Image, Video and Multidimensional Signal Processing

The Lab focuses on sampling, modeling and processing of continuous-time and discrete-time signals and on new design paradigms in which sampling and processing are designed jointly in order to exploit signal properties already in the sampling stage. This approach has the potential to drastically reduce the sampling and processing rates well below the Nyquist rate, typically considered as the ultimate limit for analog to digital conversion. The laboratory facilitates the transition from pure theoretical research to the development, design and implementation of prototype systems ( DOA, MIMO, SAR and more.. ) in areas ranging from bio imaging trough communications, laser optics, cognitive radio, radar systems and graph signal processing.

We are looking for thought leaders, M.Sc. & Ph.D. students who can develop new areas and applications in RADAR and remote sensing. The applicant should be very comfortable with Radar hardware design and will lead research activity. The balance of work between theory and hardware will vary on project-basis, and a successful candidate should be proficient in both aspects. 

Required Background:

• Signal and Systems
• Mavlas
• Random Signals

The Norwegian Colour and Visual Computing Laboratory is an internationally leading research group in the field of colour, image and video processing. The group has today 8 permanent staff members (4 professors, 3 associate professors, and 1 project manager), 6 post-doctoral researchers, 12 PhD students, and several other researchers, visiting researchers, etc. The group is coordinating and participating in a large number of research projects nationally and internationally. These projects include projects from the European Union as Marie Curie Initial Training Networks, national projects from the Norwegian Research Council, regional projects and industrial projects.

In order to extend our current project portfolio, we are looking for a Project Developer who will contribute to developing a long-term vision and roadmap for Colour and Visual Computing research; the group’s EU positioning and research collaboration; and all stages of research proposal development. The successful applicant will work as part of a large multidisciplinary team, with responsibility to organize focus areas, workshops with partners in Norway and abroad to increase the group’s capacity and competency. Lead and assist in writing several proposals, and take an active role in the development of national and EU frameworks for research in Colour and Visual Computing and related fields. A generous budget for running costs will be made available for this activity. Depending on the success of the proposals, it will be expected that the candidate later take some role in managing funded projects. The candidate is also expected to be an active researcher within strategically important research areas, and to some extent contribute to other academic activities in the group (such as supervision of students).

Apply at https://www.jobbnorge.no/en/available-jobs/job/143517/project-developer-in-colour-and-visual-computing 

The internship program at Mitsubishi Electric Research Laboratories (MERL) gives students excellent opportunities to work in an industrial research lab environment side-by-side with world-class researchers. This program emphasizes close collaboration with a particular researcher or members of a small team. MERL considers graduate students from all over the world. As many of our projects benefit from specialized knowledge in a given field, graduate students pursuing a Ph.D. typically fill the majority of internship openings.

MERL is currently seeking an intern to research and develop new methods for coding and compressing 3D point clouds. The ideal candidate will have relevant expertise in computer graphics representations and compression, and in lossy or lossless image or video compression. Familiarity with processing point cloud data related to mobile mapping, laser scanning, or virtual reality would be a plus. Candidates working toward a PhD in related fields such as Electrical Engineering or Computer Science are encouraged to apply. This is a paid internship with a duration of approximately 3 to 6 months.

Qualified candidates are encouraged to apply to opening MM1012 at http://www.merl.com/internship/openings.php.

Additional information about the internship program at MERL and other opportunities can be found at http://www.merl.com/internship/.

Success in the first 180 days will be measured on the IC engineer’s ability to come up to speed on how an optical mouse integrated circuit works by participating with the design team on testing and evaluating new and existing silicon. Low power design experience with knowledge of the trade-offs for synthesis that include both timing closure and power consumption considerations is critical to accomplish the objectives of this job.

A track record of accomplishments with DSP elements and algorithm development for custom ASIC or FPGA along with full custom digital design experience is essential to success as well. For an engineer to excel in this role they will need to possess a solid understanding of electrical engineering where an advanced engineering degree and experience in image processing would be beneficial.

This opportunity offers a competitive salary with benefits in a beautiful city nestled at the foot of the Colorado Rocky Mountains in the city Colorado Springs, Colorado.

Inquire or take action at http://icde.hiring.ink 

Postdoctoral Position – 3D Computer Graphics

The research group Realistic 3D (Multidimensional Signal Processing and Imaging – www.miun.se/stc/realistic3d), which is a part of the STC Research Centre (Sensible Things that Communicate – www.miun.se/stc), is looking for a post-doc in 3D Computer Graphics for the project Quality of Experience for Augmented Telepresence. (www.miun.se/stc/QoEAT). The project integrates technology challenges in the fields of Telepresence and Augmented Reality in the application area of industrial remote operation, in particular considering assessment of such systems. The successful applicant will collaborate with researchers and industrial partners within the project as well as with other researchers at the department. Part of the research will be carried out at the collaborating parties.

Job description: As a postdoctoral fellow you will conduct original research in the area of Computer Graphics, Augmented Reality, Telepresence, and Quality Assessment. The job includes theoretical analysis, practical experiments, algorithm design and software implementation, as well as documentation in the form of scientific articles and reports. Teaching and writing research applications may be included at a level not exceeding 20%.

