Focusing on out-of-focus : assessing defocus estimation algorithms for the benefit of automated image masking

Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the " sharpness " of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted " acceptably " sharp throughout the whole image collection. Although none of these three fields has ever properly quantified " acceptably sharp " , it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well-or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper

Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications

Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications

The simultaneity of complementary conditions:re-integrating and balancing analogue and digital matter(s) in basic architectural education

The actual, globally established, general digital procedures in basic architectural education,producing well-behaved, seemingly attractive up-to-date projects, spaces and first general-researchon all scale levels, apparently present a certain growing amount of deficiencies. These limitations surface only gradually, as the state of things on overall extents is generally deemed satisfactory. Some skills, such as “old-fashioned” analogue drawing are gradually eased-out ofundergraduate curricula and overall modus-operandi, due to their apparent slow inefficiencies in regard to various digital media’s rapid readiness, malleability and unproblematic, quotidian availabilities. While this state of things is understandable, it nevertheless presents a definite challenge. The challenge of questioning how the assessment of conditions and especially their representation,is conducted, prior to contextual architectural action(s) of any kind

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.Comment: 32 pages, 21 figure

Automatic image annotation system using deep learning method to analyse ambiguous images

Image annotation has gotten a lot of attention recently because of how quickly picture data has expanded. Together with image analysis and interpretation, image annotation, which may semantically describe images, has a variety of uses in allied industries including urban planning engineering. Even without big data and image identification technologies, it is challenging to manually analyze a diverse variety of photos. The improvements to the Automated Image Annotation (AIA) label system have been the subject of several scholarly research. The authors will discuss how to use image databases and the AIA system in this essay. The proposed method extracts image features from photos using an improved VGG-19, and then uses nearby features to automatically forecast picture labels. The proposed study accounts for both correlations between labels and images as well as correlations within images. The number of labels is also estimated using a label quantity prediction (LQP) model, which improves label prediction precision. The suggested method addresses automatic annotation methodologies for pixel-level images of unusual things while incorporating supervisory information via interactive spherical skins. The genuine things that were converted into metadata and identified as being connected to pre-existing categories were categorized by the authors using a deep learning approach called a conventional neural network (CNN) - supervised. Certain object monitoring systems strive for a high item detection rate (true-positive), followed by a low availability rate (false-positive). The authors created a KD-tree based on k-nearest neighbors (KNN) to speed up annotating. In order to take into account for the collected image backdrop. The proposed method transforms the conventional two-class object detection problem into a multi-class classification problem, breaking the separated and identical distribution estimations on machine learning methodologies. It is also simple to use because it only requires pixel information and ignores any other supporting elements from various color schemes. The following factors are taken into consideration while comparing the five different AIA approaches: main idea, significant contribution, computational framework, computing speed, and annotation accuracy. A set of publicly accessible photos that serve as standards for assessing AIA methods is also provided, along with a brief description of the four common assessment signs