46 results about "Aspect ratio (image)" patented technology

Disclosed is a image processing apparatus and a method for controlling the image processing apparatus. The image processing apparatus according to an embodiment of the present disclosure may identify an object from an acquired image, determine whether the object is hidden by another object by using an aspect ratio of a bounding box of the detected object, and based on the object being hidden, estimate an entire length of the object based on coordinate information of the bounding box. Accordingly, the size information of the hidden object may be efficiently identified while a large amount of database is applied or resources of the apparatus is minimized. The present disclosure may be in connection with a surveillance camera, an automotive driving vehicle, an artificial intelligence module of at least one of a user terminal or a server, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

Systems and methods for image processing (e.g., image extension or image uncropping) using neural networks are described. One or more aspects include obtaining an image (e.g., a source image, a user provided image, etc.) having an initial aspect ratio, and identifying a target aspect ratio (e.g., via user input) that is different from the initial aspect ratio. The image may be positioned in an image frame having the target aspect ratio, where the image frame includes an image region containing the image and one or more extended regions outside the boundaries of the image. An extended image may be generated (e.g., using a generative neural network), where the extended image includes the image in the image region as well as generated image portions in the extended regions and the one or more generated image portions comprise an extension of a scene element depicted in the image.

The invention relates to the technical field of computer image processing, and particularly discloses a multi-image intelligent compression splicing method and splicing system capable of configuring the column number, and the splicing method comprises the following steps: 1, achieving the intelligent compression of images through the iteration quality attenuation, and balancing the image quality and storage space; step 2, dynamically calculating an image layout size based on the number of columns configured by a user; 3, keeping the aspect ratio of the image by adopting a cell centering strategy; and 4, realizing multi-image splicing by a dynamic canvas synthesis technology. The method has the advantages that intelligent image compression is achieved, and mass and storage space are balanced; a compression strategy can be dynamically adjusted according to the number of images; maintaining the original aspect ratio of the image; flexible multi-column image arrangement is supported; and splicing parameters can be dynamically configured.

The invention provides a real-time open vocabulary target detection model AOV-RTDETR for an aerial image, and aims to solve the problems that a traditional detection model cannot recognize unseen categories, small targets are difficult to detect and the real-time performance is insufficient. The model is based on an RT-DETR framework, cross-modal feature alignment of images and texts is achieved in combination with a CLIP visual language model, region-text comparison loss is introduced to break through predefined category limitation, and detection of unseen categories is supported. In order to improve the detection capability of a small target and a high aspect ratio target, a large strip-shaped convolution structure is provided to enhance the feature modeling capability, and a multi-scale spatial pyramid attention encoder and a text-guided multi-stage fusion decoder are designed to improve the feature expression and semantic alignment capability of the small target. The method has high precision and real-time balance capability, and is suitable for application scenes with high requirements for target detection precision and speed, such as aerial remote sensing, emergency rescue, unmanned aerial vehicle patrol and the like.

The invention relates to a Badam sorting method and device based on an X-ray image, and the method comprises the steps: obtaining an original X-ray image, and carrying out the preprocessing of the original X-ray image; processing the preprocessed X-ray image to obtain an image to be sorted; a density defect fusion descriptor, a penetrating empty shell recognition descriptor, a structure and texture stability descriptor and a special-shaped impurity recognition descriptor are extracted from each to-be-sorted image, and the density defect fusion descriptor fuses a gray average value, a gray standard deviation and a low-gray connected region proportion; the penetrable empty shell recognition descriptor is fused with density gradient and packing ratio, the structure and texture stability descriptor is fused with contour area and roundness, and the special-shaped impurity recognition descriptor is fused with length-width ratio, pixel energy and local texture ambiguity; and obtaining a sorting result. Compared with the prior art, the method has the advantages of high classification precision and the like.

