Cybersecurity is a topic of growing interest. Do you have hands-on exercises that match the skills and levels of your students? Over the last few years, we have worked on making it easier to create, modify, and deploy exercises with assessment questions. EDURange is an open source project with exercises that span a wide range and can serve as templates for new ones. In addition to providing a framework for editing exercises, EDURange also allows Instructors to see student interaction and offer hints while they are doing the exercise. The features, that support this include chat with the instructor and machine learning algorithms for identifying which students need help.

Cybersecurity is an ever-evolving field that demands more workers and a wider array of knowledge every year. As such, cybersecurity education remains essential - not just for professionals, but for developers and non-technical roles as well. Due to this, hands-on cybersecurity exercises, such as the ones in the eduRange platform, are increasingly important. EduRange aims to be a flexible, intuitive cybersecurity platform that allows instructors to tailor pre-existing scenarios to their classes' needs. However, when students become stuck or frustrated, learning grinds to a halt. To combat this discouragement, we want to create a semi-automated hint system that can consistently identify struggling students. Such a hint system, however, requires a large quantity of data, which can be difficult to obtain through classroom testing alone.

This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly. Hands-on exercises are essential for learning since they enable students to practice their skills. In cybersecurity, hands-on exercises are often complex and require knowledge of many topics. Therefore, students may miss solutions due to gaps in their knowledge and become frustrated, which impedes their learning. Targeted aid by the instructor helps, but since the instructor's time is limited, efficient ways to detect struggling students are needed. This paper develops automated tools to predict when a student is having difficulty. We formed a dataset with the actions of 313 students from two countries and two learning environments: KYPO CRP and EDURange. These data are used in machine learning algorithms to predict the success of students in exercises deployed in these environments. After extracting features from the data, we trained and cross-validated eight classifiers for predicting the exercise outcome and evaluated their predictive power. The contribution of this paper is comparing two approaches to feature engineering, modeling, and classification performance on data from two learning environments. Using the features from either learning environment, we were able to detect and distinguish between successful and struggling students. A decision tree classifier achieved the highest balanced accuracy and sensitivity with data from both learning environments. The results show that activity data from cybersecurity exercises are suitable for predicting student success. In a potential application, such models can aid instructors in detecting struggling students and providing targeted help. We publish data and code for building these models so that others can adopt or adapt them.

Cybersecurity students need to develop practical skills such as using command-line tools. Hands-on exercises are the most direct way to assess these skills, but assessing students' mastery is a challenging task for instructors. We aim to alleviate this issue by modeling and visualizing student progress automatically throughout the exercise. The progress is summarized by graph models based on the shell commands students typed to achieve discrete tasks within the exercise. We implemented two types of models and compared them using data from 46 students at two universities. To evaluate our models, we surveyed 22 experienced computing instructors and qualitatively analyzed their responses. The majority of instructors interpreted the graph models effectively and identified strengths, weaknesses, and assessment use cases for each model. Based on the evaluation, we provide recommendations to instructors and explain how our graph models innovate teaching and promote further research. The impact of this paper is threefold. First, it demonstrates how multiple institutions can collaborate to share approaches to modeling student progress in hands-on exercises. Second, our modeling techniques generalize to data from different environments to support student assessment, even outside the cybersecurity domain. Third, we share the acquired data and open-source software so that others can use the models in their classes or research.

The critical shortage of cyber security professionals remains an unsolved problem. We need to greatly expand the community of faculty teaching in cybersecurity in order to meet the increasing demand for security professionals. The number of security-focused competitions and exercises has increased in recent years. As faculty we need to integrate them into our courses. Having a more unified environment and contributing to each other's educational projects will help. By bringing together new and experienced faculty we can build a community to engage in this work. We need to develop a critical mass of faculty, asking each other what we want from this learning community. As a learning community we can provide support for new members to learn about resources and help each other. These resources include exercises, webinars, and slides. We will share experiences, practices and ongoing efforts. The BoF also benefits experienced members, helping them to disseminate their work and reach other faculty with similar interests. As a community we have begun to discuss what works and what problems students and instructors have. The questions we will ask are, "What exercises have you tried? What are your experiences? What are you looking for from the community?"