Workplan: Lightfields and Holography Representation.  The EmergIMG project results from a Portuguese consortium that targets to design a common framework for the representation and quality assessment of emerging imaging modalities, including lightfields and holographic imaging. This consortium aims to boost an international impact in terms of research and standardization. This research grant is part of task one of this project. The main goal of this grant is to propose a representation framework for the two imaging modalities (lightfields and holographic). The representation framework designed in this task has to provide efficient compression, backward compatibility, scalability, random access, error resilience, and low complexity.

http://www.eracareers.pt/opportunities/index.aspx?task=showAnuncioOport…

The Machine Learning and Sensing Laboratory at the University of Florida has an opening for a post-doctoral research associate to develop computer vision and machine learning algorithms for a novel back-scatter x-ray system. The position will be a part of the University of Florida’s “Rays for Roots” project funded by ARPA-E ROOTS program. The researcher will develop novel super-resolution, image processing and root-vs-soil segmentation algorithms, automated feature extraction algorithms, and machine learning methods to map image features to measured values such as soil moisture, soil porosity, and other parameters of interest. The overall project, Rays for Roots – Integrating Backscatter X-Ray Phenotyping, Modeling, and Genetics to Increase Carbon Sequestration and Resource Use Efficiency, aims to develop a high-throughput system for plant root phenotyping using back-scatter x-ray technology in the field. The project team is highly interdisciplinary consisting of computer scientists, plant and soil scientists, nuclear engineers, electrical engineers, and plant modelers providing a unique post-doctoral research experience. Candidates must have a Ph.D. in computer science, computer engineering or a related area with expertise in image processing, image/data analysis and machine learning. To apply: Please send your CV, publication samples, and list of references to Dr. Alina Zare (azare@ece.ufl.edu); Lab website: https://faculty.eng.ufl.edu/alina-zare/; ROOTS project descriptions: http://bit.ly/2jtVxGL.

Location: Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal

Project: With the emergence of consumer grade devices providing 3D geometry information, such as depth sensors, light field cameras, 2D camera arrays and new camera devices (e.g. Light L16), the way visual data is captured and transmitted is changing. Most often, these new devices provide additional geometry information about the visual scene besides the usual 2D texture information, by means of the clever use of optics, sensors (e.g. infrared), illumination (e.g. reflected light) and signal processing techniques. Thus, efficient techniques to represent this geometry information are urgently needed and 3D coding and description techniques have recently received renewed interest. This PhD position targets the development of coding and description schemes on novel 3D representations, such as a point cloud and light field data.

Research grant: The research grant is associated to a yearly renewable contract that includes an experimental period of 6 months. The research grant consists of a tax-free stipend of 980€ per month for PhD positions. The candidates must fulfill the following conditions: 
  • Background on the relevant technical areas. Preference will be given to candidates that better understand visual compression, computer vision and information retrieval fields. 
  • Strong motivation to perform research and to participate in a rich and stimulating group as well as to advance state-of-the-art through the publication of results in international conferences and peer reviewed journals. 
  • Fluent in English and with good skills in technical writing and presenting. 
  • Good programming skills (C/C++, Matlab, etc.) are required. 

The following researchers will supervise the selected candidate: Prof. Fernando Pereira, Prof. João Ascenso and Prof. Catarina Brites (see http://www.img.lx.it.pt/Staff.html for details). The candidate will join a team of staff and PhD students where intense research and development activities in the image processing and coding fields are carried out. 
To apply for the research grant, please submit your application at https://itlisbon.recruiterbox.com/jobs/11034 with the following documents: 
  1. Detailed curriculum vitae with transcripts (mandatory)
  2. A motivation letter (research statement) explaining your interest in the position (mandatory)
  3. Recommendation letter(s) (optional) or a list of individuals with contact information. 

Note that if the above documents are not received, your application may not be considered. Applications shall be received until a suitable candidate for the position is found, but before 15/04/2017. Selected candidates will be submitted to one or more interviews. For any clarifications, please contact Prof. João Ascenso at joao.ascenso@lx.it.pt.

