267 results about "Confidence map" patented technology

Systems in accordance with embodiments of the invention can perform parallax detection and correction in images captured using array cameras. Due to the different viewpoints of the cameras, parallax results in variations in the position of objects within the captured images of the scene. Methods in accordance with embodiments of the invention provide an accurate account of the pixel disparity due to parallax between the different cameras in the array, so that appropriate scene-dependent geometric shifts can be applied to the pixels of the captured images when performing super-resolution processing. In several embodiments, detecting parallax involves using competing subsets of images to estimate the depth of a pixel location in an image from a reference viewpoint. In a number of embodiments, generating depth estimates considers the similarity of pixels in multiple spectral channels. In certain embodiments, generating depth estimates involves generating a confidence map indicating the reliability of depth estimates.

A method locates an object in a sequence of frames of a video. A feature vector is constructed for every pixel in each frame. The feature vector is used to training the weak classifiers. The weak classifiers separate pixels that are associated with the object from pixels that are associated with the background. The set of weak classifiers are combined into a strong classifier. The strong classifier labels pixels in a frame to generate a confidence map. A ‘peak’ in the confidence is located using a mean-shift operation. The peak indicates a location of the object in the frame. That is, the confidence map distinguishes the object from the background in the video.

An image processing system includes a calculation unit, a reconstruction unit, a confidence map estimation unit and an up-sampling unit. The up-sampling unit is configured to perform a joint bilateral up-sampling on depth information of a first input image based on a confidence map of the first input image and a second input image with respect to an object and increase a first resolution of the first input image to a second resolution to provide an output image with the second resolution.

There is provided methods, apparatuses and computer program products for image processing in which a pair of images may be downsampled to lower resolution pair of images and further to obtain a disparity image representing estimated disparity between at least a subset of pixels in the pair of images. A confidence of the disparity estimation may be obtained and inserted into a confidence map. The disparity image and the confidence map may be filtered jointly to obtain a filtered disparity image and a filtered confidence map by using a spatial neighborhood of the pixel location. An estimated disparity distribution of the pair of images may be obtained through the filtered disparity image and the confidence map.

The invention discloses a complex scene-based human body key point detection system and method. The method comprises the following steps of: inputting monitor video information to obtain a single-frame static map and a multi-frame optical flow graph; extracting features of the single-frame static map through a convolution operation so as to obtain a feature map, and in order to solve influences, on personnel target detection, of interference targets under complex scenes, judging a practical confidence coefficient and a preset confidence coefficient of the feature map by adoption of a personneltarget detection algorithm so as to obtain a discretized personnel target surrounding box; and carrying out optical flow overlapping on the multi-frame optical flow graph to form a two-dimensional vector field; extracting features in the discretized personnel target surrounding box to obtain a feature map, obtaining key points and association degrees of parts, generating a part confidence map foreach part of a human body by utilizing a predictor, and accurately detecting human body key points through the part confidence map and the two-dimensional vector field. The system and method are usedfor human body key point detection under complex scenes so as to realize accurate detection of personnel target key points.

A method for texture reconstruction associated with a three-dimensional scan of an object includes scanning, at a processor, a sequence of image frames captured by an image capture device at different three-dimensional viewpoints. The method also includes generating a composite confidence map based on the sequence of image frames. The composite confidence map includes pixel values for scanned pixels in the sequence of image frames. The method further includes identifying one or more holes of a three-dimensional model based on the composite confidence map.

Described is a system for multi-object detection and recognition in cluttered scenes. The system receives an image patch containing multiple objects of interest as input. The system evaluates a likelihood of existence of an object of interest in each sub-window of a set of overlapping sub-windows. A confidence map having confidence values corresponding to the sub-windows is generated. A non-maxima suppression technique is applied to the confidence map to eliminate sub-windows having confidence values below a local maximum confidence value. A global maximum confidence value is determined for a sub-window corresponding to a location of an instance of an object of interest in the image patch. The sub-window corresponding to the location of the instance of the object of interest is removed from the confidence map. The system iterates until a predetermined stopping criteria is met. Finally, detection information related to multiple instances of the object of interest is output.

The invention discloses a continuous and stable tracking method of a weak moving target in a dynamic background. The method includes acquiring the video data, and processing each frame of image in the following steps of acquiring the position coordinate of the moving target to be tracked in the current frame of image and determining the target tracking frame according to the position; establishing a spatial context model of the current frame of image for the area in the target tracking frame with a Bayesian framework; performing convolution calculation by means of the spatial context model of the current frame of image and the next frame of image to obtain a confidence map of the position of the moving target to be tracked in the next frame of image, the position with the greatest confidence is the position of the moving target to be tracked in the next frame of image; based on the double threshold moving target crisis determination, determining that when the moving target to be tracked is not shielded or lost, outputting the moving target position in the next frame of image when the tracking of the current frame of image is completed; otherwise, updating the target tracking frame and re-checking. The method realizes the continuous and stable tracking of the target under the condition of background interference and shielding.

A method of monitoring a dual damascene procedure that includes calculating a pre-processing confidence map for a damascene process, the pre-processing confidence map including confidence data for a first set of dies on the wafer. An expanded pre-processing measurement recipe is established for the damascene process when one or more values in the pre-processing confidence map are not within confidence limits established for the damascene process. A reduced pre-processing measurement recipe for the first damascene process is established when one or more values in the pre-processing confidence map are within confidence limits established for the damascene process.