Hands-on exercises are often used to improve student engagement and knowledge retention in systems, networking and cybersecurity classes. Even when students comprehend the concepts, they may lack the skills to complete an exercise. Teachers need effective tools to identify these problemsduring an assignment and offer targeted and timely help.

We need to greatly expand the number of students studying cybersecurity in order to meet the increasing demand for security professionals. As a community, we can use our expertise and alliances with other fields to create interdisciplinary courses and modules to attract diverse students into cybersecurity. For example, Liberal Arts Colleges have a strong core general education curriculum that can be enhanced with cybersecurity concepts. On one hand, students from non-CS majors are important for the cybersecurity workforce as they will be shaping the policies and business processes that affect cybersecurity decisions. On the other hand, well rounded CS students that take interdisciplinary security courses and CS1 will be able to apply the concepts and solve today's complex security problems. This BoF will explore ideas for integrating the technical content of cybersecurity with political science, business, law, psychology, philosophy, international affairs, and others. Participants from colleges, universities and K-12 will be able to co-create plans for these interdisciplinary experiences.

Engaging students in practical, hands-on exercises on testbeds improves student learning and knowledge retention. However, testbeds may also present an obstacle to learning for students who are not familiar with the environment, or who lack the necessary background to complete their assignments. Our research investigates how students learn with testbeds. We instrument a default operating system on the DeterLab testbed and monitor the students' command line input and output, as they perform homework assignments.We use this data to evaluate students' progress, to detect when a student is struggling and to identify common problems.

The critical shortage of cyber security professionals has driven faculty interest in adding this to the curriculum, and it was added to the ACM/ IEEE Model Curriculum of 2013. This is a subject that demands hands-on exercises. There has been a modest increase in the number of such exercises, but the limit is usability. Most faculty do not have the time to create their own exercises, modify and install VMs, and set up assessment mechanisms. EDURange is a framework for accessing, developing and assessing interactive cybersecurity exercises. It has a range of exercises from introductory to advanced. We will demo an introductory exercise about using the command line and an advanced exercise about network scanning. We want to reach and engage as many faculty as possible, so that they can develop their own exercises. EDURange uses VMs in the cloud. Students only need an ssh-client. We have built tools to give faculty detailed information on how students are doing. This allows instructors to more easily see when students are stuck or heading in the wrong direction. The exercises we have created have manuals that instructors can use. Information about EDURange can be found at https://edurange.org.

The critical shortage of cyber security professionals remains an unsolved problem. We need to greatly expand the community in order to meet the increasing demand. The number of security-focused competitions and exercises has increased in recent years. As faculty we need to choose those that we can best integrate into our courses. As a community, we can think about how to create a more unified environment to contribute to each other's projects. We would like to bring together new and experienced faculty to build community and engage in this work. We have set up a dedicated Facebook to allow us to work together outside of SIGCSE events. During the BoF, we will encourage participants to sign up for the closed group, and we will ask the questions, 'What do you want from this learning community? What exercises have you heard about or tried?" One benefit of having a learning community is that it will be easier to provide support for new members to learn about resources and get help. These resources include exercises, webinars, and slides. We will share experiences, practices and ongoing efforts (NSA's National Cybersecurity Curriculum Program), including our own (e.g. Security Injections, Security Knitting Kit, EDURange and UWGB GenCyber). The BoF also benefits experienced members, helping them to disseminate their work and reach other faculty with similar interests. We will discuss what works and what problems students and instructors have encountered. We will discuss ways to integrate security-related exercises into existing courses.