Methods of particle shape and size characterization of powder particles include imaging the particles at a pixel-scale resolution to acquire images of the particles in which perimeter irregularities appear, increasing the resolution of the images and then performing fine-graining analysis on the images to accentuate at least some of the perimeter irregularities, calculating aspect ratios and elliptical form factors of the particles from the images; and determining the perimeter irregularities of the particles and elongation of the particles by mapping particles on an aspect ratio versus elliptical form factor plane to decouple the perimeter irregularities of the particles from the elongation of the particles and yield a statistical description of shapes of the particles.

To determine a projection area of an image on a projection surface so as to effectively use a drawing area of a panel of a projector.SOLUTION: A determining method of a projection area PAR, includes steps of: specifying coordinates of four vertices P10s, P11s, P12s, and P13s, which correspond one-to-one to four vertices P10p, P11p, P12p, and P13p of a rectangle RE10p defining a drawing area DAR1 in a panel PL, in a screen coordinate system ΣS of a projection surface SC; identifying a first intersection point coordinate which is a coordinate in the screen coordinate system ΣS of a first intersection point PI1s at which two diagonal lines of a quadrangle QU10s having vertices P10s, P11s, P12s, and P13s as vertices intersect with each other; identifying a largest rectangle which falls within a range of the quadrangle QU10s and has a first aspect ratio centered on the first intersection point coordinate as a first rectangle RE1s; and determining a projection area PAR on the projection surface SC based on the first rectangle RE1s.SELECTED DRAWING: Figure 11

The invention relates to a feature fusion method based on a special-shaped convolution kernel, and aims to solve the problem that traditional feature fusion is poor in adaptability to object direction and length-width ratio change. Comprising the following steps: extracting input image features; multiple special-shaped convolution kernels are adopted to extract multi-direction features in parallel; splicing the feature maps along the height dimension; and fusing multi-scale information through expansion convolution, and adding the fused multi-scale information with original feature residual errors. According to the feature fusion method, the detection precision of targets with changeable directions and large length-width ratio difference is remarkably improved, mAPs on SIXray and CLCXray data sets are improved by 2.22% and 1.84% respectively, and the feature fusion method is suitable for target detection of complex scenes such as security check and remote sensing.

Problem: high-precision detection of impersonation of a person using a planar object such as a photograph. Solution: imaging means 11 that images a plurality of frames of the verification target at different angles; feature detection means 14 that detects the aspect ratio of the face of the verification target, i.e., the ratio of the vertical length to the horizontal length, from each of the plurality of frames of images imaged by the imaging means 11; and impersonation detection means 14 that detects impersonation of a person using a planar object based on the aspect ratio of the face of the verification target imaged at different angles.

This disclosure provides a character recognition method, apparatus, electronic device, and storage medium. The character recognition method includes: acquiring an image of a target character to be tested, wherein the target image includes at least one character to be tested; extracting and determining the grayscale value and aspect ratio of each character to be tested based on the image of the target character to be tested; extracting and determining the directional gradient, geometric invariant moments, and gray-level co-occurrence matrix of each character to be tested based on the grayscale value; determining the geometric features of each character to be tested based on the aspect ratio; determining the edge features of each character to be tested based on the directional gradient and the geometric invariant moments; determining the texture features of each character to be tested based on the gray-level co-occurrence matrix; inputting the geometric features, the edge features, and the texture features into a character recognition model; and outputting the recognition result of each character to be tested through the character recognition model.

The present invention relates to a body fluid test method, comprising: a step for identifying the ROI region of each sperm in each of n set image frames; reducing the image brightness of each of the n image frames by a set value; a step of creating an accumulated image frame in which the images of each sperm in the n image frames are accumulated; a step of confirming a movement path region of each sperm in the accumulated image frame; for each sperm, calculating the width of the moving path area, and judging the activity according to the calculated width; and calculating the aspect ratio of a rectangle surrounding the movement path region for each sperm, and determining the straightness according to the calculated aspect ratio.

