27 results about "Temporal filter" patented technology

The invention provides an infrared image flicker processing method, system and device and a medium. The method comprises the steps of generating a blocking piece mean value based on blocking piece image data of continuous N frames obtained during single-point correction; processing the histograms of the two-time single-point correction gap images according to the separation blade mean value to generate a statistical histogram of the images; performing first filtering processing on the statistical histogram to generate a first filtering result of the histogram; after the first filtering result is calculated in an accumulated mode, a gray mapping table is calculated, second filtering processing is carried out, and a second filtering result of the gray mapping table is generated; and performing time domain filtering processing on the second filtering result to realize flicker processing of the infrared image. According to the infrared image flicker processing method, the flicker phenomenon of the infrared image can be effectively reduced, and the stability and the visual effect of the image are improved.

In various embodiments, when reference picture resampling is applied to achieve better coding efficiency, methods and devices adapt temporal filtering of a noise-reduced input video prior to encoding according to a resolution ratio. According to one embodiment, temporal filtering strength values are fine-tuned at a pre-processing module according to the resolution ratio of the RPR mode. According to another embodiment, temporal filtering is deferred after rescaling when the RPR mode is enabled, and filtering strength values are fine-tuned according to the resolution ratio of the RPR mode. A third embodiment proposes to refine motion estimation in the encoder by setting the block size of motion estimation in temporal filtering according to the resolution ratio of the RPR mode.

Light time-of-flight camera for distance measurement with a time-of-flight sensor with at least one time-of-flight pixel, with a lighting system for emitting modulated light, the time-of-flight camera is designed for distance measurements according to a phase measurement principle and is designed in this way, that, a) for at least a part of the light-time-of-flight pixels, for determining a distance (d w ) of an object at least two measurements (A1, A2, ...) are carried out with different modulation frequencies (f1, f2, ...) and thus different uniqueness ranges (UR1, UR2, ...), whereby in each measurement (A i ) a residual distance (d i = mod(d, UR i )) is measured, b) starting from these residual distances (d i ) for each light travel time pixel, initially a sub-area index M of a total distance curve (UR) extd) is assigned, c) a field of the initially assigned sub-area indices M is processed using a "nearest neighbor filter" and / or a temporal filter, to generate a corrected, final sub-area index (M_final) for the respective light-time-of-flight pixel, and only then, d) the distance (d w ) by projecting the remaining distances (d i ) is determined on that sub-area of ​​the total distance curve which is defined by the corrected, final sub-area index (M_final).

A system and method for identifying container numbers on front-mounted cranes are disclosed, relating to the field of container number identification on front-mounted cranes. To address the shortcomings of existing methods, such as inability to adapt to rainy or snowy weather and low-light conditions, poor container number identification performance, and poor anti-interference capabilities, this invention acquires video or images of containers on front-mounted cranes; extracts continuous frames from the video at a fixed frequency and performs grayscale processing on each frame to reduce computational complexity; applies temporal filtering to the preprocessed video / image; applies spatial filtering to the filtered video / image to remove isolated noise points; reduces high-frequency noise and smooths the image through weighted averaging; enhances image contrast by adjusting the grayscale distribution of the image, making the container number area clearer; and performs container number area detection and character recognition on the processed image to achieve automatic container number identification. This invention is mainly used for identifying container numbers on front-mounted cranes.

The invention discloses a bare soil intelligent identification method and system of a high-resolution remote sensing image, and relates to the technical field of ecological environment remote sensing monitoring, high-resolution remote sensing image data of a to-be-identified area is acquired and preprocessed, and preprocessed high-resolution remote sensing image data is obtained; performing filtering optimization on the preprocessed high-resolution remote sensing image data by adopting a dynamic adaptive multi-temporal filtering optimization method to obtain an optimized high-resolution remote sensing image data image; cutting the optimized high-resolution remote sensing image data, extracting contour position coordinates of a mask image, and constructing a training set; and inputting the training set into a bare soil recognition model for training to obtain a trained bare soil recognition model, and recognizing the bare soil plaques of the to-be-recognized region based on the trained bare soil recognition model. The multi-scale bare soil plaques under a complex background are accurately and automatically identified under a relatively low computing resource demand, and the distinction degree between the bare soil and other ground features is enhanced.

