Unorganized Images Research Articles

A Deep Learning Based Prediction of Arabic Manuscripts Handwriting Style

With the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneously. Therefore, this study aims to collect Arabic manuscripts images in a dataset and predict their handwriting styles using the most powerful and trending technologies. There are many types of Arabic handwriting styles, including Al-Reqaa, Al-Nask, Al-Thulth, Al-Kufi, Al-Hur, Al-Diwani, Al-Farsi, Al-Ejaza, Al-Maghrabi, Al-Taqraa, etc. However, the study classified the collected dataset images according to the handwriting styles and focused on only six types of handwriting styles that existed in the collected Arabic manuscripts. To reach our goal, we applied the MobileNet pre-trained deep learning model on our classified dataset images to automatically capture and extract the features from them. Afterward, we evaluated the performance of the developed model by computing its recorded evaluation metrics. We reached that MobileNet convolutional neural network is a promising technology since it reached 0.9583 as the highest recorded accuracy and 0.9633 as the average F-score

Unsupervised Meta-Learning of Figure-Ground Segmentation via Imitating Visual Effects

This paper presents a “learning to learn” approach to figureground image segmentation. By exploring webly-abundant images of specific visual effects, our method can effectively learn the visual-effect internal representations in an unsupervised manner and uses this knowledge to differentiate the figure from the ground in an image. Specifically, we formulate the meta-learning process as a compositional image editing task that learns to imitate a certain visual effect and derive the corresponding internal representation. Such a generative process can help instantiate the underlying figure-ground notion and enables the system to accomplish the intended image segmentation. Whereas existing generative methods are mostly tailored to image synthesis or style transfer, our approach offers a flexible learning mechanism to model a general concept of figure-ground segmentation from unorganized images that have no explicit pixel-level annotations. We validate our approach via extensive experiments on six datasets to demonstrate that the proposed model can be end-to-end trained without ground-truth pixel labeling yet outperforms the existing methods of unsupervised segmentation tasks.

EFFICIENT WIDE BASELINE STRUCTURE FROM MOTION

Abstract. This paper presents a Structure from Motion approach for complex unorganized image sets. To achieve high accuracy and robustness, image triplets are employed and (an approximate) camera calibration is assumed to be known. The focus lies on a complete linking of images even in case of large image distortions, e.g., caused by wide baselines, as well as weak baselines. A method for embedding image descriptors into Hamming space is proposed for fast image similarity ranking. The later is employed to limit the number of pairs to be matched by a wide baseline method. An iterative graph-based approach is proposed formulating image linking as the search for a terminal Steiner minimum tree in a line graph. Finally, additional links are determined and employed to improve the accuracy of the pose estimation. By this means, loops in long image sequences are implicitly closed. The potential of the proposed approach is demonstrated by results for several complex image sets also in comparison with VisualSFM.

EFFICIENT WIDE BASELINE STRUCTURE FROM MOTION

This paper presents a Structure from Motion approach for complex unorganized image sets. To achieve high accuracy and robustness, image triplets are employed and (an approximate) camera calibration is assumed to be known. The focus lies on a complete linking of images even in case of large image distortions, e.g., caused by wide baselines, as well as weak baselines. A method for embedding image descriptors into Hamming space is proposed for fast image similarity ranking. The later is employed to limit the number of pairs to be matched by a wide baseline method. An iterative graph-based approach is proposed formulating image linking as the search for a terminal Steiner minimum tree in a line graph. Finally, additional links are determined and employed to improve the accuracy of the pose estimation. By this means, loops in long image sequences are implicitly closed. The potential of the proposed approach is demonstrated by results for several complex image sets also in comparison with VisualSFM.

User-assisted image compositing for photographic lighting

Good lighting is crucial in photography and can make the difference between a great picture and a discarded image. Traditionally, professional photographers work in a studio with many light sources carefully set up, with the goal of getting a near-final image at exposure time, with post-processing mostly focusing on aspects orthogonal to lighting. Recently, a new workflow has emerged for architectural and commercial photography, where photographers capture several photos from a fixed viewpoint with a moving light source. The objective is not to produce the final result immediately, but rather to capture useful data that are later processed, often significantly, in photo editing software to create the final well-lit image. This new workflow is flexible, requires less manual setup, and works well for time-constrained shots. But dealing with several tens of unorganized layers is painstaking, requiring hours to days of manual effort, as well as advanced photo editing skills. Our objective in this paper is to make the compositing step easier. We describe a set of optimizations to assemble the input images to create a few basis lights that correspond to common goals pursued by photographers, e.g., accentuating edges and curved regions. We also introduce modifiers that capture standard photographic tasks, e.g., to alter the lights to soften highlights and shadows, akin to umbrellas and soft boxes. Our experiments with novice and professional users show that our approach allows them to quickly create satisfying results, whereas working with unorganized images requires considerably more time. Casual users particularly benefit from our approach since coping with a large number of layers is daunting for them and requires significant experience.

Archeological excavation monitoring using dense stereo matching techniques

Abstract Several new tools to obtain three-dimensional information from unorganized image sets are now available for the public use. The main advantage of this software, which is based on dense stereo matching, is the possibility to generate 3D content without the need of high-cost hardware (e.g. 3D scanning devices). Nevertheless, their use in real-world application domains (like cultural heritage) is still not very diffused, due to the non-straightforward usability of the raw data produced. In this paper, we investigate the use of automatic dense stereo reconstruction tools for the monitoring of an excavation site. A methodology for the effective acquisition and processing of data is presented. In addition, the results of the data assessment demonstrate the repeatability of the data acquisition process, which is a key factor when qualitative analysis is performed. The use of three-dimensional data is integrated in an open source mesh processing tool, thus showing that a spatio-temporal analysis can be performed in a very intuitive way using off-the-shelf or free/open digital tools. Moreover, the use of peculiar rendering and the creation of snapshots from arbitrary points of view increase the amount of documentation data, and suggest a perfect integration of data produced with dense stereo matching in the future standard documentation for excavation monitoring.

What can Computer Graphics expect from 3D Computer Vision?

Abstract Computer Vision is a discipline whose ultimate goal is to interpret optical images of real scenes. It is well understood that such a problem is cursed by ambiguity of interpretation and uncertainty of evidence. Despite imperfectness of results due to the scenes never following our prior models exactly, Computer Vision has achieved a significant progress in the past two decades. This talk will outline the quest of 3D Computer Vision by describing a processing pipeline that receives a heap of unorganized images from unknown cameras and produces a consistent 3D geometric model together with camera calibrations. We will see how new algorithms allow the standard conception of the pipeline as a series of independent processing steps gradually transform to a single complex, yet efficient vision task. We will identify some points where linking Computer Vision and Computer Graphics would bring significant progress.