The disclosed approach performs image compression using a variational auto-encoder that is capable of greater compression than conventional approaches while maintaining high fidelity of the decompressed image. Examples persist a bottleneck layer output of a variational auto-encoder into a compressed image in the form of potential tensors. The potential tensor is decompressed into a restored image in the pixel space by a variational self-decoder. In some examples, different encoder / decoder pairs are trained on a particular image type based on feature attributes. For example, maps have lines that are narrower compared to their length (e.g., with high aspect ratios), which differ from features within photographs of persons and scenes. Some examples utilize contrast language image pre-training (CLIP) and / or bootstrap language image pre-training (BLIP) models to store inserts, each of which is associated with a compressed image to enable a natural language search of a set of compressed images without requiring decompression.

The invention discloses a network-based rail B-display image recognition method, which belongs to the field of railway track flaw detection and deep learning, and comprises the following steps: 1, acquiring a rail B-display image, dividing the B-display image into five types of labels, namely a rail end label, a rail head nuclear flaw label, a normal screw hole label, an abnormal screw hole label and a lead hole label, and constructing a data set after performing data enhancement on small sample types; 2, a data set is put into the model for training, an attention module is embedded into the model, the sensing ability of key features is enhanced through channel re-calibration, and the detection precision of small target damage is improved; and 3, aiming at a thin and long target with an extreme aspect ratio, an EIoU loss function is adopted to optimize bounding box regression, and the positioning precision and the convergence speed of the model in a complex form are remarkably improved. According to the invention, fine defects such as rail head nuclear injury, abnormal screw holes and the like can be effectively identified, the omission ratio and the false detection ratio are reduced, intelligent identification and automatic analysis of B-display images are realized, the manual dependence is greatly reduced, and the detection efficiency and the railway operation safety are improved.

Aspects of the technology described herein identify geologic features within seismic data using modern computer analysis. An initial step is the development of training data for the machine classifier. The training data comprises an image of seismic data paired with a label identifying points of interest that the classifier should identify within raw data. Once the training data is generated, a classifier can be trained to identify areas of interest in unlabeled seismic images. The classifier can take the form of a deep neural network, such as a U-net. Aspects of the technology described herein utilize a deep neural network architecture that is optimized to detect broad and flat features in seismic images that may go undetected by typical neural networks in use. The architecture can include a group of layers that perform aspect ratio compression and simultaneous comparison of images across multiple aspect ratio scales.

The present application relates to the field of image processing technology, and specifically to an olive leaf profile analysis method and system based on image processing. First, a standard model for olive leaf profile analysis is constructed, and then a comparison grayscale image of the leaf to be analyzed is obtained, and a leaf profile diagram of the leaf to be analyzed is separated from the image; then, the actual aspect ratio, actual symmetry, and actual circularity of the leaf to be analyzed are calculated from the leaf profile diagram, and a weighted judgment index is generated based on the actual aspect ratio, actual symmetry, and actual circularity. Finally, a comparison analysis result of the leaf to be analyzed is output based on the weighted judgment index and the standard model. The present application realizes automatic sampling and automatic recognition of olive leaves, which effectively reduces the workload of staff and can effectively improve analysis efficiency. Compared with traditional manual analysis, the present application adopts a unified standard for analysis, which can effectively avoid the influence of the subjective feelings of technicians on the analysis and improve the objectivity of the analysis.

The invention relates to the technical field of image enhancement, in particular to a blade scratch recognition method based on image enhancement, which comprises the following steps of: defining a texture sliding window with the size of N * N according to a to-be-detected blade grayscale image, and controlling the texture sliding window to carry out pixel-by-pixel traversal on a pixel coordinate system of the to-be-detected blade grayscale image, and calculating and generating a local entropy value. According to the method, the fused matrix is processed through morphological closed operation, the discontinuous high-response area is connected through the structural elements, internal holes are filled, the scratch fracture phenomenon caused by uneven illumination or metal reflection is repaired, and the completeness and continuity of the defect form are ensured. And performing connected domain marking based on a binary mask, extracting geometric parameters of an external rectangle, strictly screening the connected domains according to a length-width ratio form standard, and removing non-strip interference noise such as spots and pits, so that high-precision positioning of fine scratches on the surface of the blade is realized.

