| 1. |
- Källhammer, Jan-Erik, et al.
(författare)
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Near Zone Pedestrian Detection using a Low-Resolution FIR Sensor
- 2007
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Ingår i: Intelligent Vehicles Symposium, 2007 IEEE. - Istanbul, Turkey : IEEE. - 1424410673
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Konferensbidrag (refereegranskat)abstract
- This paper explores the possibility to use a single low-resolution FIR camera for detection of pedestrians in the near zone in front of a vehicle. A low resolution sensor reduces the cost of the system, as well as the amount of data that needs to be processed in each frame.We present a system that makes use of hot-spots and image positions of a near constant bearing to detect potential pedestrians. These detections provide seeds for an energy minimization algorithm that fits a pedestrian model to the detection. Since false alarms are hard to tolerate, the pedestrian model is then tracked, and the distance-to-collision (DTC) is measured by integrating size change measurements at sub-pixel accuracy, and the car velocity. The system should only engage braking for detections on a collision course, with a reliably measured DTC.Preliminary experiments on a number of recorded near collision sequences indicate that our method may be useful for ranges up to about 10m using an 80x60 sensor, and somewhat more using a 160x120 sensor. We also analyze the robustness of the evaluated algorithm with respect to dead pixels, a potential problem for low-resolution sensors.
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| 2. |
- Barberi, Emmanuele, et al.
(författare)
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A differential entropy-based method for reverse engineering quality assessment
- 2023
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The present work proposes the use of point clouds differential entropy as a method for reverse engineering quality assessment. This quality assessment can be used to measure the deviation of objects made with additive manufacturing or CNC techniques. The quality of the execution is intended as a measure of the deviation of the geometry of the obtained object compared to the original CAD. This paper proposes the use of the quality index of the CorAl method to assess the quality of an objects compared to its original CAD. This index, based on the differential entropy, takes on a value the closer to 0 the more they obtained object is close to the original geometry. The advantage of this method is to have a global synthetic index. It is however possible to have entropy maps of the individual points to verify which are the areas with the greatest deviation. The method is robust for comparing point clouds at different densities. Objects obtained by additive manufacturing with different print qualities were used. The quality index evaluated for each object, as defined in the CorAl method, turns out to be gradually closer to 0 as the quality of the piece's construction increases.
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| 3. |
- Brissman, Emil, 1987-, et al.
(författare)
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Camera Calibration Without Camera Access - A Robust Validation Technique for Extended PnP Methods
- 2023
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Konferensbidrag (refereegranskat)abstract
- A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models’ residual variances—however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth.
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| 4. |
- Eilertsen, Gabriel, 1984-, et al.
(författare)
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BriefMatch: Dense binary feature matching for real-time optical flow estimation
- 2017
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Ingår i: Proceedings of the Scandinavian Conference on Image Analysis (SCIA17). - Cham : Springer. - 9783319591254 ; , s. 221-233
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Konferensbidrag (refereegranskat)abstract
- Research in optical flow estimation has to a large extent focused on achieving the best possible quality with no regards to running time. Nevertheless, in a number of important applications the speed is crucial. To address this problem we present BriefMatch, a real-time optical flow method that is suitable for live applications. The method combines binary features with the search strategy from PatchMatch in order to efficiently find a dense correspondence field between images. We show that the BRIEF descriptor provides better candidates (less outlier-prone) in shorter time, when compared to direct pixel comparisons and the Census transform. This allows us to achieve high quality results from a simple filtering of the initially matched candidates. Currently, BriefMatch has the fastest running time on the Middlebury benchmark, while placing highest of all the methods that run in shorter than 0.5 seconds.
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| 5. |
- Felsberg, Michael, 1974-, et al.