Abstract: Data science is on the rise, being a high-demand skill for a number of employers, both in the tech field and in various scientific disciplines. Since it is a new field, only a handful of schools offer a data science program, most of those being large research institutions. This presents a unique opportunity for smaller schools to combine an exciting and highly relevant topic with the personalized education, small class sizes, and individual attention they are known for. On the flip side, building out data science at small schools also presents numerous challenges, including finding sufficient faculty time and expertise, attracting a sufficient number of students, forging interdepartmental connections, and convincing the administration to approve the new courses and program. In this poster, we present lessons learned from data science efforts at three small institutions, including one which recently successfully developed a data science certificate (containing four new data science courses).

One of the ways to attract a more diverse group of students to computer science is by offering courses that are interdisciplinary. Some of the first examples of this combined multimedia with programming. There are many more possibilities, but as computer scientists, we often do not have the domain knowledge to teach these courses alone. Team teaching offers a solution to this dilemma. The goal of this panel is to present some of the problems we have encountered and to discuss some of the solutions.

We need to greatly expand the community of faculty teaching cybersecurity using hands-on exercises. The number of security-focused competitions and exercises has increased in recent years so that faculty need to choose those that can best be integrated into their courses. As a community, we can contribute to each other/s projects. We would like to bring together both new and experienced faculty to build community. Our Facebook group (https://www.facebook.com/groups/TeachingCyber/) allows us to work together outside of SIGCSE events, and we are developing a critical mass of users. In the BoF, participants can sign up for the closed group. Participants will discuss answers to specific questions on teaching security. One goal of our BoF is to provide support for new members to learn about resources and get help. These resources include exercises, webinars, and slides. We will share experiences, practices and ongoing efforts, including our own (e.g. Security Injections, the Security Knitting Kit project, and EDURange). The BoF also benefits experienced members, helping them to disseminate their work and reach other faculty with similar interests. As a community we have begun to share exercises and discuss what works and what problems students and instructors have encountered. We will discuss ways to integrate security-related exercises into existing courses. The questions we will ask are, What materials and exercises have you tried? What are your experiences? How do we expand and improve cybersecurity education?

Cybersecurity is a topic of growing interest for CS educators. The goal of this workshop is to empower faculty to add hands-on security exercises to their courses. We introduce EDURange, a framework for accessing, developing and assessing interactive cybersecurity exercises. We want to reach and engage all students. The first step is to have interesting challenges that are easy to access. EDURange uses VMs in a public cloud, which also provides flexible resources. No software needs to be installed - students only need an ssh client. Another step is to give students feedback on how they are doing. This is an important role for faculty and is not something to automate. Instead, EDURange provides basic tools to visualize what students are doing as they work on the exercises. This allows instructors to more easily see when students are stuck or heading in the wrong direction. Since cybersecurity exercises are often easy to understand but hard to solve, guidance is especially important so that students don't become frustrated. In this workshop, participants will get to try EDURange and several exercises, including an introduction to ssh. Participants don't need to be security experts. We will provide sample syllabuses for an introductory security course as well as an interdisciplinary security course, and we will show how our exercises can be integrated into these courses. More information at http://www.edurange.org. Laptop required.

No abstract available.

We have been teaching cybersecurity using hands-on, interactive exercises that have successfully engaged students. However, in order to meet the increasing demand for security professionals, we need to greatly expand the community of faculty teaching in this area. We would like to bring together new and experienced faculty to do this. Ambareen Siraj has set up a dedicated Facebook group to allow us to work together outside of SIGCSE events, but we need to develop a critical mass of users. During the BoF, we will encourage participants to sign up for the closed group, and we will have an exercise where they ask and answer questions about what they want from this learning community. One benefit of having a learning community is that it will be easier to provide support for new members to learn about resources and get help. These resources include exercises, webinars, and slides. We will share experiences, practices and ongoing efforts, including our own (e.g. Security Injections, the Security Knitting Kit project, and EDURange). The BoF also benefits experienced members, helping them to reach other faculty with similar interests. As a community we have begun to share exercises and discuss what works and what problems students and instructors have encountered. For the last five years, we have been doing that at SIGCSE. We will discuss ways to integrate security-related exercises into existing courses. The questions we will ask are, "What exercises have you tried? What are your experiences? What are you looking for from the community?"