A method of processing a wafer is presented that includes creating a pre-processing measurement map using measured metrology data for the wafer including metrology data for at least one isolated structure on the wafer, metrology data for at least one nested structure on the wafer, or mask data. At least one pre-processing prediction map is calculated for the wafer. A pre-processing confidence map is calculated for the wafer. The pre-processing confidence map includes a set of confidence data for the plurality of dies on the wafer. A prioritized measurement site is determined when the confidence data for one or more dies is not within the confidence limits. A new measurement recipe that includes the prioritized measurement site is then created.

A method and apparatus for fixing a depth map wrap-around error and a depth map coverage by a confidence map. The method includes dynamically determining a threshold for the confidence map threshold based on an ambient light environment, wherein the threshold is reconfigurable depending on LED light strength distribution in TOF sensor, extracting, via the digital processor, four phases of Time Of Flight raw data from the Time Of Flight sensor data, computing a phase differential signal array for fixed point utilizing the four phases of the Time Of Flight raw data, computing the depth map, confidence map and the average light signal strength utilizing the phase differential signal array, obtaining the dynamic confidence map threshold for wrap around error correction utilizing the average light signal strength, and correcting the depth map wrap-around error utilizing the depth map, the confidence map and the dynamic confidence map threshold.

The invention relates to a target tracking method and a system based on an on-line initialization gradient enhancement regression tree. In the system consisting of a video input end, a tracking target output end and an on-line training classifier, the method comprises the steps of 1) selecting a tracking target from a video series and extracting positive and negative samples of a Haar-like feature, 2) randomly establishing the on-line classifier according to the positive and negative samples to obtain a training residual error, 3) conducting training amendment by taking the training residual error as a training sample of the on-line classifier and establishing a target model, and 4) acquiring an image confidence map from a next frame of video image, determining a maximum position of a confidence value in a target window, and accomplishing tracking. According to the method and the system, the tree can be converged to an optimum point quickly to ensure that the random forest detection optimization is accomplished, the classifier is updated through on-line study, and the problems of appearance variation, rapid movement and shielding of a target are solved well.

The invention discloses a multi-scale superpixel-fused target tracking method. Through the method, a more accurate target confidence map can be obtained by constructing a superpixel-based discriminant appearance model, and consequently the target tracking accuracy degree and robustness are effectively improved. Specifically, the method includes the first step of adopting superpixel classification results with different scales to vote for properties of one pixel point and then obtaining the more accurate target confidence map, and the second step of updating the appearance model by continuously updating a classifier so that the appearance model can continuously adapt to scenes such as illumination changes and complicated backgrounds. Consequently, more accurate and robust tracking can be achieved.

The invention particularly discloses a single-image robot disordered target grabbing method based on pose estimation and correction. The method comprises the steps: S1, generating an image data set ofa to-be-grabbed object model; S2, constructing a convolutional neural network model according to the image data set in the step S1; S3, importing the two-dimensional image of the to-be-grabbed objectinto the trained convolutional neural network model to extract a corresponding confidence map and a vector field; S4, obtaining a predicted translation amount and a predicted rotation amount of the to-be-grabbed object; S5, finding the optimal grabbing point of the object to be grabbed and calculating the measurement translation amount of the depth camera; S6, performing grabbing safety distancecorrection according to the predicted translation amount of the object to be grabbed and the measured translation amount of the depth camera, executing correction data grabbing if the correction succeeds, and entering S7 if the correction fails; and S7, repeating the steps S3-S6. The disordered target grabbing method provided by the invention has the characteristics of high reliability, strong robustness and good real-time performance, can meet the existing industrial production requirements, and has a relatively high application value.

The invention relates to an image processing method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps of: obtaining a sample; obtainingan initial depth image collected by a depth camera and a confidence map corresponding to the initial depth image; acquiring a color image by the color camera; obtaining an initial depth value corresponding to a first pixel point in the initial depth image, obtaining a confidence coefficient map of a first pixel point and a confidence coefficient value of a second pixel point corresponding to the first pixel point in the confidence coefficient map, obtaining a brightness value of a third pixel point corresponding to the first pixel point in the color image, determining a confidence coefficientthreshold of the first pixel point based on the brightness value, and determining that an initial depth value is effective when the confidence coefficient value is greater than or equal to the confidence coefficient threshold. The confidence coefficient threshold value can be determined according to the brightness of the corresponding pixel point in the color image, and whether the depth information of the corresponding pixel point in the depth image is effective or not is determined according to the confidence coefficient threshold value, so that the accuracy of the depth information can be improved.

The invention discloses a target tracking method fusing convolutional network features and a discriminant correlation filter. An end-to-end lightweight network system structure is established; and theconvolutional features are trained by learning rich stream information in continuous frames, thereby improving feature representation and tracking precision. A correlation filter tracking component is constructed as a special layer in a network to track a single image block; in the tracking process, a target block and multiple background blocks are tracked at the same time; by perceiving a structural relationship between a target and surrounding background blocks, a model is built for a part with a high discrimination degree for the target and a surrounding environment; a target tracking effect is measured through a relationship between a peak sidelobe ratio and a peak value of a confidence map; and under the condition of high tracking difficulty such as large-area shielding, target shapeextreme deformation, illumination drastic change, locating is performed by automatically utilizing the discriminated background part.