Adaptation of a clustered lumpy background model for task‐based image quality assessment in x‐ray phase‐contrast mammography
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Since the introduction of clinical x-ray phase-contrast mammography (PCM), a technique that exploits refractive-index variations to create edge enhancement at tissue boundaries, a number of optimization studies employing physical image-quality metrics have been performed. Ideally, task-based assessment of PCM would have been conducted with human readers. These studies have been limited, however, in part due to the large parameter-space of PCM system configurations and the difficulty of employing expert readers for large-scale studies. It has been proposed that numerical observers can be used to approximate the statistical performance of human readers, thus enabling the study of task-based performance over a large parameter-space.Methods are presented for task-based image quality assessment of PCM images with a numerical observer, the most significant of which is an adapted lumpy background from the conventional mammography literature that accounts for the unique wavefield propagation physics of PCM image formation and will be used with a numerical observer to assess image quality. These methods are demonstrated by performing a PCM task-based image quality study using a numerical observer. This study employs a signal-known-exactly, background-known-statistically Bayesian ideal observer method to assess the detectability of a calcification object in PCM images when the anode spot size and calcification diameter are varied.The first realistic model for the structured background in PCM images has been introduced. A numerical study demonstrating the use of this background model has compared PCM and conventional mammography detection of calcification objects. The study data confirm the strong PCM calcification detectability dependence on anode spot size. These data can be used to balance the trade-off between enhanced image quality and the potential for motion artifacts that comes with use of a reduced spot size and increased exposure time.A method has been presented for the incorporation of structured breast background data into task-based numerical observer assessment of PCM images. The method adapts conventional background simulation techniques to the wavefield propagation physics necessary for PCM imaging. This method is demonstrated with a simple detection task.Keywords:
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In robotics object tracking is needed to steer towards objects, check if grasping is successful, or investigate objects more closely by poking or handling them. While many 3D object tracking approaches have been proposed in the past, real world settings pose challenges such as automatically detecting tracking failure, real-time processing, and robustness to occlusion, illumination, and view point changes. This paper presents a 3D tracking system that is capable of overcoming these difficulties using a monocular camera. We present a method of Tracking-State-Detection (TSD) that takes advantage of commercial graphics processors to map textures onto object geometry, to learn textures online, and to recover object pose in real-time. Our system is able to handle 6 DOF object motion during changing lighting conditions, partial occlusion and motion blur while maintaining an accuracy of a few millimetres. Furthermore using TSD we are able to automatically detect occlusions or whether we lost track, and can then trigger a SIFT-based recognition system that is trained during tracking to recover the pose. Evaluations are presented in relation to ground truth pose data and examples present TSD on real-world scenes presented in video sequences.
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Abstract In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calculated and later used for position based visual servoing. Experiments were carried out to verify the proposed approach for object recognition. The stereo camera was also tested to see whether the depth accuracy is adequate. The proposed object recognition method is invariant to scale, orientation and lighting condition which increases the level of robustness. The accuracy of stereo vision camera can reach 1 mm. The accuracy is adequate for tasks such as grasping and manipulation.
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The stripe laser based stereo vision is often used in robot vision-guided system in the eye-in-hand configuration. The 3D scene is reconstructed from many 3D stripes obtained in stripe laser based stereo vision. But 3D objects can not be recognized by 3D stripe information. In 3D cluttered scene, the recognition of 3D objects is also difficult due to the object pose and match. In fact, the video from camera of stripe laser based stereo vision can be benefit to recognize 3D objects. This paper proposes an approach of the object-oriented vision-guided robot that video segmentation, tracking and recognition are used to guide robot to reduce the complexity of 3D object detection, recognition and pose estimation. Experimental results demonstrate the effectiveness of the approach.
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Procedures for three-dimensional image reconstruction that are based on the optical and neural apparatus of human stereoscopic vision have to be designed to work in conjunction with it. The principal methods of implementing stereo displays are described. Properties of the human visual system are outlined as they relate to depth discrimination capabilities and achieving optimal performance in stereo tasks. The concept of depth rendition is introduced to define the change in the parameters of three-dimensional configurations for cases in which the physical disposition of the stereo camera with respect to the viewed object differs from that of the observer's eyes.