We see teaching cybersecurity through hands-on, interactive exercises as a way to engage students. However, we also want to assess how much students are learning from these exercises, and the exercises themselves could be used to assess what students know. Creating new hands-on exercises requires significant preparation on the part of the instructor. As a community we have begun to share exercises and discuss what works and what problems students and instructors have encountered. The purpose of this BOF is two-fold: 1) to continue to bring together instructors who have developed hands-on exercises with those who would like to use them, and 2) extend the discussion to include assessment of student learning. We recognize that few CS programs can afford new required courses, so we will discuss ways to integrate security-related exercises into existing ones. This could include networking, OS, computer architecture, programming languages, software engineering, algorithms and programming (CS0, CS1, CS2). The questions we will ask are, "What exercises have you tried? What are your experiences? What are you looking for? What are the learning goals for your students? How do you assess them?"

Assessing student performance in cybersecurity labs and exercises is a difficult and time-consuming task. Simply recording the number of correct answers is inferior to in-depth assessment. Faculty are often best placed to offer expert feedback, advice, and guidance based on assessing student achievement and quality of performance for time-constrained exercises. Since this often takes place in the context of large classes and complex exercises, it can present obstacles to offering qualitative feedback. Yet, in some cases there is more information available that could simplify this task. This paper explores the use of command line history and visualization to add additional information and simplify the problem.

Writing secure code requires a programmer to think both as a defender and an attacker. One can draw a parallel between this model of thinking and techniques used in test-driven development, where students learn by thinking about how to effectively test their code and anticipate possible bugs. In this study, we analyzed the quality of both attack and defense code that students wrote for an assignment given in an introductory security class of 75 (both graduate and senior undergraduate levels) at NYU. We made several observations regarding students' behaviors and the quality of both their defensive and offensive code. We saw that student defensive programs (i.e., assignments) are highly unique and that their attack programs (i.e., test cases) are also relatively unique. In addition, we examined how student behaviors in writing defense programs correlated with their attack program's effectiveness. We found evidence that students who learn to write good defensive programs can write effective attack programs, but the converse is not true. While further exploration of causality is needed, our results indicate that a greater pedagogical emphasis on defensive security may benefit students more than one that emphasizes offense.

This paper reports on the experience of using the EDURange framework, a cloud-based resource for hosting on-demand interactive cybersecurity scenarios. Our framework is designed especially for the needs of teaching faculty. The scenarios we have implemented each are designed specifically to nurture the development of analysis skills in students as a complement to both theoretical security concepts and specific software tools.

We see teaching cybersecurity through hands-on, interactive exercises as a way to engage students. Some of the exercises that we have seen require significant preparation on the part of the instructor. Having a community makes it easier to share exercises, knowing what works and what problems students and instructors have encountered. The purpose of this BOF is to bring together instructors who have developed hands-on exercises, those who have used them and those who would like to. We recognize that few CS programs can afford new required courses, so we will discuss ways to integrate security-related exercises into existing ones. This could include networking, OS, computer architecture, programming languages, software engineering, algorithms and programming. The questions we will ask are, "What exercises have you tried? What are your experiences? What are you looking for?"

