String-like occluding region extraction for background restoration

In this paper, we propose a method for extracting string-like objects in a still image for background restoration. We assume that the object regions occluding the background are long and narrow, and contrasted in intensity with background. First the method introduces a circle contrast, the intensity difference between a pixel and those on a circle around, to find the occluding string-like regions. Then the signs of the circle contrast are decided so that the occluding regions and backgrounds are well separated, and further enhanced by an optimization process. Extracted regions are removed with interpolation (inpainting) for background restoration. Experimental results on real images show the validity of the proposed method

Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.Authors want to give thanks to the Xunta de Galicia under Grant ED481A and the Spanish Ministry of Economy and Competitiveness under the National Science Program TEC2017-84197-C4-2-R

BOLD Features to Detect Texture-less Objects

Object detection in images withstanding significant clut-ter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and dis-tinctive representation of groups of neighboring line seg-ments aggregated over limited spatial supports and invari-ant to rotation, translation and scale changes. Peculiarly, our proposal allows for leveraging on the inherent strengths of descriptor-based approaches, i.e. robustness to occlu-sion and clutter and scalability with respect to the size of the model library, also when dealing with scarcely textured objects. 1

Taisnes nogriežņu telpiskas tīklveida konfigurācijas algoritmiska atpazīšana no stereopāra

Mācību rakstura mēģinājums telpiski atpazīt specifiskus stereopārus, kurus veido telpisku ģeometrisku grafu divas plakanas projekcijas. Aplūkoto ģeometrisko grafu šķautnes ir taisnes nogriežņi, bet virsotnes šo nogriežņu galapunkti. Ar šādiem grafiem, kurus saucam arī par nogriežņu tīkliem, mēs vienkāršoti un abstrakti modelējam gan dabisku, gan mākslīgu telpiskus tīklus veidojošu līniju struktūru fundamentālo uzbūvi. Piemēram, organisma asinsvadu tīklus vai augu lapu dzīslojumus bioloģijā, vai dažādas stieņu konstrukcijas sadzīvē un industrijā. Šādā nostādnē algoritmiskajā apstrādē dominē struktūru kombinatoriskais aspekts, kuru tad arī mēs izvēršam savā darbā. Mūsu mērķis bija pārliecināties, ka samērā primitīva stereopāru analīze ar precizitāti līdz zināmai neviennozīmībai ļauj telpiski rekonstruēt sintezētus projicētos taisnes nogriežņu tīklus. Galvenais moments ir tas, ka katru taisnes nogriezni pilnībā raksturo tā galapunktu pāris. Šo pāru atrašana ir mūsu telpiskās ģeometrijas rekonstruēšanas tehnikas būtība, kuru balstām vienkāršā rupja spēka pieejā, realizējot galapunktu pāru pilnu polinomiāla apjoma pārlasi ar heiristikas elementiem rezultāta optimizācijā. Galvenie skatītie un risinātie tehniskie jautājumi ir telpisko un projicēto nogriežņu svarīgāko attieksmju precizēšana, specifiska stereopāru nogriežņu datu struktūra, pilnās telpisko nogriežņu konfigurācijas konstruēšana un tās redukcija ar neviennozīmības atrisināšanas heiristiku, telpisko nogriežņu rekonstruēšanas demonstrācijas piemēru sērijas. Noslēgumā formulēti dažāda rakstura papildjautājumi, uz kuriem būtu jāatbild tālākos pētījumos un kuri varētu noderēt arī kā mācību darbu temati

Brain White Matter Hyperintensity Lesion Characterization in T2 Fluid-Attenuated Inversion Recovery Magnetic Resonance Images: Shape, Texture, and Potential Growth

Prior methods in characterizing age-related white matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have mainly been limited to understanding the sizes of, and occasionally the locations of WMH lesions. Systematic morphological characterization has been missing. In this work, we proposed innovative methods to fill this knowledge gap. We developed an innovative and proof-of-concept method to characterize and quantify the shape (based on Zernike transformation) and texture (based on fuzzy logic) of WMH lesions. We have also developed a multi-dimension feature vector approach to cluster WMH lesions into distinctive groups based on their shape and then texture features. We then developed an approach to calculate the potential growth index (PGI) of WMH lesions based on the image intensity distributions at the edge of the WMH lesions using a region-growing algorithm. High-quality T2 FLAIR images containing clearly identifiable WMH lesions with various sizes from six cognitively normal older adults were used in our method development Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH group clusters in terms of either the shape (P = 1.06 × 10−2) or the texture (P < 1 × 10−20) features. In conclusion, we propose a systematic framework on which the shape and texture features of WMH lesions can be quantified and may be used to predict lesion growth in older adults

Autonomous Robotic Grasping in Unstructured Environments

A crucial problem in robotics is interacting with known or novel objects in unstructured environments. While the convergence of a multitude of research advances is required to address this problem, our goal is to describe a framework that employs the robot\u27s visual perception to identify and execute an appropriate grasp to pick and place novel objects. Analytical approaches explore for solutions through kinematic and dynamic formulations. On the other hand, data-driven methods retrieve grasps according to their prior knowledge of either the target object, human experience, or through information obtained from acquired data. In this dissertation, we propose a framework based on the supporting principle that potential contacting regions for a stable grasp can be found by searching for (i) sharp discontinuities and (ii) regions of locally maximal principal curvature in the depth map. In addition to suggestions from empirical evidence, we discuss this principle by applying the concept of force-closure and wrench convexes. The key point is that no prior knowledge of objects is utilized in the grasp planning process; however, the obtained results show that the approach is capable to deal successfully with objects of different shapes and sizes. We believe that the proposed work is novel because the description of the visible portion of objects by the aforementioned edges appearing in the depth map facilitates the process of grasp set-point extraction in the same way as image processing methods with the focus on small-size 2D image areas rather than clustering and analyzing huge sets of 3D point-cloud coordinates. In fact, this approach dismisses reconstruction of objects. These features result in low computational costs and make it possible to run the proposed algorithm in real-time. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both simulation and real-world experiment setups