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We present a 3D tracking method of an object which is moving in a complicated scene by an active stereo vision system. The system uses binocular vision robot, which can simulate the human eye movements. Gaze holding on an target object with the controlled cameras keeps the target's stereo disparity small, and simplifies the visual processing to locate the target for pursuit control. The novel feature of our tracking method is the disparity-based segmentation method of the target object. The method utilizes zero disparity filter and correlation to separate the target object with small disparity from distracting background. Furthermore, using correlation method to estimate stereo disparity makes it possible to fixate on a surface of the target object. We show the experimental results with the complicated scene to demonstrate the effectiveness of the proposed method.< >
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A novel object tracking method based on RGB-D camera is proposed to handle fast appearance change, occlusion, background clutter which may arise for vision-based robot navigation. It makes use of appearance and depth information that are complementary to each other in visual perception to get robust tracking. First, RGB image and depth information are captured by the RGB-D camera. Then, an online updating appearance model is created with features extracted from RGB image. A motion model is created on plan-view map that is drawn from depth information and camera parameters. The estimation of object position and scale is performed on the motion model. Finally, appearance features are combined with position and scale information to track the target. The performance of our method is compared with a state-of-art video tracking method. It shows that our tracking method is more stable and accurate, and has overwhelming superiority when there is a great appearance change. A vision-based robot using our tracking method can navigate in cluttered environment successfully.
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Motion blur is common in images captured by handheld devices, arising from hand, device and/or object motion. To restore sharp images from the images degraded by the motion, it is extremely important to assess the quality of the captured image and its corresponding blur profile as accurately as possible. In image deblurring, the perceived image quality is usually assessed by the SSIM and the PSNR metric. These methods have certain limitations and the objective image quality assessed by these methods can be contradictory to the subjectively perceived image quality. We propose a new reference image based objective blur level (BL) metric by utilizing point spread function/blur kernel analysis in this paper. In our experiments, we found our BL metric describes the perceived image quality of motion blurred images better than SSIM and PSNR in most cases. Additionally, our method performs well in low light and low texture images, where SSIM and PSNR metrics are prone to failure in describing blurriness/sharpness of the image.
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To study an observer's eye movements during realistic tasks, the observer should be free to move naturally throughout our three-dimensional world. Therefore, a technique to determine an observer's point-of-regard (POR) as well as his/her motion throughout a scene in three dimensions with minor user input is proposed. This requires robust feature tracking and calibration of the scene camera in order to determine the 3D location and orientation of the scene camera in the world. With this information, calibrated 2D PORs can be triangulated to 3D positions in the world; the scale of the world coordinate system can be obtained via input of the distance between two known points in the scene. Information about scene camera movement and tracked features can also be used to obtain observer position and head orientation for all video frames. The final observer motion -- including the observer's positions and head orientations -- and PORs are expressed in 3D world coordinates. The result is knowledge of not only eye movements but head movements as well allowing for the evaluation of how an observer combines head and eye movements to perform a visual task. Additionally, knowledge of 3D information opens the door for many more options for visualization of eye-tracking results.
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Most important measures in image research is the analysis of image quality. Quality analysis of images plays very important role in generation of multi viewed images and for the automatic image development in near future. Image quality measure can be performed by two ways subjective and objective. There are various subjective & objectives quality analysis technics are emerged in past year but these are applicable on single camera images. In multi camera images very less research is carried out for the quality analysis. Multi image is nothing but combining multiple images or events into single image. In multi camera images the quality is depend on various factors like configuration, calibration, features of different cameras used to take the images. In multi camera images we can find two types of distortions like photometric and geometric. This paper deals with the various methods and their results to achieve the quality of multi camera images. Here main focus is on the methods like PSNR, MSSIM & VIF and the results of these methods is compared with MIVQM Multi camera image Vision with quality measure (MIVQM) is calculated by combining indices like spatial motion, luminance and contrast and edge based arrangement. The result and comparison with the other measures, like Peak Signal-to Noise Ratio (PSNR), Mean Structural Similarity (SSIM), and Visual Information Fidelity (VIF) prove that MIVQM surpass other measure to capture the quality of images from multi camera system.
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Previous methods for estimating the motion of an observer through a static scene require that image velocities can be measured. For the case of motion through a cluttered 3D scene, however, measuring optical flow is problematic because of the high density of depth discontinuities. This paper introduces a method for estimating motion through a cluttered 3D scene that does not measure velocities at individual points. Instead the method measures a distribution of velocities over local image regions. We show that motion through a cluttered scene produces a bowtie pattern in the power spectra of local image regions. We show how to estimate the parameters of the bowtie for different image regions and how to use these parameters to estimate observer motion. We demonstrate our method on synthetic and real data sequences.
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