One of the central skills in computer security is reasoning about how programs fail. As a result, computer security necessarily involves thinking about the corner cases that arise when software executes. An unfortunate side effect of this is that computer security assignments typically necessitate deep understanding of a topic, such as how the stack is laid out in memory or how web applications interact with databases. This work presents a series of assignments that require very little background knowledge from students, yet provide them with the ability to reason about failures in programs. In this set of assignments, students implement two very simple programs in a high-level language (Python). Students first implement a reference monitor that tries to uphold a security property within a sandbox. For the second portion, the students are provided each others' reference monitors and then write attack code to try to bypass the reference monitors. By leveraging a Python-based sandbox, student code is isolated cleanly, which simplifies development and grading. These assignments have been used in about a dozen classes in a range of environments, including a research university, online classes, and a four year liberal arts school. Student and instructor feedback has been overwhelmingly positive. Furthermore, survey results demonstrate that after a 2-3 week module, 76% of the students who did not understand reference monitors and access control learned these key security concepts.

We see teaching information security through hands-on, interactive exercises as a way to engage students. Some of the exercises that we have tried require significant preparation on the part of the instructor. Having a community makes it easier to share exercises, knowing what works and what problems students and instructors have encountered. The purpose of this BOF is to bring together instructors who have used hands-on exercises and those who would like to. We recognize that few CS programs can afford new required courses, so we would be discussing ways to integrate security-related exercises into existing ones. This could include networking, OS, computer architecture, programming languages, software engineering and algorithms. The questions we will ask are, "What exercises, if any, have you tried" What are your experiences? What are you looking for?

Cybersecurity is a topic of growing interest for CS educators. The goal of this workshop is to provide faculty with tools and interactive exercises that would facilitate adding this topic to their curriculum. We will introduce the EDURange framework for developing interactive cybersecurity exercises. We have several exercises for participants to try, including network scanning and firewall configuration. We will give an introduction to some tools such as nmap, as well as the basic concepts. We have tested our framework several times in classrooms and workshops for students and faculty. One of its advantages is that faculty can use it in their classes with very little set-up. It uses Amazon's EC2/AWS to make the exercises readily available, so instructors will be able to access them with an account. We will also discuss other security exercises, including ones in the RAVE. It also provides each user with a number of VMs with tools and vulnerabilities. We will discuss using exercises as assessment tools. No previous experience in security is necessary. Laptop required. http://blogs.evergreen.edu/edurange

We see teaching cybersecurity through hands-on, interactive exercises as a way to engage students. Some of the exercises that we have seen require significant preparation on the part of the instructor. Having a community makes it easier to share exercises, knowing what works and what problems students and instructors have encountered. The purpose of this BOF is to bring together instructors who have used hands-on exercises and those who would like to. We recognize that few CS programs can afford new required courses, so we would be discussing ways to integrate security-related exercises into existing ones. This could include networking, OS, computer architecture, programming languages, software engineering and algorithms.

This workshop is intended for anyone who would like to use hands-on exercises in cybersecurity for a variety of classes including Networking, OS, Computer Security and Software Engineering. It has received increased attention nationally in the proposed ACM/IEEE CS2013 Curricula Guidelines. The goal of this workshop is to provide faculty with varied backgrounds (including none) in this area with some tools and interactive exercises to facilitate incorporating this knowledge area into their curriculum. We believe that it is important that students develop analytical skills, thus we include an introduction to EDURange1 and the Hacker Curriculum principles: (1) understanding failure modes and (2) seeing across or through layers of abstraction according to a particular scenario. We will guide attendees through exercises tailored to their level and interests. The exercises will include port scanning and setting up a firewall. These can be done at a range of levels from beginner to advanced. We will include an introduction to tools for packet inspection, which is an important part of analysis. Attendees will receive accounts on the RAVE2 with exercises that they can take back and use immediately with their classes. RAVE provides a number of VMs pre-configured for the exercises. RAVE is also a general purpose technology for other hands-on exercises. Laptop required.