This invention relates to a method and apparatus for sorting almonds based on X-ray images. The method includes: acquiring original X-ray images and preprocessing them; processing the preprocessed X-ray images to obtain images to be sorted; extracting density defect fusion descriptors, penetrating shell identification descriptors, structure and texture stability descriptors, and irregular impurity identification descriptors from each image to be sorted. The density defect fusion descriptor incorporates the mean gray level, gray level standard deviation, and proportion of low-gray-level connected regions; the penetrating shell identification descriptor incorporates density gradient and fill ratio; the structure and texture stability descriptor incorporates contour area and roundness; and the irregular impurity identification descriptor incorporates aspect ratio, pixel energy, and local texture ambiguity. The sorting result is then obtained. Compared with existing technologies, this invention has the advantages of high classification accuracy.

This invention relates to a method and apparatus for object detection and determining training samples for object detection. The method, when dividing positive and negative samples, determines positive samples of target boxes based on the aspect ratio of the target boxes in the training image. Furthermore, when the aspect ratio of a target box is large, a first preset threshold inversely proportional to the aspect ratio is set. This results in a smaller first preset threshold when the aspect ratio of the target box is large. Using the intersection-union ratio (IUU) of candidate boxes and target boxes, along with the first preset threshold, can also effectively determine whether a candidate box is a positive sample, avoiding missed detections. This lays the foundation for training an object detection model based on positive and negative samples of target boxes in the training image. The trained object detection model can better learn the position and feature information of the object to be detected, reducing the missed detection rate of objects with large aspect ratios, thereby improving the accuracy of object detection.

A system and a method for 3D surface imaging of a 3D object, the method comprising directing a light beam to a digital micromirror device that displays binary fringe patterns, converting the binary fringe patterns to grayscale fringe patterns at an intermediate image plane; rotating the grayscale fringe patterns onto the intermediate image plane to match an aspect ratio of the object, projecting the greyscale fringe patterns from the intermediate image plane to the object, capturing deformed structure images reflected by the object; and transferring the captured images to a computer connected to a frame grabber.

The invention discloses a multi-plane millimeter wave image target frame fusion method and system. The method comprises the following steps: firstly, grouping mapping results of a plurality of millimeter wave images on the same surface, and then calculating fusion scores of all target frames in groups; and fusing the target frame of each group mapping result based on the fusion score. And finally obtaining the current-round fusion result of each group, replacing the'mapping result 'for grouping, and recalculating the fusion score to carry out the next-round fusion operation. And repeating the steps until a final unique fusion result is obtained, and mapping the final unique fusion result on the standard doll graph for display. According to the method, three indexes of intersection-to-union ratio score, width-to-height ratio score and area ratio score in the vertical direction are designed, and the three scores are comprehensively considered to perform target frame fusion. By applying the method, the accuracy of the target mapping result can be effectively improved, and then the visual presentation effect is remarkably improved.

The application discloses an aragonite whisker preparation method and system based on image recognition and multi-model prediction, and comprises the following steps: in each sampling period, a microscopic image of reaction slurry is subjected to mask extraction, skeleton thinning, cross point breaking and ellipse fitting processing, an average observation vector of crystal size is decoupled and output, a 2D-PBM is input with estimated liquid phase ion concentration, a two-dimensional growth kinetics model is combined, and a posterior state vector at the current moment is obtained; an ESN proxy model is used to replace the 2D-PBM, and the crystal morphology evolution trajectory in the future time domain is deduced within the current sampling period; air inlet component fluctuation is fed forwardly compensated, and adjustment instructions for temperature and air inlet flow are output and executed. The application realizes direct and real-time closed-loop control on the aspect ratio of acicular crystals, suppresses morphology degradation caused by impurity fluctuation, and improves product quality and aragonite phase purity.