This paper presents a method for fusing and reconstructing trajectory data from multiple sources in hybrid traffic flow, relating to the fields of multi-source sensor fusion localization and target tracking. The method acquires the 3D acceleration sequence and RGB video stream of the target object, extracting the geometric center coordinate sequence. Based on the frequency domain energy distribution of acceleration, the target's fundamental frequency parameters are obtained. Temporal filtering coefficients are constructed using the dynamic sampling rate of the video, and the geometric center coordinates are filtered to obtain a corrected center coordinate sequence, which is then mapped to a measurement vector. Kinematic state prediction is performed using acceleration to obtain prior inferred state variables, and then posterior updates are performed using the measurement vectors to obtain the optimal spatial position. The reconstructed trajectory sequence is generated by stitching the data together, and the sliding time window is updated based on feedback-optimized parameters. This method effectively eliminates spurious displacements caused by gait, fundamentally eliminating jagged oscillations in the trajectory, resulting in a smooth and stable fused trajectory.

A neural network-based medical image processing apparatus according to the present invention, which identifies a small intestine region from a medical image acquired by a capsule endoscope, comprises: a memory equipped with an organ identification algorithm for performing organ identification from the medical image; and a processor for applying the medical image to the organ identification algorithm such that the small intestine region is identified. The organ identification algorithm includes: a convolutional neural network algorithm for identifying an organ included in the medical image as the stomach, the small intestine, and the large intestine to identify the small intestine region therefrom; and a temporal filtering algorithm linked to the convolutional neural network algorithm to reduce images which may be misidentified by the convolutional neural network algorithm.

Embodiments described relate to adjusting one or more pixel values in a region corresponding to an image artifact based on one or more spatial characteristics and one or more temporal characteristics to reduce or eliminate the image artifact. An electronic device may employ spatio-temporal filtering circuitry that includes spatial adjustment circuitry, temporal adjustment circuitry, and / or fuse circuitry. The spatial adjustment circuitry may perform a spatial adjustment of image data in the region of the image frame. Moreover, the temporal adjustment circuitry may perform a temporal adjustment of the image data in the region of the image frame. The fuse circuitry may then merge the spatially adjusted image data and the temporally adjusted image data in the region of the image frame.

The application discloses a training method and a decoding method of a single-time spatial-temporal filter based on double-frequency intermodulation, and the training method comprises the following steps: displaying a plurality of training targets in a row-column distribution on a visual stimulation interface, wherein each training target is jointly coded by a first stimulation signal and a second stimulation signal; collecting single-time training data of a subject; pre-processing the single-time training data to obtain ; performing time-domain displacement processing on the pre-processed mean template of the training target to obtain a time-shifted template; integrating the pre-processed mean template and the time-shifted template of the plurality of training targets to obtain a training target matrix; obtaining a standard reference signal of the training target, wherein the standard reference signal comprises a double-frequency intermodulation component; obtaining a training target reference signal matrix according to the standard reference signal; and obtaining a first spatial-temporal filter according to the training target reference signal matrix. The application achieves the technical effect that a spatial-temporal filter with good performance is obtained by using single-time training data, and provides a new method for efficient encoding and decoding of a large-instruction-set visual evoked brain-computer interface.

The invention provides an image encoding method and an image encoding device. The image coding method comprises the steps that first quantization parameter information of a code rate control unit block in an image to be filtered is acquired, and the code rate control unit block represents a processing unit used for code rate distribution and image quality control; adjusting a filtering weight of a filtering block to be subjected to motion compensation time domain filtering in the bit rate control unit block based on the first quantization parameter information; performing motion compensation time domain filtering on the filtering block based on the adjusted filtering weight; and performing encoding based on the filtering result.

Provided is a method for encoding a video, an electronic device and a storage medium, relating to the field of video encoding. The method includes: obtaining a reference frame that is adjacent to a current frame and has established hierarchical division of an initial ROI; reusing a motion vector generated by the reference frame in a motion compensated temporal filter process to determine an ROI position prediction value of the current frame; extracting facial feature points from the current frame, and correcting the ROI position prediction value based on a spatial topological relationship of feature points in the initial ROI; configuring differentiated quantization parameters for a main face region, a secondary face region and a background region after correction respectively; dynamically allocating a three-region bitrate according to a real-time network bandwidth; and outputting an encoded frame of the current frame and associated ROI-level metadata.

The present disclosure provides a video encoding method and apparatus, an electronic device, and a non-transitory computer-readable storage medium. The video encoding method includes performing motion estimation and motion compensation on a current block; calculating a block error between an extended block of the current block and an extended block of a motion compensated block; determining a filtering weight based on the block error; temporally filtering the current block by performing a weighted sum of the current block and the motion compensated block based on the determined filtering weight; and encoding the current block based on a result of the temporal filtering. The present disclosure can consider the relationship between a block-level filtering weight factor and surrounding pixels in a motion compensation-based temporal filtering process, and utilize the surrounding pixels to correct the result of the block-level error term in the temporal filtering weight, thereby improving the temporal filtering effect and improving the encoding efficiency.

