Adaptive skin color classificator

Abstract A lot of computer vision applications benefit from robust skin color classification. But this is a hard challenge due to the various image conditions like camera settings, illumination, light source, shadows and many more. Furthermore people's tans and ethnic groups also extend those conditions. In this work we present a parametric skin color classifier that can be adapted to the conditions of each image or image sequence. This is done by evaluating some previously know skin color pixels which are acquired by applying a face detector. This approach can distinguish skin color from very similar color like lip color or eye brow color. Its high speed and high accuracy makes it appropriate for real time applications such as face tracking and mimic recognition

Towards a multisensor station for automated biodiversity monitoring

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution. (C) 2022 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie

Expression analysis of genes associated with human osteosarcoma tumors shows correlation of RUNX2 overexpression with poor response to chemotherapy

Background: Human osteosarcoma is the most common pediatric bone tumor. There is limited understanding of the molecular mechanisms underlying osteosarcoma oncogenesis, and a lack of good diagnostic as well as prognostic clinical markers for this disease. Recent discoveries have highlighted a potential role of a number of genes including: RECQL4, DOCK5, SPP1, RUNX2, RB1, CDKN1A, P53, IBSP, LSAMP, MYC, TNFRSF1B, BMP2, HISTH2BE, FOS, CCNB1, and CDC5L. Methods: Our objective was to assess relative expression levels of these 16 genes as potential biomarkers of osteosarcoma oncogenesis and chemotherapy response in human tumors. We performed quantitative expression analysis in a panel of 22 human osteosarcoma tumors with differential response to chemotherapy, and 5 normal human osteoblasts.Results: RECQL4, SPP1, RUNX2, and IBSP were significantly overexpressed, and DOCK5, CDKN1A, RB1, P53, and LSAMP showed significant loss of expression relative to normal osteoblasts. In addition to being overexpressed in osteosarcoma tumor samples relative to normal osteoblasts, RUNX2 was the only gene of the 16 to show significant overexpression in tumors that had a poor response to chemotherapy relative to good responders. Conclusion: These data underscore the loss of tumor suppressive pathways and activation of specific oncogenic mechanisms associated with osteosarcoma oncogenesis, while drawing attention to the role of RUNX2 expression as a potential biomarker of chemotherapy failure in osteosarcoma. © 2010 Sadikovic et al; licensee BioMed Central Ltd

Image sequence analysis using relational structures

Submitted to Pattern RecognitionTIB: RN 4106 (106)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Auswertung von digitisierten Fernsehbildern zur Beschreibung bewegter Objekte

SIGLETIB Hannover: RN 3437 (59) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman