

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Image Features from Morphological Scale-Spaces

Semantic Mining Technologies for Multimedia Databases ◽

10.4018/978-1-60566-188-9.ch002 ◽

2011 ◽

pp. 32-79

Author(s):

Sébastien Lefèvre

Keyword(s):

Data Mining ◽

Performance Evaluation ◽

Video Processing ◽

State Of The Art ◽

Image Features ◽

Video Data ◽

Multimedia Data ◽

Multimedia Data Mining ◽

Shot Detection ◽

Scale Spaces

Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data available. The aim of this chapter is to introduce researchers, especially new ones, to the “video representation, processing, and segmentation techniques”. This includes an easy and smooth introduction, followed by principles of video structure and representation, and then a state-of-the-art of the segmentation techniques focusing on the shot-detection. Performance evaluation and common issues are also discussed before concluding the chapter.

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Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

Applied Sciences ◽

10.3390/app10072474 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2474

Author(s):

Honglie Wang ◽

Shouqian Sun ◽

Lunan Zhou ◽

Lilin Guo ◽

Xin Min ◽

...

Keyword(s):

Feature Extraction ◽

Large Scale ◽

Feature Matching ◽

State Of The Art ◽

Intelligent Transportation ◽

The State ◽

Local Feature ◽

Public Security ◽

Matching Model ◽

Image Deformation

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

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Design and Experimental Evaluation of a Single-Actuator Continuous Hopping Robot Using the Geared Symmetric Multi-Bar Mechanism

Applied Sciences ◽

10.3390/app9010013 ◽

2018 ◽

Vol 9 (1) ◽

pp. 13 ◽

Cited By ~ 5

Author(s):

Long Bai ◽

Fan Zheng ◽

Xiaohong Chen ◽

Yuanxi Sun ◽

Junzhan Hou

Keyword(s):

Performance Evaluation ◽

Energy Storage ◽

Experimental Evaluation ◽

State Of The Art ◽

Energy Conversion Efficiency ◽

Servo Motor ◽

Hopping Robot ◽

Closed Chain ◽

Hopping Robots ◽

And Performance

This paper proposes the design and performance evaluation of a miniaturized continuous hopping robot RHop for unstructured terrain. The hopping mechanism of RHop is realized by an optimized geared symmetric closed-chain multi-bar mechanism that is transformed from the eight-bar mechanism, and the actuator of RHop is realized by a servo motor and the clockwork spring, thereby enabling RHop to realize continuous hopping while its motor rotates continuously only in one direction. Comparative simulations and experiments are conducted for RHop. The results show that RHop can realize better continuous hopping performance, as well as the improvement of energy conversion efficiency from 70.98% to 76.29% when the clockwork spring is applied in the actuator. In addition, comparisons with some state-of-the-art hopping robots are conducted, and the normalized results show that RHop has a better energy storage speed.

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Part-Based Visual Tracking via Online Weighted P-N Learning

The Scientific World JOURNAL ◽

10.1155/2014/402185 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Heng Fan ◽

Jinhai Xiang ◽

Jun Xu ◽

Honghong Liao

Keyword(s):

Visual Tracking ◽

State Of The Art ◽

The State ◽

Tracking Algorithm ◽

Local Feature ◽

Experimental Results ◽

Learning Method ◽

Target Model ◽

Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

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