(författare)
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A COSPAL Subsystem : Solving a Shape-Sorter Puzzle
- 2005
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Ingår i: AAAI Fall Symposium: From Reactive to Anticipatory Cognitive Embedded Systems, FS-05-05. - : AAAI Press. ; , s. 65-69
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Konferensbidrag (refereegranskat)abstract
- To program a robot to solve a simple shape-sorter puzzle is trivial. To devise a Cognitive System Architecture, which allows the system to find out by itself how to go about a solution, is less than trivial. The development of such an architecture is one of the aims of the COSPAL project, leading to new techniques in vision based Artificial Cognitive Systems, which allow the development of robust systems for real dynamic environments. The systems developed under the project itself remain however in simplified scenarios, likewise the shape-sorter problem described in the present paper. The key property of the described system is its robustness. Since we apply association strategies of local features, the system behaves robustly under a wide range of distortions, as occlusion, colour and intensity changes. The segmentation step which is applied in many systems known from literature is replaced with local associations and view-based hypothesis validation. The hypotheses used in our system are based on the anticipated state of the visual percepts. This state replaces explicit modeling of shapes. The current state is chosen by a voting system and verified against the true visual percepts. The anticipated state is obtained from the association to the manipulator actions, where reinforcement learning replaces the explicit calculation of actions. These three differences to classical schemes allow the design of a much more generic and flexible system with a high level of robustness. On the technical side, the channel representation of information and associative learning in terms of the channel learning architecture are essential ingredients for the system. It is the properties of locality, smoothness, and non-negativity which make these techniques suitable for this kind of application. The paper gives brief descriptions of how different system parts have been implemented and show some examples from our tests.
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| 6. |
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| 7. |
- Forssén, Per-Erik, 1973-, et al.
(författare)
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Autonomous Learning of Object Appearances using Colour Contour Frames
- 2006
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Ingår i: 3rd Canadian Conference on Computer and Robot Vision, CRV06, Québec City, Québec, Canada. - Québec, Canada : IEEE Computer Society. - 0769525423 ; , s. 3-3
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Konferensbidrag (refereegranskat)abstract
- In this paper we make use of the idea that a robot can autonomously discover objects and learn their appearances by poking and prodding at interesting parts of a scene. In order to make the resultant object recognition ability more robust, and discriminative, we replace earlier used colour histogram features with an invariant texture-patch method. The texture patches are extracted in a similarity invariant frame which is constructed from short colour contour segments. We demonstrate the robustness of our invariant frames with a repeatability test under general homography transformations of a planar scene. Through the repeatability test, we find that defining the frame using using ellipse segments instead of lines where this is appropriate improves repeatability. We also apply the developed features to autonomous learning of object appearances, and show how the learned objects can be recognised under out-of-plane rotation and scale changes.
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| 8. |
- Forssén, Per-Erik, 1973-, et al.
(författare)
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Blobs in Epipolar Geometry
- 2004
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Ingår i: Blobs in Epipolar Geometry,2004. ; , s. 82-85
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Epipolar geometry is the geometry situation of two cameras depicting the same scene. For un-calibrated cameras epipolar geometry is compactly described by the fundamental matrix. Estimation of the fundamental matrix is trivial if we have a set of corresponding points in the two images. Corresponding points are often found using e.g. the Harris interest point detector, but there are several advantages with using richer features instead. In this paper we will use blob features. Blobs are homogeneous regions which are compactly described by their colour, area, centroid and inertia matrix. Using blobs to establish correspondences is fast, and the extra information besides position, allows us to reject false matches more accurately.
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| 9. |
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| 10. |
- Forssén, Per-Erik, 1973-, et al.
(författare)
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Informed Visual Search : Combining Attention and Object Recognition
- 2008
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation<em></em>. - Pasadena : IEEE Robotics and Automation Society. - 9781424416462 ; , s. 935-942
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Konferensbidrag (refereegranskat)abstract
- This paper studies the sequential object recognition problem faced by a mobile robot searching for specific objects within a cluttered environment. In contrast to current state-of-the-art object recognition solutions which are evaluated on databases of static images, the system described in this paper employs an active strategy based on identifying potential objects using an attention mechanism and planning to obtain images of these objects from numerous viewpoints. We demonstrate the use of a bag-of-features technique for ranking potential objects, and show that this measure outperforms geometric matching for invariance across viewpoints. Our system implements informed visual search by prioritising map locations and re-examining promising locations first. Experimental results demonstrate that our system is a highly competent object recognition system that is capable of locating numerous challenging objects amongst distractors.
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