The embodiment of the invention relates to the technical field of image processing, in particular to a sample balancing method suitable for small target detection, and the method comprises the steps: obtaining a plurality of anchor frames and truth value bounding boxes for a target object in a feature map of a target image, the scale parameters and aspect ratio parameters of different anchor frames being different, and the truth value bounding boxes being different; pixel points occupied by the target object in the target image are smaller than a preset value; based on the position coordinates of the truth value bounding box and the position coordinates of the anchor frames, respectively calculating detection quantities between the truth value bounding box and the anchor frames; wherein the detection quantity is used for measuring the similarity degree between the anchor frame and the true value bounding box; dividing the plurality of anchor frames into a positive training sample, a negative training sample and an invalid sample based on the detection quantity between the true value bounding box and the plurality of anchor frames; and outputting a positive training sample and a negative training sample, wherein the positive training sample and the negative training sample are used for subsequent classification operation and regression operation of the target object. According to the method, the small target detection capability can be greatly improved.

A procedure for detecting vehicle damage on a target vehicle, comprising the following: Subdivide (S101) the overall appearance of the target vehicle into N predefined blocks, where N is a positive integer; Performing an image capture (S102) to obtain original images; Performing vehicle part recognition (S103) for each of the original images to obtain a vehicle part position recognition result; Performing a damage assessment (S105) for each of the original images to obtain a damage assessment result; and Merging (S106) the vehicle part position detection result with the damage detection result to obtain a vehicle part damage result for the target vehicle, characterized by the fact that an image acquisition is performed for each of the N blocks according to a predefined image acquisition model (S102) to obtain N original images corresponding to the N blocks; wherein the image acquisition model further comprises: performing an image acquisition for the N blocks of the target vehicle, each from a preset recording angle, in order to obtain N original images, each with an aspect ratio a:b, a vehicle part recognition is performed for each of the N original images (S103), Each of the N original images is cropped into M sub-images of a predefined size according to a predefined cropping model (S104), where M is a positive integer; The cropping model includes the following: Dividing each of the N original images horizontally into a equal parts and vertically into b equal parts to obtain a × b sub-images, where a × b = M, and A damage detection (S105) is performed for each of the N original images and its corresponding M sub-images.

The present invention discloses a document table detection method and device, comprising: extracting abstract semantic features of a table image to be detected based on a rectangular convolution kernel in a Table Residual Block, so that the extracted semantic features are more complete; fusing the abstract semantic features based on Bottom-up Path Augmentation and Advanced Image Format (ASFF); effectively fusing multi-scale features to obtain multi-scale fusion features, wherein the ASFF adaptive fusion method is used to increase the flexibility of the fusion; performing K-means clustering on the aspect ratios of table frames in a training set, determining anchor types based on the clustering results, applying the anchor types to the multi-scale fusion features in a head, obtaining each target table frame in the table image to be abstracted, and improving the accuracy of the target table frame.

Systems and methods for optimizing single shot detector (SSD) for object detection are disclosed herein. A system receives an image of a plurality of objects. Further, the system determines a plurality of feature layers and a plurality of feature cell sizes corresponding to the received image, based on an aspect ratio of the received image. Furthermore, the system determines aspect ratio of anchor boxes from trained model file, based on aspect ratio of anchor boxes, position and number of anchor boxes to be tiled in each feature cell of the plurality of feature layers. The size of the one or more anchor boxes corresponds to an anchor box aspect ratio. Additionally, the system assigns the one or more anchor boxes as a horizontal tile or a vertical tile in each feature cell, when the anchor box aspect ratio is less than a first pre-defined threshold value and greater than a second pre-defined threshold value, respectively. Further, the system generates one or more feature maps using an object detection model and a neural network (NN) model. The one or more feature maps comprises one or more feature map tensors. Furthermore, the system generates, for each layer of the one or more feature maps, a prediction tensor of predefined dimension from the one or more feature map tensors, using a prediction convolution layer. Additionally, the system detects and classifies the plurality of objects, based on the generated prediction tensor.

