Forgery Detection in a Questioned Hyperspectral Document Image using K-means Clustering

10.36227/techrxiv.12580028.v1 ◽

2020 ◽

Author(s):

Maria Yaseen ◽

Rammal Aftab ◽

Rimsha mahrukh

Keyword(s):

Hyperspectral Imaging ◽

Spectral Response ◽

Document Image ◽

Spectral Signature ◽

Detection Techniques ◽

Spectral Signatures ◽

Spectral Bands ◽

Mismatch Detection ◽

The One ◽

Imaging Sensor

Hyperspectral imaging allows for analysis of images in several hundred of spectral bands depending on the spectral resolution of the imaging sensor. Hyperspectral document image is the one which has been captured by a hyperspectral camera so that the document can be observed in the different bands on the basis of their unique spectral signatures. To detect the forgery in a document various Ink mismatch detection techniques based on hyperspectral imaging have presented vast potential in differentiating visually similar inks. Inks of different materials exhibit different spectral signature even if they have the same color. Hyperspectral analysis of document images allows identification and discrimination of visually similar inks. Based on this analysis forensic experts can identify the authenticity of the document. In this paper an extensive ink mismatch detection technique is presented which uses KMean Clustering to identify different inks on the basis of their unique spectral response and separates them into different clusters.

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Underwater Spectral Imaging System Based on Liquid Crystal Tunable Filter

Journal of Marine Science and Engineering ◽

10.3390/jmse9111206 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1206

Author(s):

Hong Song ◽

Syed Raza Mehdi ◽

Chaopeng Wu ◽

Zixin Li ◽

Hai Gong ◽

...

Keyword(s):

Liquid Crystal ◽

Imaging System ◽

Spectral Response ◽

Image Data ◽

Spectral Imaging ◽

Tunable Filter ◽

Test Analysis ◽

Spectral Bands ◽

Collecting Efficiency ◽

Imaging Sensor

In the past decade, underwater spectral imaging (USI) has shown great potential in underwater exploration for its high spectral and spatial resolution. This proposal presents a stare-type USI system combined with the liquid crystal tunable filter (LCTF) spectral splitting device. Considering the working features of LCTF and the theoretical model of USI, the core structure containing “imaging lens-LCTF-imaging sensor” is designed and developed. The system is compact, and the optical geometry is constructed minimally. The spectral calibration test analysis proved that the spectral response range of the system covers a full band of 400 nm to 700 nm with the highest spectral resolution between 6.7 nm and 18.5 nm. The experiments show that the system can quickly collect high-quality spectral image data by switching between different spectral bands arbitrarily. The designed prototype provides a feasible and reliable spectral imaging solution for in situ underwater targets observation with high spectrum collecting efficiency.

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Histopathology Feature Mining and Association with Hyperspectral Imaging for the Detection of Squamous Neoplasia

Scientific Reports ◽

10.1038/s41598-019-54139-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Guolan Lu ◽

Dongsheng Wang ◽

Xulei Qin ◽

Susan Muller ◽

James V. Little ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Digital Pathology ◽

Disease Status ◽

Spectral Signature ◽

Spectral Difference ◽

Neoplastic Tissue ◽

Spectral Signatures ◽

Architectural Features ◽

Histological Images ◽

Feature Mining

AbstractHyperspectral imaging (HSI) is a noninvasive optical modality that holds promise for early detection of tongue lesions. Spectral signatures generated by HSI contain important diagnostic information that can be used to predict the disease status of the examined biological tissue. However, the underlying pathophysiology for the spectral difference between normal and neoplastic tissue is not well understood. Here, we propose to leverage digital pathology and predictive modeling to select the most discriminative features from digitized histological images to differentiate tongue neoplasia from normal tissue, and then correlate these discriminative pathological features with corresponding spectral signatures of the neoplasia. We demonstrated the association between the histological features quantifying the architectural features of neoplasia on a microscopic scale, with the spectral signature of the corresponding tissue measured by HSI on a macroscopic level. This study may provide insight into the pathophysiology underlying the hyperspectral dataset.

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Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning

Remote Sensing ◽

10.3390/rs12132111 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2111

Author(s):

Adam Papp ◽

Julian Pegoraro ◽

Daniel Bauer ◽

Philip Taupe ◽

Christoph Wiesmeyr ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Video Processing ◽

Dynamic Range ◽

State Of The Art ◽

Image Resolution ◽

Spectral Signature ◽

Imaging Technology ◽

Spectral Signatures ◽

Dense Vegetation

Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation.

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Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽

10.3390/s20143995 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3995 ◽

Cited By ~ 3

Author(s):

Ning Liu ◽

Ruomei Zhao ◽

Lang Qiao ◽

Yao Zhang ◽

Minzan Li ◽

...