Provided are a video coding method and apparatus, an electronic device, a program product, a storage medium, and a bit stream storage method, the video coding method comprising: performing motion compensation time domain filtering on a current frame based on the current frame and a reference frame of the current frame; the current frame is coded based on the motion compensation time domain filtering result of the current frame, and motion compensation time domain filtering is carried out on the current frame based on the current frame and the reference frame of the current frame, and comprises the following steps: in the process of carrying out motion compensation time domain filtering on the current frame, carrying out motion compensation time domain filtering on the reference frame of the current frame; spatial enhancement filtering is performed on the input of the motion compensation phase, and / or spatial enhancement filtering is performed on the input of the filtering phase.

This invention relates to the field of sound field reproduction technology, specifically, to a temporal broadband optimization algorithm for speaker placement in a personal audio zone system, comprising the following steps: assuming L0 speakers are located at L0 candidate positions, the l-th speaker is muted, and the temporal filter coefficients of the remaining L0-1 speakers are calculated; based on the obtained optimal speaker filter coefficients, the performance index of the remaining L0-1 speakers is calculated when the l-th speaker is muted; the performance index corresponding to l is calculated for l taking values ​​of 1, 2, ..., L0, and the speaker position corresponding to the minimum performance index is found and denoted as l0; the l0-th speaker is removed from the candidate positions, and the remaining L0-1 speaker positions are taken as new candidate positions; for the new L0-1 speaker candidate positions, steps one to three are repeated for iterative optimization until the pre-set L speaker positions are selected.

A system for regulating temporal filtering strength is configurable to: (i) obtain a light level indicator indicating light level associated with a real-world environment; (ii) determine a motion compensation confidence indicator using a current image and a previous image; (iii) determine a filter weight by processing the light level indicator and the motion compensation confidence indicator using a filter strength regulation module; and (iv) generate an output image by using at least the filter weight to filter a current frame with a previous frame.

The invention belongs to the technical field of computer vision and video analysis, and particularly relates to a time sequence adaptive filtering weak supervision video anomaly detection method based on CLIP assistance. According to the method, a time sequence modeling and filtering framework fusing vision-language cross-modal information is designed, and the method comprises the steps that firstly, video local and global time sequence dependence is decomposed and modeled through a local-global time sequence adapter, and the capturing capacity of a model for the dynamic evolution process of abnormal events is enhanced; secondly, a double-branch architecture is adopted, pure visual features are used for completing coarse-grained anomaly detection, fine-grained anomaly recognition is achieved in combination with visual-language alignment features, and detection requirements of different complexity scenes are met; and finally, introducing a Fourier transform time sequence filter, adaptively suppressing noise in a frequency domain, strengthening abnormal feature representation, and improving the anti-interference capability of the model. The method is remarkably superior to an existing weak supervision method, and the precision and robustness of anomaly detection can be effectively improved through the synergistic effect of the modules.

A method for a multi-antenna receiver arrangement, the multi-antenna receiver arrangement being comprisable in a wireless device, WD, the method comprising: receiving a first plurality of analog radio signals; converting the first plurality of analog radio signals into a first plurality of digital signals; extracting reference signals from each of the first plurality of digital signals; determining characteristics for each of the first plurality of digital signals based on the extracted reference signals; processing the first plurality of digital signals with a second plurality of spatial-temporal filters to obtain a second plurality of combined signals, wherein the first plurality is larger than the second plurality. Corresponding computer program product, multi-antenna receiver arrangement, and wireless device are also disclosed.

Embodiments described relate to adjusting one or more pixel values in a region (90) corresponding to an image artifact (80) based on one or more spatial characteristics and one or more temporal characteristics to reduce or eliminate the image artifact (80). An electronic device (10) may employ spatio-temporal filtering circuitry (110) that includes spatial adjustment circuitry (130), temporal adjustment circuitry (132), and / or fuse circuitry (134). The spatial adjustment circuitry (130) may perform a spatial adjustment of image data in the region (90) of the image frame. Moreover, the temporal adjustment circuitry (132) may perform a temporal adjustment of the image data in the region (90) of the image frame. The fuse circuitry (134) may then merge the spatially adjusted image data and the temporally adjusted image data in the region (90) of the image frame.