Incorporating information security into the undergraduate curriculum continues to be a topic of interest to SIGCSE attendees. The purpose of this BOF is to help sustain the existing community of educators and researchers interested in bringing ethical hacking skills and an understanding of security into the classroom and relating these topics to the foundations of Computer Science. We would like to bring our colleagues together to share pedagogical practices, stories of hacking and how to use them to inspire our students and communicate complex concepts in computer science and security. We also plan to discuss our own experiences, practices and ongoing efforts (e.g., our infosec teaching experiences, the SISMAT program, EDURange and the dissemination of infosec interactive exercises). SISMAT is one of many alternatives for teaching cybersecurity to undergraduates. It is based on the "Hacker Curriculum" and includes a two-week summer intensive program, a research component and an internship. A description can be viewed at http://www.ists.dartmouth.edu/events/sismat/. An education in computer science can be an important resource in nurturing future leaders in cybersecurity, but only if we enable a community of educators willing to share approaches. This was highlighted by Sally Finch's keynote speech in 2010. At our BOF last year, several people had stories to share, and we plan to focus on that.

Major sources of transient errors in microprocessors today include noise and single event upsets. As feature sizes and voltages are reduced to create faster, more efficient, and computationally more powerful processors, these errors will increase significantly. We show that (contrary to conventional wisdom) error correction codes (ECC) can be efficiently utilized to handle these errors as instructions are being processed through the microprocessor pipeline. We will analyze some of the tradeoffs involved in a hardware implementation of ECC for the instruction queue with respect to performance, power, area, and reliability. Specifically, for an environment with high error rates, we show that we can correct all single bit errors with a negligible drop in performance. Our approach can be generalized to other data structures within the microprocessor, including the register file and reorder buffer.

As the performance gap between processors and memory systems increases, the CPU spends more time stalled waiting for data from main memory. Critical long latency instructions, such as loads that miss to main memory and floating point arithmetic operations, are primarily responsible for these stalls. We present a technique, Fetch Halting that suspends instruction fetching when the processor is stalled by a critical long latency instruction. This enables us to save power in one of the primary sources of power dissipation, the issue logic. By reducing the occupancy rates in the issue queue and reorder buffer, we save power by disabling a large number of unused queue entries. In order to characterize critical instructions, our approach combines software profiling and hardware monitoring techniques. Statistical profiling information obtained from sample runs is used to identify critical instructions while hardware cache-miss prediction is used to monitor these instructions. We show that, on average, Fetch Halting can reduce issue queue and reorder buffer occupancy rates by 17.2% and 23.4%, respectively, with an average performance loss of only 4.6%.

Data prefetching is an e ective approach to addressing the memory latency problem. While a few processors have implemented hardware-based data prefetching, the majority of modern processors support data-prefetch instructions and rely on compilers to automatically insert prefetches. However, most prefetching schemes in commercial compilers suffer from two limitations: (1) the source code must be available before prefetching can be applied, and (2) these prefetching schemes target only loops with statically-known strided accesses. In this study, we broaden the scope of softwarecontrolled prefetching by addressing the above two limitations. We use pro ling to discover strided accesses that frequently occur during program execution but are not determinable by the compiler. We then use the strides discovered to insert prefetches into the executable directly, without the need for re-compilation. Performance evaluation was done on an Alpha 21264-based system with a 64KB data cache and an 8MB secondary cache. We nd that even with such large caches, our technique o ers speedups ranging from 3% to 56 % in 11 out of the 26 SPEC2000 benchmarks. Our technique has been incorporated into Pixie and Spike, two products in Compaq's Tru64 Unix.

In this paper we describe the effect of errors in the intrinsic camera parameters on reconstructed panoramic images. A panoramic image is created by first capturing a sequence of images while rotating the camera about a vertical axis a full 360/spl deg/. The subsequent steps are projecting the original rectilinear images onto cylindrical surfaces and compositing them to form the panoramic image. Our analysis has led to a technique that allows simultaneous recovery of the camera focal length and properly composited panoramic images. The correct focal length can be determined by iterating the processes of projecting the original rectilinear images onto cylindrical surfaces given an estimate of the focal length and compositing the resulting images to yield an increasingly better estimate of the focal length. This paper shows that the convergence towards the correct focal length is exponential.