The invention relates to a chip surface defect quantitative characterization method, a chip surface defect quantitative characterization device and computer equipment. The method comprises the following steps: extracting features of a defect image of chip surface defects to obtain current defect features, and performing image processing on the defect image to obtain an initial binary image corresponding to the defect image; identifying current defect features according to preset defect features to obtain defect types of chip surface defects, and if the defect types are scratch defects, performing skeleton extraction on the initial binary image to obtain an image skeleton of the defect image; determining image size data of the skeleton based on the plurality of skeleton pixel points and the initial binary image; performing area calculation on a scratch area in the initial binary image to obtain an image length-width ratio of the scratch area; and determining a quantitative characterization result of the chip surface defect based on the image size data of the skeleton and the length-width ratio of the scratch area. By adopting the method, the defect image can be quantitatively represented so as to determine information such as size and / or area.

A method for determining a projection region including identifying the coordinates that are in a screen coordinate system, for each of four vertices corresponding in a one-to-one relationship to four vertices of a rectangle that defines a drawing region of a panel, the screen coordinate system of a projection surface; identifying first intersection coordinates that are in the screen coordinate system and that are coordinates of a first intersection where the two diagonals of a quadrangle corresponding to the first quadrangle; identifying as a first rectangle a largest rectangle that falls within the quadrangle, has a center at the first intersection coordinates, and has a first aspect ratio; and determining the projection region of the projection surface based on the first rectangle.

[Problem] To accurately detect spoofing of a person that is based on a photograph or other planar objects. [Solution] Involving the following: an imaging means 11 for capturing images in a plurality of frames of a subject to be authenticated from different angles; a feature detection means 14 for detecting an aspect ratio, which is the ratio between the vertical length and horizontal length of the face of the subject, from each of the frame images captured by the imaging means 11; and a spoofing detection means 14 for, on the basis of the aspect ratio of the face of the subject captured from different angles, detecting spoofing of a person based on a planar object.

This invention provides a method for rapidly testing the two-dimensional shape characteristics of aggregates, relating to the field of civil engineering materials. The method includes: sieving to obtain a sufficient quantity of aggregates with a specified particle size; acquiring image information of the aggregates using an image acquisition device; processing the aggregate images using grayscale conversion, threshold segmentation, and binarization methods to facilitate subsequent calculations; obtaining the corresponding isoellipse information of the aggregate images by calculating the covariance matrix, further obtaining information such as the aspect ratio of the isoellipses; and calculating the perimeter and area of ​​polygons using contour edge recognition methods and Green's formula to obtain roundness values. This method can not only process aggregate information in large batches simultaneously to obtain the key two-dimensional shape characteristics of the aggregates in that batch, but also has advantages such as simple operation, short testing cycle, and accurate results, providing an efficient and convenient technical means for analyzing the impact of aggregate morphology on the performance of cement concrete and asphalt concrete.

The invention provides a face frame generation method and device, electronic equipment and a storage medium, and relates to the technical field of image processing. The face frame generation method comprises the following steps: acquiring first coordinate data of a face region in an original image; determining a target image according to a first aspect ratio of the original image and a second aspect ratio of a canvas in the display device, and determining a scaling ratio and a first offset of the target image; after the target image is filled into the canvas according to the scaling and the first offset, determining second coordinate data of the face area in the canvas according to the first coordinate data of the face area and the scaling and the first offset of the target image so as to generate a face frame; and in response to the calibration instruction, calibrating the face frame, determining a calibration displacement of the face frame, and determining third coordinate data of the face region in the original image in combination with the first coordinate data of the face region and the scaling of the target image. According to the invention, the accuracy of marking the face in the image by the face frame is improved.