A NOVEL BIOMECHANICS-BASED APPROACH FOR PERSON RE-IDENTIFICATION BY GENERATING DENSE COLOR SIFT SALIENCE FEATURES

2017 ◽  
Vol 17 (07) ◽  
pp. 1740011 ◽  
Author(s):  
JAMAL HUSSAIN SHAH ◽  
ZONGHAI CHEN ◽  
MUHAMMAD SHARIF ◽  
MUSSARAT YASMIN ◽  
STEVEN LAWRENCE FERNANDES
Keyword(s):  
Convex Hull ◽  
Nearest Neighbor ◽  
Identification Accuracy ◽  
Significant Feature ◽  
Identification System ◽  
Feature Descriptor ◽  
Global Features ◽  
Outlier Test ◽  
Local And Global Features ◽  
Unsolved Issue

Currently, identifying humans using biomechanics-based approaches has gained a lot of significance for person re-identification. Biomechanics-based approaches use knee-hip angle–angle relationships and body movements for person re-identification. Generally, biomechanics of human walking and running is used for person re-identification. In fact, person re-identification is a complex and important task in academia as well as industry and remains an unsolved issue in the computer vision field. The subjects most commonly addressed regarding person re-identification include significant feature extraction that can function accurately with invariant appearance and robust classification. In this study, a significant color feature descriptor is proposed by combining dense color-SIFT and global convex hull salience region features. First convex hull boundary points are detected using the SIFT technique. Furthermore, it is extended with Grubb’s outlier test to eliminate the outlier points detected by SIFT and mark the saliency region via convex hull. Then dense-SIFT and dense-CHF methods are used to extract local and global features within the convex hull region, respectively. Finally, the pre-ranked common nearest neighbor selection technique is applied to minimize overhead of dataset and generate more robust rank classification. The proposed technique is tested using three-camera database video sequences and three publicly available datasets, namely i-LIDS, VIPeR and GRID. Performance of re-identification system is evaluated using a statistical method with CMC curves. The results show better re-identification accuracy in solving the aforementioned problems.

scholarly journals Palmprint Authentication System Based on Local and Global Feature Fusion Using DOST

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
N. B. Mahesh Kumar ◽  
K. Premalatha
Keyword(s):  
Feature Fusion ◽  
Personal Identification ◽  
Identification System ◽  
Global Information ◽  
Global Feature ◽  
Global Features ◽  
Global Matching ◽  
Local And Global Features ◽  
Physical Stimuli ◽  
Stockwell Transform

Palmprint is the region between wrist and fingers. In this paper, a palmprint personal identification system is proposed based on the local and global information fusion. The local and global information is critical for the image observation based on the results of the relationship between physical stimuli and perceptions. The local features of the enhanced palmprint are extracted using discrete orthonormal Stockwell transform. The global feature is obtained by reducing the scale of discrete orthonormal Stockwell transform to infinity. The local and global matching distances of the two palmprint images are fused to get the final matching distance of the proposed scheme. The results show that the fusion of local and global features outperforms the existing works on the available three datasets.

Spherical object recognition based on the number of contour edges extracted by fitting and convex hull processing

2020 ◽  
pp. 095440702096950
Author(s):  
Bochang Zou ◽  
Huadong Qiu ◽  
Yufeng Lu
Keyword(s):  
Convex Hull ◽  
Target Object ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Circle Detection ◽  
Spherical Object ◽  
Single Feature ◽  
Contour Fitting ◽  
Rgb Images

The detection of spherical targets in workpiece shape clustering and fruit classification tasks is challenging. Spherical targets produce low detection accuracy in complex fields, and single-feature processing cannot accurately recognize spheres. Therefore, a novel spherical descriptor (SD) for contour fitting and convex hull processing is proposed. The SD achieves image de-noising by combining flooding processing and morphological operations. The number of polygon-fitted edges is obtained by convex hull processing based on contour extraction and fitting, and two RGB images of the same group of objects are obtained from different directions. The two fitted edges of the same target object obtained at two RGB images are extracted to form a two-dimensional array. The target object is defined as a sphere if the two values of the array are greater than a custom threshold. The first classification result is obtained by an improved K-NN algorithm. Circle detection is then performed on the results using improved Hough circle detection. We abbreviate it as a new Hough transform sphere descriptor (HSD). Experiments demonstrate that recognition of spherical objects is obtained with 98.8% accuracy. Therefore, experimental results show that our method is compared with other latest methods, HSD has higher identification accuracy than other methods.

