We always look for excellent graduate students or postdocs to join the lab and for undergraduate students to do senior projects. If any of the vision sciences catches your imagination and research is your passion, please do contact us to tell us about yourself and learn more about the lab and its research. Research positions for the 2018/2019 academic year are described in this page. Unless stated differently, most projects offer opportunities at all of the M.Sc., Ph.D., and Postdoc levels. If you are looking for a senior project during your BA/BSc, please refer to the Senior Projects page.
Algorithmic puzzle solving for computational archeology
Automatic solving of jigsaw puzzles has been receiving increasing interest in the computational vision community, and our lab has been pushing the abilities of such algorithms to new fronts. Following this initial success (and a follow up senior project on robotic puzzle solving) we will set to study important (and yet unexplored) extensions of the problem, as well as important applications, in particular in computational cultural heritage (and more specifically in computational archeology). Strong background in computer science, algorithms, and computational geometry are a plus. For details please contact: Prof. Ohad Ben-Shahar
From neuroscience to foolproof portable biometrics
Now, more than ever, cyber security is also about the verification and identification of individuals for physical or cyber access control, and in this quest, biometrics has become a primary tool. As a scientific and technological field dedicated to measuring human characteristics, the security that biometrics provides always juggle between robustness, reliability, portability, and affordability. Research directions in this field in our lab lies at the intersection of computational sciences (in particular, computational vision), neuroscience, and psychophysics in order to optimize all these aspects of biometrics simultaneously towards a foolproof, portable, and affordable methods for individual verification and identification in cyber systems. For details please contact: Prof. Ohad Ben-Shahar
Computational vision for the study of animal behavior
Do you care for both computational tools and biological issues? In a long term project with Prof. Ronen Segev from the Life Sciences department we study both behavior and neurophysiology of the archer fish with some visionary applications to futuristic robotics. As part of this project we intend to study and develop fully automatic tools and algorithms for the analysis of the fish behavior, set a fully operational experimental system, and explore the latter in various experiments. For details and how to apply please contact: Prof. Ohad Ben-Shahar.
Computational visual contour completion
Visual completion is a fundamental perception capacity of human vision and a crucial function for computer vision. Previous computational research in the iCVL has explored the visual contour completion problem in a biologically-inspired and perceptual-consistent fashion, pushing a unique approach that considers the problem not in the image plane but rather in a mathematical space that abstracts the primary visual cortex. Candidate interested in this problem will study both computational aspects and perceptual issues. No special background is needed, but previous experience with tools from analysis, differential equations, differential geometry and numerical analysis, as well as background in visual perception are a plus. For details please contact: Prof. Ohad Ben-Shahar
The role of occluder appearance on amodal visual completion
Visual completion is a fundamental perception capacity of human vision and a crucial function for computer vision. Previous perceptual research in the iCVL has explored the effect of various visual cues on the grouping of the inducers, and in this research project we would like to explore the role of the occluder on shape of the computed contour. In particular, we are interested to understand if the shading pattern on the occluder has a role in the perceptual completion outcome. Strong background in Cognitive and Brain sciences is a plus but not a must. For details please contact: Prof. Ohad Ben-Shahar
Shape completion in neglect patients
Visual completion is a fundamental perceptual capacity taht allows human observers experience whole and coherent objects from visual fragments. Does this capacity depends on the integrity of spatial vision? In this project we will explore this question by testing neglect patients who suffer from a particular spatial deficit related to their spatial visual field. Key in this project is the formulation of special stimuli in which normal and neglect patients are predicted to make different completions . Strong background in Cognitive and Brain sciences is a plus. For details please contact: Prof. Ohad Ben-Shahar
While color vision allows humans and other primates to see the world in amazing detail, light in the visible spectrum carries more information than just “red, green and blue”, advanced “Hyperspectral” cameras allow us to explore the would around us in greater detail revealing the hidden colors that lie beyond the capabilities of human vision. Following initial research in the lab that results with new technology for hyperspectral acquisition and reconstruction, several research question emerge about the links between hyperspectral images, color images, and vision. For details please contact: Prof. Ohad Ben-Shahar.