The invention belongs to the technical field of robot control, and particularly relates to a multi-modal robot 6D pose estimation system and method based on SAM segmentation and ROS communication. A plug-and-play and high-reliability 6D pose estimation solution is provided for industrial application. The method specifically comprises the following steps: S1, initializing and configuring a system; and S2, performing zero sample object segmentation by using an SAM model, determining at least one target object from a segmentation result, and allocating a unique model index for each target object. S3, initializing a 2D visual tracker, a 6D pose estimator and a time sequence filter; and S4, executing a 6D pose fusion and time sequence filter based on 2D tracking confidence. And S5, evaluating the consistency score of the 6D pose. And S6, monitoring a GPU video memory in real time, and triggering model simplification and processing resolution adaptive adjustment in a grading manner. And S7, converting the 6D pose of each target object to an object coordinate system, and issuing the object coordinate system with the model index to an independent ROS topic.

Embodiments of the present application provide an image denoising method and an image processing device. The method comprises: obtaining a tth input image and a tth reference image; determining a first weight map based on the tth input image and the tth reference image; performing a guided filtering operation on the first weight map with the tth input image as a guide image to generate a second weight map; and performing a temporal filtering operation on the tth input image and the tth reference image based on the second weight map to generate a tth output image.

Embodiments of the present disclosure provide a solution for point cloud coding. A method for point cloud coding is proposed. The method comprises: performing a conversion between a current point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, wherein in accordance with that a region adaptive hierarchical transform (RAHT) is not used as an attribute coding scheme for the current PC sample or an integer Harr transform is used for the RAHT, a first set of indications associated with a temporal filtering process is absent from the bitstream.

In an example, a filter used to denoise shadows for a pixel or pixels can be adapted based on at least a variance in time-accumulated ray-tracing samples. The variance can be used to define a range of filter values for a spatio-temporal filter and to exclude time ray-tracing samples outside of the range. Data used to compute a first moment of a distribution can be used to compute a second moment of the distribution used to compute the variance. For a binary signal, such as visibility, the first moment (e.g., an accumulated average) can be equivalent to the second moment (e.g., a mean square). In a further aspect, spatial filtering of a pixel or pixels can be skipped based on comparing a mean of a variance of the pixel or pixels to one or more thresholds and based on an accumulated number of values for the pixel.

Systems and methods herein are for a video encoder to be associated with a temporal filter and a coding tree and that can perform a main pass for video encoding using individual video blocks towards prediction of at least one frame associated with the media stream, where the coding tree is associated with a lookahead pass, and where the temporal filter can enable denoising within the lookahead pass to reduce an effect of noise in one or more of motion estimation or mode selection of the video encoding.

The application discloses a training method of a single-time spatial-temporal filter based on a periodic characteristic and a discriminant analysis method, and comprises the following steps: collecting original steady-state visual evoked potentials of a subject when a video frequency is f n ; pre-processing the original steady-state visual evoked potentials to obtain a sub-band; dividing the sub-band according to a length to obtain a number of data segments with the length, and the value of the length increases with the increase of the frequency f n ; averaging the data segments to obtain, and connecting multiple to obtain ; expanding and enhancing to obtain ; projecting onto a subspace spanned by an ideal reference signal to obtain ; re-enhancing to obtain ; calculating an inter-class difference matrix and an intra-class difference matrix; calculating a scatter matrix and ; obtaining a projection subspace capable of effectively classifying all classes by using generalized eigenvalue decomposition, and obtaining the spatial-temporal filter according to the projection subspace. The application achieves the technical effect of reducing the training cost of the SSVEP spatial-temporal filter.

A method for rating a medical facility based on complication rates includes determining, for each procedure of a group of procedures, a temporal filter for complications associated with the procedure based on information obtained from a group of data sources, the group data sources including one or more data sources with encrypted patient data. The method also includes identifying a group of patients having performed a same procedure at the medical facility. The method further includes filtering, from the group of patients, complications associated with the group of patients based on the temporal filter to generate a list of filtered complications. The method also includes generating a rating for the medical facility based on a quantity of complications in the list of filtered complications.

This invention provides a spatiotemporal video denoising method based on PID control, comprising: S1. Spatial filtering of the current frame; S2. Spatial filtering of the reference frame; S3. Calculating the spatial filtering weights of the reference frame: calculating the optimal spatial filtering weights of the reference frame using a PID adjustment method; S4. Calculating the temporal filtering weights: calculating the optimal temporal fusion weights of the reference frame using a PID adjustment method; S5. Spatiotemporal fusion to obtain the final processing result. In this method, the PID adjustment method smoothly adjusts the weighted weights of the reference frames in moving and stationary regions, making the transition between the imaging effects in moving and stationary regions smoother and the visual effect more natural. The integral control part of the PID adjustment method can effectively eliminate the problem of inaccurate motion judgment caused by noise, and can effectively avoid the phenomenon of motion blur.