scholarly journals A machine learning approach for accurate and real-time DNA sequence identification

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiren Wang ◽  
Mashari Alangari ◽  
Joshua Hihath ◽  
Arindam K. Das ◽  
M. P. Anantram
Keyword(s):  
Real Time ◽  
Dna Sequence ◽  
Single Molecule ◽  
Identification Accuracy ◽  
Measuring System ◽  
Identification System ◽  
Genetic Sequencing ◽  
Sequence Identification ◽  
Tree Classifier ◽  
Single Mismatch

Abstract Background The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. Results This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. Conclusions A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

scholarly journals Combination of Dual-Energy X-ray Transmission and Variable Gas-Ejection for the In-Line Automatic Sorting of Many Types of Scrap in One Measurement

2021 ◽  
Vol 11 (10) ◽  
pp. 4349
Author(s):  
Tianzhong Xiong ◽  
Wenhua Ye ◽  
Xiang Xu
Keyword(s):  
High Efficiency ◽  
Nearest Neighbor ◽  
High Energy ◽  
Dual Energy ◽  
Center Of Gravity ◽  
Low Energy ◽  
Identification Accuracy ◽  
Flow Process ◽  
X Ray ◽  
Automatic Sorting

As an important part of pretreatment before recycling, sorting has a great impact on the quality, efficiency, cost and difficulty of recycling. In this paper, dual-energy X-ray transmission (DE-XRT) combined with variable gas-ejection is used to improve the quality and efficiency of in-line automatic sorting of waste non-ferrous metals. A method was proposed to judge the sorting ability, identify the types, and calculate the mass and center-of-gravity coordinates according to the shading of low-energy, the line scan direction coordinate and transparency natural logarithm ratio of low energy to high energy (R_value). The material identification was satisfied by the nearest neighbor algorithm of effective points in the material range to the R_value calibration surface. The flow-process of identification was also presented. Based on the thickness of the calibration surface, the material mass and center-of-gravity coordinates were calculated. The feasibility of controlling material falling points by variable gas-ejection was analyzed. The experimental verification of self-made materials showed that identification accuracy by count basis was 85%, mass and center-of-gravity coordinates calculation errors were both below 5%. The method proposed features high accuracy, high efficiency, and low operation cost and is of great application value even to other solid waste sorting, such as plastics, glass and ceramics.

On the analysis of local and global features for hyperemia grading

2017 ◽  
Author(s):  
L. Sánchez ◽  
N. Barreira ◽  
N. Sánchez ◽  
A. Mosquera ◽  
H. Pena-Verdeal ◽  
...  
Keyword(s):  
Global Features ◽  
Local And Global Features

RF-Identity

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Chao Feng ◽  
Jie Xiong ◽  
Liqiong Chang ◽  
Fuwei Wang ◽  
Ju Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Critical Role ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Frequency Bandwidth ◽  
Person Identification ◽  
Data Set ◽  
Rf Devices ◽  
Large Frequency

Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi

2022 ◽  
Vol 18 (1) ◽  
pp. 1-24
Author(s):  
Yi Zhang ◽  
Yue Zheng ◽  
Guidong Zhang ◽  
Kun Qian ◽  
Chen Qian ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Gait Recognition ◽  
Wearable Sensors ◽  
Human Identification ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Gait Patterns ◽  
Training Samples ◽  
Augmentation Techniques

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

scholarly journals Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Hu ◽  
Shiqiang Hu ◽  
Xiaoyu Zhang ◽  
Huanlong Zhang ◽  
Lingkun Luo
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Compact Representation ◽  
Feature Descriptor ◽  
Neighbor Distance ◽  
Low Level ◽  
Feature Descriptors ◽  
Nearest Neighbor Distance ◽  
Crowded Scenes

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

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