Visual content, images or videos, dominates our world not only because it is rich (after all, “a picture is worth a thousand words”) but because often we tend to believe that “seeing is believing”. This approach has been at the basis of statutory procedures also, allowing images to serve as admissible evidence as long as they are original. But with sophisticated image editing tools such as Photoshop and computer vision techniques such as image inpainting and augmented reality, seeing is no longer believing and visual content can definitely quality as “fake news”. Image forensics attempts to study how to tackle such frauds and in particular, how one can authenticate digital images and other visual content. For details please contact: Prof. Ohad Ben-Shahar
Humans execute rapid eye movements between locations well over 100,000 times a day. These movements are an integral part of our visual system, serving in acquiring relevant information, tracking visual targets, and allowing us to operate in our world. Tracking eye-movements and measuring how they change in response to different visual stimuli provide a window into human visual perception. Using either of the ICVL’s head mounted, remote, and portable eye trackers, we are able to explore various aspects of these eye movements and utilize them for the understanding of human behavior for solving specific computational (and occasionally applied) computational tasks in order to incorporate this knowledge into artificial visual systems. For details please contact: Prof. Ohad Ben-Shahar
Scene and object recognition
The ability of humans to recognize and classify both visual scenes and objects is astonishingly rapid and reliable, even when the number of examples experienced from a particular class is very small. Part of this capacity may be assisted by a hierarchical representation of visual classes, reminiscent of artificial datasets like ImageNet, and computational foundations represented as ontologies of the sort our lab has been studying for some time . While ImageNet is based on hierarchy crafted from lexical relationships, a question remains about the perceptual hierarchy that may be innate to the human visual system and how it may have formed during development. In this research project we will explore this question, first using psychophysical and behavioral tools, followed by a computational inquiry that could eventually lead to better recognition algorithms. For details please contact: Prof. Ohad Ben-Shahar
Deep learning for inverse problems in computer vision
Ill-posed inverse problems in vision (or other domains) are one-to-many problems where the I/O mapping is under-determined. In some very basic sense, the vision problem as a whole is one grand inverse problem. For decades now the computer vision community has been addressing such problems using regularized optimization – a fancy terms to say more constraints are needed to single our certain solutions over others. Could deep learning, the new king in town, do better? In this research project we will explore this topic in depth and tackle some of the most challenging vision problems. For details please contact: Prof. Ohad Ben-Shahar
The phase problem in natural image statistics
The statistics of natural images is a source for great advancements in computer vision, as well as in computational modeling of biological and human vision. In this research project we will explore the implications of certain statistical properties of images for a problem closely related to the Phase Problem in crystallography and investigate applications for image representation, compression, retrieval and search operations. For details please contact: Prof. Ohad Ben-Shahar
Cognitive assistive robotics (Postdocs only)
As the elderly population grows, so does the number of elderly patients with restricted mobility, vision, and dexterity. Service robots can help such people perform important tasks safely, allowing them to function independently in their homes many more year. With the enormous societal and economic impact of such robots no accepted, assistive robotics is emerging as a strong discipline that draw much academic and interest (e.g., see recent workshops in IROS 2014, HRI 2014, and ECCV 2014, to name but few). In this spirit we are starting a multidisciplinary projects in assistive robotics involving several PIs and research in of robotics, computer vision, NLP, plannings, and HRI. A seek a postdoctoral researcher with expertise in at least one of these areas to lead the effort. A successful candidate will not only demonstrate excellent research track record but will have hands-on approach, good programming skills, and some knowledge of ROS (or the ability to acquire one quickly).
For details and how to apply please contact: Prof. Ohad Ben-Shahar or Prof. Ronen Brafman.
Agrovision (Postdocs only)
A forefront application domain of computer vision is agriculture, or what is occasionally known as agrovision . The iCVL has long been involved in agrovision projects as part of agricultural robotics for selective agriculture, an activity that incorporates international research groups, field experiments, and much international travel. We seek candidates to research and develop computer vision algorithms for natural agricultural environments, integration of algorithms into robotic systems (typically using ROS), supervision of undergraduate and graduate students, interaction and cooperation with industries and research groups abroad, and general project management. This is an open call as projects are initiated periodically . For details and how to apply please contact: Prof. Ohad Ben-Shahar.