Keyword(s):

Growth Stage ◽

Spectral Response ◽

Classification Model ◽

Growth Stages ◽

Potato Field ◽

Spectral Bands ◽

Svm Model ◽

Selection Algorithms ◽

Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

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Forensic analysis of beverage stains using hyperspectral imaging

Scientific Reports ◽

10.1038/s41598-021-85737-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Binu Melit Devassy ◽

Sony George

Keyword(s):

Hyperspectral Imaging ◽

Crime Scene ◽

Forensic Analysis ◽

Classification Model ◽

Support Vector ◽

Additional Information ◽

Non Invasive ◽

Spectral Bands ◽

Data Dimensionality Reduction ◽

Gradient Based

AbstractDocumentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.

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Hyperspectral Imaging for Bloodstain Identification

Sensors ◽

10.3390/s21093045 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3045

Author(s):

Maheen Zulfiqar ◽

Muhammad Ahmad ◽

Ahmed Sohaib ◽

Manuel Mazzara ◽

Salvatore Distefano

Keyword(s):

Hyperspectral Imaging ◽

Dna Analysis ◽

Forensic Sciences ◽

Blood Protein ◽

Chemical Methods ◽

Acrylic Paint ◽

Spectral Bands ◽

Nail Polish ◽

Spatial Dimensions ◽

Non Destructive

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

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Application of Hyperspectral Imaging in the Assessment of Drought and Salt Stress in Magneto-Primed Triticale Seeds

Plants ◽

10.3390/plants10050835 ◽

2021 ◽

Vol 10 (5) ◽

pp. 835

Author(s):

Jose Alvarez ◽

Elvira Martinez ◽

Belén Diezma

Keyword(s):

Salt Stress ◽

Hyperspectral Imaging ◽

Human Observer ◽

Negative Effects ◽

Stress Symptoms ◽

Data Variability ◽

Drought And Salt Stress ◽

The One ◽

Almost All ◽

Germination Parameters

Hyperspectral imaging is an appropriate method to thoroughly investigate the microscopic structure of internally heterogeneous agro-food products. By using hyperspectral technology, identifying stress symptoms associated with salinity, before a human observer, is possible, and has obvious benefits. The objective of this paper was to prove the suitability of this technique for the analysis of Triticale seeds subjected to both magneto-priming and drought and salt stress conditions, in terms of image differences obtained among treatments. It is known that, on the one hand, drought and salt stress treatments have negative effects on seeds of almost all species, and on the other hand, magneto-priming enhances seed germination parameters. Thus, this study aimed to relate hyperspectral imaging values—neither positive nor negative in themselves—to the effects mentioned above. Two main conclusions were reached: Firstly, the hyperspectral application is a feasible method for exploring the Triticale structure and for making distinctions under different drought and salt stress treatments, in line with the data variability obtained. Secondly, the lower spectral reflectance in some treatments—in the 400–1000 nm segment—is the result of a great number of chemical compounds in the seed that could be related to magneto-priming.

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Deepfake videos: synthesis and detection techniques –a survey

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210625 ◽

2021 ◽

pp. 1-21

Author(s):

Shahela Saif ◽

Samabia Tehseen

Keyword(s):

Social Systems ◽

Generative Models ◽

Detection Methods ◽

Future Research ◽

Fake News ◽

Research Directions ◽

Detection Techniques ◽

Future Research Directions ◽

Video Creation ◽

The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

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Applying satellite images to spectral signature development of maize production (Zea mays L.) under colombia’s middle tropics conditions

ENTRAMADO ◽

10.18041/1900-3803/entramado.2.5734 ◽

2019 ◽

Vol 15 (2) ◽

pp. 256-262

Author(s):

Edier Fernando Ávila Vélez ◽

Natalia Escobar Escobar ◽

Carlos Francisco Morantes Choconta

Keyword(s):

Satellite Images ◽

Near Infrared ◽

Spectral Response ◽

Spectral Signature ◽

Growth Stages ◽

Total Production ◽

Radiometric Calibration ◽

Maize Crop ◽

Maize Production ◽

Production Stages

Maize is currently the world’s second largest crop in terms of production, after wheat and rice. It is the first cereal as for grain yield per hectare and the second, after wheat, regarding total production. Maize has great economic importance worldwide, both as human and/or animal food, and as a source of a large number of industrial products. New digital technologies are allowing greater monitoring of farming production stages. This research developed the spectral signature of maize fields ground covers across different growth stages (2 months, 2.3 months and 4.3 months). Similarly, this research paper proposes the application of a methodology based on four phases: 1. Maize crop georeferencing, 2. Satellite images selection, 3. Radiometric calibration of images 4. Spectral signature development of maize crops. Spectral response or signature of maize crops at visible and near-infrared wavelengths was obtained, indicating significant changes in crops growth. The use of satellite imagery becomes an interesting tool that introduces an approach towards more accurate and controlled monitoring systems in agricultural production.

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