DSLR Imperfections Extraction from Image for Source Detection

Digital images frequently contain the objects which are not true for the depicted scene that can completely change the human perception of provided information. This article proposes an approach to search of the digital image forensics caused by distortions and noise of the Digital Single Lens Reflex (DSLR) devices. The tampered object detection algorithms can find these distortions and imperfections despite their size. Therefore it is necessary to reduce influence of these factors in various stages of image processing and forming in the digital devices. The method of determining the reliability of test image forming in the device under consideration based on the pixel non-uniformity of the device that appears at incident light was suggested

Weld bead detection based on 3D geometric features and machine learning approaches

14 p.Weld bead detection is essential for automated welding inspection processes. The non-invasive passive techniques, such as photogrammetry, are quickly evolving to provide a 3D point cloud with submillimeter precision and spatial resolution. However, its application in weld visual inspection has not been extensively studied. The derived 3D point clouds, despite the lack of topological information, store significant information for the weld-plaque segmentation. Although the weld bead detection is being carried out over images or based on laser profiles, its characterization by means of 3D geometrical features has not been assessed. Moreover, it is possible to combine machine learning approaches and the 3D features in order to realize the full potential of the weld bead segmentation of 3D submillimeter point clouds. In this paper, the novelty is focused on the study of 3D features on real cases to identify the most relevant ones for weld bead detection on the basis of the information gain. For this novel contribution, the influence of neighborhood size for covariance matrix computation, decision tree algorithms, and split criteria are analyzed to assess the optimal results. The classification accuracy is evaluated by the degree of agreement of the classified data by the kappa index and the area under the receiver operating characteristic (ROC) curve. The experimental results show that the proposed novel methodology performs better than 0.85 for the kappa index and better than 0.95 for ROC area.S

Photogrammetry for 3D Reconstruction in SOLIDWORKS and its Applications in Industry

Indiana University-Purdue University Indianapolis (IUPUI)Close range, image based photogrammetry and LIDAR laser scanning technique are commonly utilized methodologies to snap real objects.3D models of already existing model or parts can be reconstructed by laser scanning and photogrammetry. These 3D models can be useful in applications like quality inspection, reverse engineering. With these techniques, they have their merits and limitations. Though laser scanners have higher accuracy, they require higher initial investment. Close-range photogrammetry is known for its simplicity, versatility and e ective detection of complex surfaces and 3D measurement of parts. But photogrammetry techniques can be initiated with comparatively much lower initial cost with acceptable accuracy. Currently, many industries are using photogrammetry for reverse engineering, quality inspection purposes. But, for photogrammetric object reconstruction, they are using di erent softwares. Industrial researchers are using commercial/open source codes for reconstruction and another stand-alone software for reverse engineering and mesh deviation analysis. So the problem statement here for this thesis is to integrate Photogrammetry, reverse engineering and deviation analysis to make one state-of-the-art work ow. xx The objectives of this thesis are as follows: 1. Comparative study between available source codes and identify suitable and stable code for integration; understand the photogrammetry methodology of that particular code. 2. To create a taskpane add-in using API for Integration of selected photogrammetry methodology and facilitate methodology with parameters. 3. To demonstrate the photogrammetric work ow followed by a reverse engineering case studies to showcase the potential of integration. 4. Parametric study for number of images vs accuracy 5. Comparison of Scan results, photogrammetry results with actual CAD dat

Termografía activa y fotogrametría de objeto cercano para la detección, medición y evaluación de defectos e imperfecciones en uniones soldadas

Tesis por compendio de publicaciones[ES]La inspección de uniones soldadas constituye un requerimiento de seguridad cuya importancia a día de hoy queda lejos de toda duda. Gran parte de la defectología en uniones soldadas es de tipo superficial y por ello la normativa internacional de calidad exige la valoración de cualquier discontinuidad geométrica en los cordones de soldadura. Además de las discontinuidades existen defectos con un alto potencial de peligrosidad como son las fisuras. Estas, aunque pueden ser pequeñas, bajo estados altos de solicitaciones térmicas o mecánicas tienden a propagarse, pudiendo provocar un colapso inminente y repentino de la unión, con drásticas consecuencias en términos de funcionalidad y, sobre todo, de seguridad. En la presente Tesis Doctoral se pretende dotar al inspector de soldadura de nuevas herramientas. Por un lado, se pretende estudiar una técnica tan ventajosa en términos de detección como es la termografía activa para diseñar protocolos de ensayo sencillos y aplicables en ambientes exteriores que permitan la detección, caracterización y medición de fisuras superficiales mediante el estudio de temperaturas y de velocidades de enfriamiento después de inducir en el material una estimulación energética ligera y segura. Por otro lado, se pretende facilitar la labor de inspección visual mediante la posibilidad de recrear modelos tridimensionales de soldaduras desde imágenes tomadas con una cámara fotográfica comercial. Estos modelos se generan en formatos tales que puedan ser exportables a software CAD para su minucioso análisis metrológico, posibilitando la medición sobre el modelo y sin necesidad de que el inspector acuda al lugar de la instalación de la soldadura ni realizar medidas con los instrumentos físicos tradicionales. A tales efectos se diseña una investigación que estudia inicialmente de forma separada la aplicación de la técnica de la termografía activa y la referente a la fotogrametría de objeto cercano para, finalmente, confluir ambas en el desarrollo de modelos predictivos de profundidad en fisuras que permita, desde una simple imagen térmica del plano superficial de la soldadura, predecir la profundidad de la fisura dentro del material

