Abstract: The present disclosure provides a blockchain-based data processing method, device, and an electronic device thereof. The method includes the steps of acquiring a first block in a blockchain at a current node; broadcasting a first transaction to other nodes in the blockchain, wherein data related to the first transaction includes a model parameter; and creating a second block based on the model parameter and adding the second block into the blockchain in response to two or more second transactions corresponding to the first transaction, wherein data related to the second transactions include voting results corresponding to the first transaction.

Abstract: The present disclosure provides a blockchain-based data processing method, device, and an electronic device thereof. The method includes the steps of acquiring a first block in a blockchain at a current node; broadcasting a first transaction to other nodes in the blockchain, wherein data related to the first transaction includes a model parameter; and creating a second block based on the model parameter and adding the second block into the blockchain in response to two or more second transactions corresponding to the first transaction, wherein data related to the second transactions include voting results corresponding to the first transaction.

Abstract: A method and a system for analyzing a target in a stereo image by displaying the stereo image using a cascade structure are disclosed. The method includes for the input stereo image, generating, based on a first relevant feature, rule or model of the stereo image, at least a first first-level structure map, each of the first first-level structure maps being generated based on an individual tolerance level of the first relevant feature, rule or model, and each of the first first-level structure maps including the target at an individual first division level; and at least partly integrating the first first-level structure maps and analyzing the target in the stereo image, to obtain a structure map of a first-level target analysis result including the target.

Abstract: Disclosed is an image feature extraction method including a step of defining a combination of at least two kinds of solid angles along at least two directions, of an input spherical image; a step of determining respective values of the combination of the at least two kinds of solid angles, so that surface areas of spherical crowns of spherical segments, which are obtained by dividing the spherical image by the respective values of the combination of the at least two kinds solid angles, have a same value; and a step of generating, by utilizing the respective values of the combination of the at least two kinds of solid angles, an image feature template so as to conduct image feature extraction.

Abstract: Disclosed is an object detection method used to detect an object in an image pair corresponding to a current frame. The image pair includes an original image of the current frame and a disparity map of the same current frame. The original image of the current frame includes at least one of a grayscale image and a color image of the current frame. The object detection method comprises steps of obtaining a first detection object detected in the disparity map of the current frame; acquiring an original detection object detected in the original image of the current frame; correcting, based on the original detection object, the first detection object so as to obtain a second detection object; and outputting the second detection object.

Abstract: A method and an apparatus for detecting a continuous road partition with a height that includes obtaining disparity maps having the continuous road partition, and U-disparity maps corresponding to the disparity maps; obtaining an intermediate detection result of the continuous road partition detected from the U-disparity maps of first N frames; and detecting the continuous road partition from the U-disparity map of a current frame, based on the obtained intermediate detection result.

Abstract: Disclosed is an object tracking method. The method includes steps of obtaining a first boundary region of a waiting-for-recognition object in the disparity map related to the current frame; calculating a probability of each valid pixel in the first boundary region so as to get a pixel probability map of the waiting-for-recognition object; obtaining historic tracking data of each tracked object, which includes identifier information of the tracked object and a pixel probability map related to each of one or more prior frame related disparity maps prior to the disparity map related to the current frame; determining identifier information of the waiting-for-recognition object, and updating the pixel probability map of the waiting-for-recognition object; and updating, based on the updated pixel probability map of the waiting-for-recognition object, the first boundary region of the waiting-for-recognition object, so as to get a second boundary region of the waiting-for-recognition object.

Abstract: Disclosed is an image feature extraction method including a step of defining a combination of at least two kinds of solid angles along at least two directions, of an input spherical image; a step of determining respective values of the combination of the at least two kinds of solid angles, so that surface areas of spherical crowns of spherical segments, which are obtained by dividing the spherical image by the respective values of the combination of the at least two kinds solid angles, have a same value; and a step of generating, by utilizing the respective values of the combination of the at least two kinds of solid angles, an image feature template so as to conduct image feature extraction.

Abstract: A method and a device are disclosed for detecting a drivable region of a road, the method comprising the steps of: deriving a disparity map from a gray-scale map including the road and detecting the road from the disparity map; removing a part with a height above the road greater than a predetermined height threshold from the disparity map so as to generate a sub-disparity map; converting the sub-disparity map into a U-disparity map; detecting the drivable region from the U-disparity map; and converting the drivable region detected from the U-disparity map into the drivable region within the gray-scale map.

Abstract: Disclosed is an object detection method used to detect an object in an image pair corresponding to a current frame. The image pair includes an original image of the current frame and a disparity map of the same current frame. The original image of the current frame includes at least one of a grayscale image and a color image of the current frame. The object detection method comprises steps of obtaining a first detection object detected in the disparity map of the current frame; acquiring an original detection object detected in the original image of the current frame; correcting, based on the original detection object, the first detection object so as to obtain a second detection object; and outputting the second detection object.

