A system is provided for determining coordinated motion between objects. The system includes a velocity data receiving component, a position data receiving component, a multidimensional indexing component and a determining component. The velocity data receiving component receives velocity data of the objects. The position data receiving component receives position data of the objects. The multidimensional indexing component generates multidimensional indices of the objects based on the velocity data and position data. The determining component determines whether there is coordinated motion between objects based on the multidimensional indices.

Provided is an image processing device including an acquisition unit configured to acquire information on an imaging position and an imaging direction in units of frame images that constitute a moving image obtained through capturing by an imaging unit, a converted image generation unit configured to generate converted images having different imaging directions for each frame image that constitutes the moving image based on the frame image itself and preceding and succeeding frame images of the frame image, an evaluation value calculation unit configured to calculate an evaluation value for each converted moving image constituted by combining the converted image and the original frame image, the evaluation value being used to evaluate a blur between the converted images or between the original frame images, and a selection unit configured to select a converted moving image with less blur based on an evaluation value calculated by the evaluation value calculation unit.

Provided is an image processing device including an acquisition unit configured to acquire information on an imaging position and an imaging direction in units of frame images that constitute a moving image obtained through capturing by an imaging unit, a converted image generation unit configured to generate converted images having different imaging directions for each frame image that constitutes the moving image based on the frame image itself and preceding and succeeding frame images of the frame image, an evaluation value calculation unit configured to calculate an evaluation value for each converted moving image constituted by combining the converted image and the original frame image, the evaluation value being used to evaluate a blur between the converted images or between the original frame images, and a selection unit configured to select a converted moving image with less blur based on an evaluation value calculated by the evaluation value calculation unit.

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

A system and method are arranged to provide recommendations to a user of a vehicle. In one aspect, the vehicle navigates in an autonomous mode and the sensors provide information that is based on the location of the vehicle and output from sensors directed to the environment surrounding the vehicle. In further aspects, both current and previous sensor data is used to make the recommendations, as well as data based on the sensors of other vehicles.

A system and method are arranged to provide recommendations to a user of a vehicle. In one aspect, the vehicle navigates in an autonomous mode and the sensors provide information that is based on the location of the vehicle and output from sensors directed to the environment surrounding the vehicle. In further aspects, both current and previous sensor data is used to make the recommendations, as well as data based on the sensors of other vehicles.

Methods and apparatus to improve detection and false alarm rate over image segmentation are disclosed. An example method includes determining a first score based on a first pixel distance between a first feature of a first object in a reference image and a second feature in a second image of a search area, determining a second score corresponding to a second pixel distance between the second feature and a mathematical representation of a plurality of shapes representing a similarity between the second feature in the second image of the search area and the plurality of shapes observed simultaneously. The method further includes determining a normalized score by normalizing the first score based on the second score and identifying a second object in the second image of the search area as the first object in the reference image when the normalized score satisfies a threshold score.

Methods, apparatus, systems and articles of manufacture are disclosed herein. An example method to improve object detection and false alarm rate over image segmentation includes overlaying a first object of a first image onto a second image. A first score based on a first chamfer distance between first edges of the first object and second edges in the second image is determined. A second score corresponding to a second chamfer distance between the second edges and a mathematical representation of a plurality of shapes is determined, the second score representing a similarity between the second edges and the plurality of shapes observed simultaneously. A normalized score is determined by normalizing the first score based on the second score. A presence of the second object in the second image matching the first object is detected based on whether the normalized score satisfies a threshold score.

A robust algorithm is disclosed for real-time strain imaging. The method can be implemented on conventional medical imaging modalities such as ultrasound, MRI, etc. The pre- and post-deformation images, acquired by an imaging system, are used by the algorithm to produce the strain map. Deformation can be performed by any available means, such as an existing part of the imaging device (as in freehand elastography) or a separate compression fixture. Due to the computational efficiency of the algorithm, any conventional processing platform, including personal computers, tablet PCs, and smart phones can be employed for strain imaging, to construct an ultra-portable strain imaging system.