**3. Data used**

Different types of security-related data will be used for the work provided by the end users:

• Passive millimeter-wave (PMMW) images and video are used for security screening as many materials, including clothing, are transparent to millimeterwaves. The imagers that use this technology, such as those developed by ALFA [1], are therefore installed at security checkpoints to screen people for hidden weapons (including powders, liquids, and gels) and contraband. They are characterized by a low resolution compared to visible images, due to the wavelength used. ALFA's current software automatically detects objects within the spatial and thermal resolution of the system and draws a red box around them. Some examples of this image type are given in **Figures 2–4**. These are then represented at the approximate locations on a generic silhouette to preserve the subject's privacy. However, object classification to automatically distinguish between a threat and a nonthreat object is not currently performed. A new system will be developed to make a classification based on the shape and size of the objects detected in the raw millimeter-wave image. This would reduce the number of false alarms.


### **Figure 2.**

*(a) Left: clothed subject; center: raw millimeter-wave image of subject; right: subject showing hidden suicide bomber belt; (b) left: clothed subject; center: raw millimeter-wave image of subject; right: subject showing hidden gun and knife; (c) left: clothed subject at 10 m; center: millimeter-wave image of subject at 10 m; right: subject showing two hidden bags of powder explosives. Subject with gel pack hidden between the legs and automatic millimeter-wave detection marked + raw millimeter-wave image of subject; right: subject with gel pack hidden under the arm and automatic millimeter-wave detection marked + raw millimeter-wave image of subject.*

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forces.

**Figure 4.**

**Figure 3.**

*4.1.1 State of the art*

**4. Related work and progress**

*Person with hidden object around the hip.*

*Novel Methods for Forensic Multimedia Data Analysis: Part I*

• Video and Image databases with case scenarios will be provided by police

*Automatic object and potential threat detection (ATD) on processed millimeter-wave image on the left and* 

**4.1 Video and image enhancement, filtering, and assessment**

• Handwriting documents will be collected through the involvement of graduate and undergraduate students. We also plan to use the following benchmark data set: IAM Database for Off-line Cursive Handwritten Text http://www.iam.unibe. ch/~zimmerma/iamdb/iamdb.html. The database contains the forms of unconstrained western handwritten text. It includes 27,000 isolated words (400 pages).

In most image applications, the acquired images represent a degraded version of the original scene. These applications include astronomical imaging [2] (e.g., using

*DOI: http://dx.doi.org/10.5772/intechopen.92167*

*privacy protection output to operator on the right.*

*Novel Methods for Forensic Multimedia Data Analysis: Part I DOI: http://dx.doi.org/10.5772/intechopen.92167*

### **Figure 3.**

*Digital Forensic Science*

number of false alarms.

for real-life investigations.

the objects detected in the raw millimeter-wave image. This would reduce the

• Anonymous Data from Text will be collected. These data are freely available on the Web. We propose to perform initial experiments on anonymized data to validate the feasibility of our approach. After authorization of the responsible superiors of the cybercrime unit is obtained, we will use the developed system

• A Telekom company will prepare a speech database obtained under various conditions and under various speech coders and encoders to test the new algorithms.

*(a) Left: clothed subject; center: raw millimeter-wave image of subject; right: subject showing hidden suicide bomber belt; (b) left: clothed subject; center: raw millimeter-wave image of subject; right: subject showing hidden gun and knife; (c) left: clothed subject at 10 m; center: millimeter-wave image of subject at 10 m; right: subject showing two hidden bags of powder explosives. Subject with gel pack hidden between the legs and automatic millimeter-wave detection marked + raw millimeter-wave image of subject; right: subject with gel pack hidden under the arm and automatic millimeter-wave detection marked + raw millimeter-wave image of subject.*

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**Figure 2.**

*Automatic object and potential threat detection (ATD) on processed millimeter-wave image on the left and privacy protection output to operator on the right.*

### **Figure 4.** *Person with hidden object around the hip.*


## **4. Related work and progress**

### **4.1 Video and image enhancement, filtering, and assessment**

### *4.1.1 State of the art*

In most image applications, the acquired images represent a degraded version of the original scene. These applications include astronomical imaging [2] (e.g., using

ground-based imaging systems or extraterrestrial observations of the earth and the planets), commercial photography [3, 4], surveillance and forensics [5, 6], medical imaging [7] (e.g., X-rays, digital angiograms, autoradiographs, MRI, and SPECT), and security tasks where commercial photography and other image modalities like Synthetic Aperture Radar (SAR) [8] and Passive Millimeter (PMMW) [9] are frequently used.

Degradations in such images may appear in different forms. They may be due to a known or an unknown blurring function that leads to the consideration of deconvolution [9–13] and blind deconvolution [3, 14] problems. They may also be due to the use of very low-resolution devices, which lead to the combination of several low-resolution images to obtain a high-resolution one, the so called, super-resolution problem [15, 16] or to the utilization of highly compressed images, which suffer from compression artifacts [17]. These types of degradations must be removed before the images or video sequences are used for classification or decision making. Interestingly, all the problems described above can be formulated within the Bayesian framework [18–20]. A fundamental principle of the Bayesian philosophy is to regard all parameters and unobservable variables as unknown stochastic quantities, assigning probability distributions based on subjective beliefs. Thus, the original image(s), the observation noise, and even the function(s) defining the acquisition process are all treated as samples of random fields, with corresponding prior probability density functions that model our knowledge about the imaging process and the nature of images.