Machine Learning Advances for Practical Problems in Computer Vision

Convolutional neural networks (CNN) have become the de facto standard for computer vision tasks, due to their unparalleled performance and versatility. Although deep learning removes the need for extensive hand engineered features for every task, real world applications of CNNs still often require considerable engineering effort to produce usable results. In this thesis, we explore solutions to problems that arise in practical applications of CNNs. We address a rarely acknowledged weakness of CNN object detectors: the tendency to emit many excess detection boxes per object, which must be pruned by non maximum suppression (NMS). This practice relies on the assumption that highly overlapping boxes are excess, which is problematic when objects are occluding overlapping detections are actually required. Therefore we propose a novel loss function that incentivises a CNN to emit exactly one detection per object, making NMS unnecessary. Another common problem when deploying a CNN in the real world is domain shift - CNNs can be surprisingly vulnerable to sometimes quite subtle differences between the images they encounter at deployment and those they are trained on. We investigate the role that texture plays in domain shift, and propose a novel data augmentation technique using style transfer to train CNNs that are more robust against shifts in texture. We demonstrate that this technique results in better domain transfer on several datasets, without requiring any domain specific knowledge. In collaboration with AstraZeneca, we develop an embedding space for cellular images collected in a high throughput imaging screen as part of a drug discovery project. This uses a combination of techniques to embed the images in 2D space such that similar images are nearby, for the purpose of visualization and data exploration. The images are also clustered automatically, splitting the large dataset into a smaller number of clusters that display a common phenotype. This allows biologists to quickly triage the high throughput screen, selecting a small subset of promising phenotypes for further investigation. Finally, we investigate an unusual form of domain bias that manifested in a real-world visual binary classification project for counterfeit detection. We confirm that CNNs are able to ``cheat'' the task by exploiting a strong correlation between class label and the specific camera that acquired the image, and show that this reliably occurs when the correlation is present. We also investigate the question of how exactly the CNN is able to infer camera type from image pixels, given that this is impossible to the human eye. The contributions in this thesis are of practical value to deep learning practitioners working on a variety of problems in the field of computer vision

DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DepthCut, a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 15 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings.Comment: 12 page

An image recapture detection algorithm based on learning dictionaries of edge profiles

With today's digital camera technology, high-quality images can be recaptured from an liquid crystal display (LCD) monitor screen with relative ease. An attacker may choose to recapture a forged image in order to conceal imperfections and to increase its authenticity. In this paper, we address the problem of detecting images recaptured from LCD monitors. We provide a comprehensive overview of the traces found in recaptured images, and we argue that aliasing and blurriness are the least scene dependent features. We then show how aliasing can be eliminated by setting the capture parameters to predetermined values. Driven by this finding, we propose a recapture detection algorithm based on learned edge blurriness. Two sets of dictionaries are trained using the K-singular value decomposition approach from the line spread profiles of selected edges from single captured and recaptured images. An support vector machine classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high-quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images

Detection of humidity-treated aged latent prints using cyanoacrylate fuming and a reflected ultraviolet imaging system (RUVIS)

For the past several decades, challenges in the detection and collection of latent prints exposed to harsh environmental conditions have inspired research in pretreatment methods prior to the application of chemical, physical, or optical-based enhancement techniques. Some of the difficulties associated with processing degraded latent prints are attributed to dehydration, alterations in chemical composition, and physical disturbance of ridge detail. This study seeks to investigate the effectiveness of humidity, cyanoacrylate fuming method (CFM), and a reflected ultraviolet imaging system (RUVIS) on the detection and collection of aged latent palmprints. Prints were exposed to air flow and ultraviolet (UV) light for a period of 0 to 28 days, and subsequently treated with either cool or warm humidity and CFM. RUVIS was then utilized to detect and capture friction ridge detail after each treatment step. Improvements in RUVIS detection between treatments were evaluated based on four response factors: minutiae count, percent print recovery, ridge thickness and contrast. By measuring these factors, each latent print photograph was able to be converted to quantifiable data to facilitate statistical analysis of potential differences or improvements between treatments. The results demonstrate that the application of 80% relative humidity successfully revived aged latent palmprints across all factors. The combined effect of humidity followed v by CFM treatment and RUVIS detection was greatest for minutiae count and ridge thickness, while percent print recovery and contrast demonstrated more modest improvements when compared to control prints. Additionally, cool temperature treatments outperformed warm temperature treatments across all factors except contrast. The data therefore suggest that to achieve print rejuvenation and overall improvements in RUVIS detection, combined cool humidity and CFM is more effective than humidity alone. The data also indicate a potential correlation between temperature treatments and latent print age. Warm humidity combined with CFM appeared to best enhance RUVIS images on fresher prints of a few days to one week old, while cool humidity and CFM appeared to maximally enhance RUVIS images on prints of several weeks old