Postdoctoral position on structure-preserving graph signal processing 

The postdoctoral position is funded under the research project GRAPHSIP (Graph Signal Processing - P.I. : O. Lézoray, University of Caen – https://graphsip.greyc.fr) supported by the French National Research Agency and the Region Council of Normandy. Background: ----------- Processing of very large datasets with irregular structure poses significant challenges, as they can be nowa- days collected in numerous domains, from engineering sciences to social networks. Massive datasets rep- resented as graphs can be seen as a set of data samples, with one sample at each vertex in the graph. In such a scenario, the high-dimensional data associated to vertices can be viewed as graph signals. As a consequence, Graph Signal Processing (GSP) has recently emerged as a new methodology for such irreg- ular big data processing [1, 2]. However, classical signal processing methods are defined only for regular grid-like graphs (such as images [3]), and for irregular graphs usual signal processing methods have to be completely rethought. There is therefore a strong interest (both on theoretical and application sides) for the development of a unified theory for the processing and analysis of irregular graph signals. Description of the position: Recently, low cost sensors have brought 3D scanning into the hands of consumers and one can now easily produce 3D colored meshes or point clouds with each vertex described by its position and color (a 3D colored graph signal). However, the quality of the 3D data is not always visually good and several post-production steps are necessary to improve the final quality. Traditional image processing for editing tasks use structure-preserving smoothing filters [4, 5, 6, 7] within a hierarchical framework. Structure-preserving filters distinguish details from major image structures based on color or patch differences. Then, they decompose an image into different layers from coarse scale structures to small scale fine details, making it easier for subsequent detail manipulation (abstraction, simplification, enhancement, completion, ...). Some filters have been extended to 3D meshes but most of them merely handle vertex positions [8, 9] and cannot deal with irregular graph signals [10]. The post-doctoral research scientist will join the image team of the GREYC laboratory located in Caen (UMR CNRS 6072 – https: //www.greyc.fr/en) to develop new methods for editing 3D colored graph signals. He will focus on the adaptation, under a variational formulation for irregular graphs, of structure-preserving signal processing methods based on data-adaptive regularization. Convolutional networks on graphs will be also be of interest [11, 12] for an efficient extraction of data-adaptive features. The developed methods will be investigated for detail manipulation of 3D selfies [13]. Candidate profile:

The candidate must have a recent Ph.D. (within 5 years) in Computer Science or Applied Mathematics. Knowledge in the areas of graphs, image processing, computer vision or computer graphics is also very welcomed. The candidate will perform research and algorithmic development in C++ and solid programming skills are required. Excellent interpersonal skills and the ability to work well individ- ually or as a member of a project team are recommended. Good written and verbal communication skills required, the candidate has to be fluent in English both written and spoken. Working language can be English or French. Location:  Caen, France in the GREYC UMR CNRS laboratory. Situated in the Normandy region of France close to the sea and about 240km west of Paris; the city still has many old quarters, a population of around 120,000; the city area has roughly 250,000 inhabitants. Duration:  One year, starting in September 2017.

Advantages:

Possibility of French courses, participation in transport costs, possibility of restoration on site. Gross Salary:  2500 euros per month. Application:  Interested applicants should submit (by email, in a single pdf file) their Curriculum Vitae, list of publications, a statement of research interests and 2 reference letters. Contact: Olivier Lézoray (olivier.lezoray@unicaen.fr) – https://lezoray.users.greyc.fr. Applications will be admitted until the position is filled. References  [1] D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,” IEEE Signal Process. Mag., vol. 30, no. 3, pp. 83–98, 2013. [2] A. Sandryhaila and J. M. F. Moura, “Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure,” IEEE Signal Processing Magazine, vol. 31, no. 5, pp. 80–90, 2014. [3] O. Lézoray and L. Grady, Image Processing and Analysis with Graphs: Theory and Practice, Digital Imaging and Computer Vision. CRC Press / Taylor and Francis, 2012. [4] Qi Zhang, Xiaoyong Shen, Li Xu, and Jiaya Jia, “Rolling guidance filter,” in European Conference on Computer Vision ECCV, 2014, pp. 815–830. [5] H. Cho, H. Lee, H. Kang, and S. Lee, “Bilateral texture filtering,” ACM Transactions on Graphics, vol. 33, no. 4, pp. 128:1–128:8, 2014. [6] E. S. L. Gastal and M. M. Oliveira, “Domain transform for edge-aware image and video processing,” ACM Transactions on Graphics, vol. 30, no. 4, pp. 69, 2011. [7] L. Xu, C. Lu, Y. Xu, and J. Jia, “Image smoothing via L0 gradient minimization,” ACM Transactions on Graphics, vol. 30, no. 6, pp. 174, 2011. [8] S. Fleishman, I. Drori, and D. Cohen-Or, “Bilateral mesh denoising,” ACM Transactions on Graphics, vol. 22, no. 3, pp. 950–953, 2003. [9] Michael Kolomenkin, Ilan Shimshoni, and Ayellet Tal, “Prominent field for shape processing and analysis of archaeological artifacts,” International Journal of Computer Vision, vol. 94, no. 1, pp. 89–100, 2011. [10] M. Hidane, O. Lézoray, and A. Elmoataz, “Graph signal decomposition for multi-scale detail manipulation,” in International Conference on Image Processing (IEEE), 2014, pp. 2041–2045. [11] Jonathan Masci, Davide Boscaini, Michael M. Bronstein, and Pierre Vandergheynst, “Geodesic convolutional neural networks on riemannian manifolds,” in 2015 IEEE International Conference on Computer Vision Work- shop, ICCV Workshops 2015, Santiago, Chile, December 7-13, 2015, 2015, pp. 832–840. [12] Michael Edwards and Xianghua Xie, “Graph based convolutional neural network,” CoRR, vol. abs/1609.08965, 2016. [13] O. Lézoray, “3d colored mesh graph signals multi-layer morphological enhancement,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, vol. accepted, p. to appear.