Three different algorithms for mobile robot vision-based obstacle detection are presented in this paper each based on different assumptions The first two algorithms are qualitative in that they return only yes/no answers regarding the presence of obstacles in the field of view; no 3D reconstruction is performed. They have the advantage of fast determination of the existence of obstacles in a scene based on the solvability of a linear system. The first algorithm uses information about the ground plane, while the second only assumes that the ground is planar. The third algorithm is quantitative in that it continuously estimates the ground plane and reconstructs partial 3D structures by determining the height above the ground plane of each point in the scene. Experimental results are presented for real and simulated data, and the performance of the three algorithms under different noise levels is compared in simulation. We conclude that in terms of the robustness of performance, the third algorithm is superior to the other two.

Abstract Even though the problem of computing Steiner minimal trees (SMTs) in the plane is well known to be NP‐hard, There exist heuristic algorithms which run in polynomial time. Recent research results have shown that the problem in three dimensions is demonstrably more difficult. This paper approaches the development of a heuristic for the problem utilizing the Delaunay triangulation in 3‐space to compute suboptimal SMTs. Computational results are also provided.

Three different algorithms for qualitative obstacle detection are presented in this paper. Each one is based on different assumptions. The first two algorithms are aimed at yes/no obstacle detection without indicating which points are obstacles. They have the advantage of fast determination of the existence of obstacles in a scene based on the solvability of a linear system. The first algorithm uses information about the ground plane, while the second algorithm only assumes that the ground is planar. The third algorithm continuously estimates the ground plane, and based on that determines the height of each matched point in the scene. Experimental results are presented for real and simulated data, and performances of the three algorithms under different noise levels are compared in simulation. We conclude that in terms of the robustness of performance, the third one works best.>

Recovering the shape of an object from triangulation fails at occluding contours of smooth objects because the contour generators are view dependent. For three or more views, shape recovery is possible, and several algorithms have been developed for this purpose. The authors' approach uses a linear smoother to optimally combine all of the measurements available at the contours (and other edges) in all of the images. This allows extraction of a robust and dense estimate of surface shape, and shape information from both surface markings and occluding contours.>

The variation, with respect to view, of 2D features defined for projections of 3D point sets and line segments is studied. It is established that general-case view-invariants do not exist for any number of points, given true perspective, weak perspective, or orthographic projection models. Feature variation under the weak perspective approximation is then addressed. Though there are no general-case weak-perspective invariants, there are special-case invariants of practical importance. Those cited in the literature are derived from linear dependence relations and the invariance of this type of relation to linear transformation. The variation with respect to view is studied for an important set of 2D line segment features: the relative orientation, size, and position of one line segment with respect to another. The analysis includes an evaluation criterion for feature utility in terms of view-variation. This relationship is a function of both the feature and the particular configuration of 3D line segments. The use of this information in objection recognition is demonstrated for difficult discrimination tasks.>

A target location is represented by compressing a 360 degrees image from a spherical mirror into a circular waveform. Navigation tasks are specified as a sequence of homing tasks on target locations. This is accomplished by matching landmarks from the current view with the next target view. A method is provided for matching using qualitative geometric features of each of the waveforms. In addition, a geometric analysis allows one to do 3-D reasoning about the environment, which is sufficient for computing the rotation and translation between the current location and the target location. It eventually may allow acquisition of a 3-D model of the environment.>

Multifingered manipulators are mechanically redundant and so may address a broad array of object geometries, more general force domain tasks, and nested grasp objectives. Many complex robot systems, such as multifingered manipulators, legged platforms, and teams of kinematically independent robots, can benefit from techniques which determine how to use environmental geometry to advantage in interaction tasks. A multiple-resolution object representation in the force domain which facilitates grasp synthesis is presented. A means for describing grasp objectives and a control strategy for designing the geometry of contact mediated by force domain goals are presented.>

