Unsupervised classification of digital images using enhanced sensor pattern noise

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

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Adaptive Median Filtering Based on Unsupervised Classification of Pixels

Handbook of Research on Computational Intelligence for Engineering, Science, and Business ◽

10.4018/978-1-4666-2518-1.ch011 ◽

2013 ◽

pp. 273-296 ◽

Cited By ~ 2

Author(s):

J. K. Mandal ◽

Somnath Mukhopadhyay

Keyword(s):

Clustering Algorithm ◽

Digital Images ◽

Unsupervised Classification ◽

Median Filtering ◽

Adaptive Window ◽

Novel Approach ◽

Weighted Median ◽

Window Selection ◽

Filtering Approach

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

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Denoising of digital images through PSO based pixel classification

Open Computer Science ◽

10.2478/s13537-013-0111-3 ◽

2013 ◽

Vol 3 (4) ◽

Cited By ~ 1

Author(s):

Somnath Mukhopadhyay ◽

Jyotsna Mandal

Keyword(s):

Digital Images ◽

Fitness Function ◽

Unsupervised Classification ◽

Noisy Image ◽

Pixel Classification ◽

Visual Effects ◽

Swarm Optimization ◽

Image Pixels ◽

Noise Models

AbstractThis paper proposes a de-noising method where the detection and filtering is based on unsupervised classification of pixels. The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function the image pixels are classified using the Particle Swarm Optimization (PSO) technique. The distance function measured similarity/dissimilarity among pixels using not only the intensity values, but also the positions of the pixels. The detection technique enforced PSO based clustering, which is very simple and robust. The filtering operator restored only the noisy pixels keeping noise free pixels intact. Four types of noise models are used to train the digital images and these noisy images are restored using the proposed algorithm. Results demonstrated the effectiveness of the proposed technique. Various benchmark images are used to produce restoration results in terms of PSNR (dB) along with other parametric values. Some visual effects are also presented which conform better restoration of digital images through the proposed technique.

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A DFT-Based Analysis to Discern Between Camera and Scanned Images

Crime Prevention Technologies and Applications for Advancing Criminal Investigation ◽

10.4018/978-1-4666-1758-2.ch001 ◽

2012 ◽

pp. 1-8

Author(s):

Roberto Caldelli ◽

Irene Amerini ◽

Francesco Picchioni

Keyword(s):

Fourier Transform ◽

Discrete Fourier Transform ◽

Digital Images ◽

Experimental Results ◽

Digital Photo ◽

Geometrical Features ◽

Pattern Noise ◽

Photo Camera ◽

Sensor Pattern Noise ◽

Scanned Images

Digital images are generated by different sensors, understanding which kind of sensor has acquired a certain image could be crucial in many application scenarios where digital forensic techniques operate. In this paper a new methodology which permits to establish if a digital photo has been taken by a photo-camera or has been scanned by a scanner is presented. The specific geometrical features of the sensor pattern noise introduced by the sensor are investigated by resorting to a DFT (Discrete Fourier Transform) analysis and consequently the origin of the digital content is assessed. Experimental results are provided to witness the reliability of the proposed technique.

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On the Performance of Li’s Unsupervised Image Classifier and the Optimal Cropping Position of Images for Forensic Investigations

International Journal of Digital Crime and Forensics ◽

10.4018/jdcf.2011010101 ◽

2011 ◽

Vol 3 (1) ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Ahmad Ryad Soobhany ◽

Richard Leary ◽

KP Lam

Keyword(s):

Error Rate ◽

Digital Imaging ◽

Cross Validation ◽

Digital Images ◽

Sample Space ◽

Forensic Investigation ◽

Storage Devices ◽

Pattern Noise ◽

Sensor Pattern Noise ◽

Optimal Cropping

Images from digital imaging devices are prevalent in society. The signatures of these images can be extracted as sensor pattern noise (SPN) and classified according to their source devices. In this paper, the authors assess the reliability of an unsupervised classifier for forensic investigation of digital images recovered from storage devices and to identify the best position for cropping the images before processing. Cross validation was performed on the classifier to assess the error rate and determine the effect of the size of the sample space and the classifier trainer on the performance of the classifier. Moreover, the authors find that the effect of saturation and subsequently the contamination of the SPN in the images affected performance negatively. To alleviate the negative performance, the authors identify the areas of images where less contamination occurs to perform cropping.

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