Abstract: A method and a system for analyzing a target in a stereo image by displaying the stereo image using a cascade structure are disclosed. The method includes for the input stereo image, generating, based on a first relevant feature, rule or model of the stereo image, at least a first first-level structure map, each of the first first-level structure maps being generated based on an individual tolerance level of the first relevant feature, rule or model, and each of the first first-level structure maps including the target at an individual first division level; and at least partly integrating the first first-level structure maps and analyzing the target in the stereo image, to obtain a structure map of a first-level target analysis result including the target.

Abstract: Disclosed is an object tracking method. The method includes steps of obtaining a first boundary region of a waiting-for-recognition object in the disparity map related to the current frame; calculating a probability of each valid pixel in the first boundary region so as to get a pixel probability map of the waiting-for-recognition object; obtaining historic tracking data of each tracked object, which includes identifier information of the tracked object and a pixel probability map related to each of one or more prior frame related disparity maps prior to the disparity map related to the current frame; determining identifier information of the waiting-for-recognition object, and updating the pixel probability map of the waiting-for-recognition object; and updating, based on the updated pixel probability map of the waiting-for-recognition object, the first boundary region of the waiting-for-recognition object, so as to get a second boundary region of the waiting-for-recognition object.

Abstract: Disclosed are a method and a system for detecting a vehicle position by employing a polarization image. The method comprises a step of capturing a polarization image by using a polarization camera; a step of acquiring two road shoulders in the polarization image based on a difference between a road surface and each of the two road shoulders in the polarization image, and determining a part between the two road shoulders as the road surface; a step of detecting at least one vehicle bottom from the road surface based on a significant pixel value difference between each wheel and the road surface in the polarization image; and a step of generating a vehicle position from the vehicle bottom based on a pixel value difference between a vehicle outline corresponding to the vehicle bottom and background in the polarization image.

Abstract: A method and a device are disclosed for detecting a drivable region of a road, the method comprising the steps of: deriving a disparity map from a gray-scale map including the road and detecting the road from the disparity map; removing a part with a height above the road greater than a predetermined height threshold from the disparity map so as to generate a sub-disparity map; converting the sub-disparity map into a U-disparity map; detecting the drivable region from the U-disparity map; and converting the drivable region detected from the U-disparity map into the drivable region within the gray-scale map.

Abstract: A method and an apparatus are disclosed for detecting a continuous road partition with a height, the method comprising the steps of: obtaining disparity maps having the continuous road partition, and U-disparity maps corresponding to the disparity maps; obtaining an intermediate detection result of the continuous road partition detected from the U-disparity maps of first N frames; and detecting the continuous road partition from the U-disparity map of a current frame, based on the obtained intermediate detection result.

Abstract: An object image detection device is disclosed that is able to rapidly detect an object image from an input image without a great deal of computation. The object image detection device includes an object image classification unit for determining whether the object images are included in an image having a given orientation, an image orientation detection unit for detecting orientation of the input image, an image rotation unit for rotating the object image classification unit according to the detected orientation of the input image, and a detection unit for detecting the object images from the input image by using the rotated object image classification unit.

Abstract: A night-scene light source detecting device includes a pixel value obtaining unit configured to obtain a pixel value of each pixel in an input image; a night-scene-feature extraction unit provided for extracting a zone area of a mean corrected-brightness value and a high corrected-brightness value of the input image as two night-scene features based on the pixel value of each pixel in the input image; a night-scene image detection unit provided for determining whether the input image is a night-scene image or not based on the two night-scene features; a specific color detection unit provided for detecting whether each pixel belongs to specific color or not; and a night-scene light source determining unit provided for determining whether the night-scene image is picked up under irradiation by the specific light sources in a night scene or not based on the result of the specific color detection.

Abstract: Disclosed is a sky detection system that detects sky in an image collection device. The system includes an image collection unit that collects information of a color image; a color-feature extraction unit that extracts a color feature of each pixel from the collected image; a distance measurement unit that measures a distance between each pixel of the collected image and a lens; a first classification unit that classifies each pixel of the collected image as either a sky pixel or a non-sky pixel based on the color feature; and a second classification unit that further classifies each pixel of the collected image as either the sky pixel or the non-sky pixel based on the distance and a result of the first classification unit.

Abstract: An image processing apparatus and an image processing method blurs a background portion of an image. The image processing apparatus includes an image acquiring unit for acquiring original image data; an image processing unit for separating background pixels from foreground pixels in the original image; an image filtering unit for filtering the background pixels; and an image merging unit for merging the foreground pixels with the filtered background pixels in order to generate a new image having a blurred background.

Abstract: Disclosed are a method and a system for detecting a vehicle position by employing a polarization image. The method comprises a step of capturing a polarization image by using a polarization camera; a step of acquiring two road shoulders in the polarization image based on a difference between a road surface and each of the two road shoulders in the polarization image, and determining a part between the two road shoulders as the road surface; a step of detecting at least one vehicle bottom from the road surface based on a significant pixel value difference between each wheel and the road surface in the polarization image; and a step of generating a vehicle position from the vehicle bottom based on a pixel value difference between a vehicle outline corresponding to the vehicle bottom and background in the polarization image.