A system that allows a robot to acquire a model of its environment and to use this model to navigate is described. The system maps the environment as a set of snapshots of the world taken at target locations. The robot uses an image-based local homing algorithm to navigate between neighboring target locations. Features of the approach include an imaging system that acquires a compact, 360 degrees representation of the environment and an image-based, qualitative homing algorithm that allows the robot to navigate without explicitly inferring three-dimensional structure from the image. The results of an experiment in a typical indoor environment are described, and its argued that image-based navigation is a feasible alternative to approaches using three-dimensional models.>

An examination is made of vanishing point calculation as a statistical estimation problem. It is assumed that image line segments have been previously clustered into groups of convergent lines. For each group, the vanishing point location is estimated as the polar axis of an equatorial distribution on the unit sphere, and the statistical error of the estimate is determined. The sensitivity of the estimates to the number of lines in a convergent cluster is studied.>

A local search optimization procedure, subset-convergent local search, is developed for identifying known 2D models in broken and skewed line data. Due to fragmentation in the data, the mapping between model and data line segments may be one-to-many. The proposed algorithm starts with a model-data mapping selected at random and refines it until it is locally optimal. Results are presented in which the algorithm frequency converges upon the globally optimal match. This best match can be found with greater confidence by independently running the algorithm several times. The match error being minimized has two basic components, a spatial fit error and an omission error. Spatial fit is a function of the integrated squared perpendicular distance between model lines and data line segments. This measure is minimized, subject to rotation, translation, and scaling, for every set of correspondences tested. The closed-form equation solving for this optimal transform is second order. Although the matching system itself is 2D, results are presented in which the matcher, in conjunction with a 3D robot navigation system, correctly updates estimates of robot position.>

A method for planning smooth robot paths is presented. The method relies on the use of Laplace's equation to constrain the generation of a potential function over regions of the configuration space of an effector. Once the function is computed, paths may be found very quickly. These functions do not exhibit the local minima which plague the potential field method. Unlike decompositional and algebraic techniques. Laplace's equation is very well suited to computation on massively parallel architectures.>

Given two parallel lines in a plane, their images formed by central projection onto the viewing plane intersect at a vanishing point, which provides a constraint on the orientation of the plane. Two such independent constraints define a vanishing line and thereby determine the orientation of the plane uniquely. In order to effectively use this information, an estimate of its accuracy must be incorporated. In the approach suggested, line directions and surface normal vectors are represented as points on a Gaussian sphere, and the error bounds are computed as regions on the sphere. Information from multiple vanishing points and surfaces is combined by intersecting the corresponding regions. In addition, constraints based on real-world knowledge can be introduced to improve the accuracy. Some experimental results on natural images re presented to illustrate this method.>

The authors present a computational approach to the extraction of straight lines based on the principles of perceptual organization. In particular, they consider how local information that is spatially distributed can be organized into a large-scale geometric structure in a computationally efficient manner. Symbolic tokens representing line segments and relations which are primarily geometric in nature and used to control a hierarchical grouping process. The relational measures on pairs of lines are based on collinearity, proximity, and similarity in contrast. The algorithm is implemented within a local, parallel, hierarchical framework for symbolic grouping that involves a cycle of linking, optimization, and replacement steps. Experimental results on a variety of natural scene images demonstrate effectiveness of the filtering and optimization stages in the extraction of straight lines. Issues in the development of a more general framework for symbolic grouping are also discussed.>

The paper is concerned with the use of symmetry groups as descriptors for surfaces-such symmetry groups can be used to characterise the constraints imposed between two bodies by areal contact between their surfaces. A method is presented for combining constraints imposed by spatial relationships between features (infinite geometric entities) of bodies. It consists of an algorithm for computing the intersections of infinite symmetry groups from their characteristic invariants. It is demonstrated that invariants of symmetry groups for these combined features can be computed in an efficient and practical way.>