238 results about "Normalization (image processing)" patented technology

The invention belongs to the field of mode recognition, computer vision and image processing, particularly relates to an iris image segmentation and positioning method, system and device based on deeplearning, and aims to solve the problem of low iris recognition precision in a non-controllable scene. The method comprises the steps that a to-be-processed iris image is acquired; four mapping images are generated by adopting a multi-task neural network model, wherein the four mapping images respectively correspond to a pupil center, an iris inner boundary, an iris outer boundary and an iris segmentation mask; the iris segmentation mask mapping graph is processed by adopting threshold segmentation to complete iris segmentation; the pupil center position is predicted according to the geometrical relationship between the pupil center and the iris mask; the mapping graph is de-noised and calculated by utilizing a geometrical relationship among the pupil, the iris and the sclera to obtain iris inner and outer circle parameters and finish iris positioning. According to the method, the iris image acquired in the non-controllable environment can be effectively segmented and positioned, a good foundation is laid for subsequent normalization and recognition, and the iris recognition precision in the non-controllable environment is improved.

The invention relates to the technical field of computer image processing, specifically to a remote-sensing image super-resolution reconstruction method based on a generative adversarial network. Theremote-sensing image super-resolution reconstruction method comprises the following steps: constructing a remote-sensing image super-resolution reconstruction model consisting of a generator network and a discriminator network; introducing a scene constraint sub-network into the generator network to solve the problem of scene change, introducing an edge enhancement sub-network to solve the problemof edge transition smoothness of a generated image, introducing TV loss for noise suppression, and introducing a content fidelity to deal with the problems of instability and gradient disappearance in the training process; and introducing spectrum normalization into a discriminator network to control the performances of a discriminator so as to promoting better learning of the generator. The remote-sensing image super-resolution reconstruction method provided by the invention has the following advantages: a high-quality high-resolution remote-sensing image can be generated based on a low-resolution remote-sensing image; the precision of the low-resolution remote-sensing image in classification detection is effectively improved; the problems of edge transition smoothness and scene change in super-resolution of the remote-sensing image are solved; meanwhile, the problems of training instability and gradient disappearance under the GAN network are solved.

The invention provides a mining electric locomotive passerby monitoring method based on image processing and an alarm system and belongs to the field of safe transportation of coal mines. The system comprises a video collection module, an image processing module and an acousto-optic alarm module, wherein an infrared video camera for video collection is used for collecting images at the front of an electric locomotive; the image processing module comprises image preprocessing, rail identifying and fitting and passerby identifying; the image preprocessing is based on a genetic algorithm, image self-adapting correction combined with a normalization non-complete Beta function and an image binarization method based on a pulse coupling neural network; the rail identifying and fitting is based on fuzzy edge detection fast algorithm with an improved threshold of a genetic algorithm and a heuristic connection method; the passerby identifying is based on a pulse coupling neural network image binarization method realized by virtue of an FPGA (Field Programmable Gate Array); and the acousto-optic alarm module comprises an acousto-optic alarm and a control circuit. The system can be used for effectively identifying passersby at the neighborhood of a rail, judging and pre-alarming the position of the passersby and eliminating the potential safety hazard of the electric locomotive in a transportation process.

The invention discloses a method for enhancing and extracting high stars and other sliding fixed stars in the view field of a telescope. Aiming at the problems that high background stars and other sliding fixed stars are low in detection rate and not high in positioning precision in the celestial positioning process, the astronomy and an image processing technique are combined, the sliding tracks of the fixed stars are calculated by using astronomical information, and reliable enhancement and high-precision extraction of the high stars and other sliding fixed stars are realized by using a background equalization method, a quasi-randomized orbital determination method, a noise normalization method, a double-threshold orbital determination correlation method, a two-step centroid extraction method and other image processing techniques, so that technical support is provided for the celestial positioning. The method has the benefits that the false detection rate and the misdetection rate are extremely low; the influence from interference factors among fixed stars, of other celestial bodies and background noise and the like can be better overcome; weak high stars and other fixed stars are extracted; information about the number, the size, the position, the precision and the like of the fixed stars are synchronously obtained; besides, fewer parameters are set and the set parameters are simple, and real-time processing can be realized.

The invention belongs to the field of light field image processing, and particularly discloses a micro-lens light field camera full-focus image generation algorithm based on a blocking effect. According to the algorithm, fusion is directly carried out based on 4D light field data; when the data fusion rule is established, the focusing degree evaluation is completed by calculating the blocking effect of light field macro pixels formed by light energy integration in different directions at the same spatial position, and finally, the fused 4D light field is integrated to obtain a full-focus image. The effectiveness of the algorithm is demonstrated through experiments. The fusion image obtained based on the angle information and the fusion image obtained based on the space information have thesame visual effect, the perception definition value of the image obtained through the algorithm is the highest from the average quality evaluation comparison result of the fusion image, and the phaseconsistency value and the normalization mutual information value are only second to those of the BF algorithm.

The invention discloses a smiling face identification and encouragement system, and belongs to the technical field of image processing and artificial intelligence. The smiling face identification and encouragement system is implemented through the following steps: collecting basic expression samples; automatically starting videos at startup; collecting the expressions in real time; pre-processing the expression images; identifying the expression images; carrying out comprehensive statistics, analysis and judgment; summing up the phased expressions; and encouraging the staff to relax. According to the technical key point of the invention, a plurality of expression characteristics represented through vectors are matched with corresponding key characteristics in several pre-shot user typical expression samples to compare the similarity; the difference and geometric shape change between the Euclidean distance at a captured expression key characteristic and the Euclidean distance at a preset typical expression characteristic are judged; and after normalization, a statistic shape analysis method is used for judging whether the expression in the phase is anxiety or happiness according to the expression distribution probability of the staff in a certain time period. The system has the benefit of encouraging the staff to feel anxious less and smile more so as to improve the working efficiency and the living quality.

The invention discloses a training method and device for a student model for image processing, and belongs to the technical field of knowledge distillation, and the method comprises the steps: obtaining the parameters of a classification layer in a teacher model which is obtained through the classification training of target objects in a plurality of image samples, initializing parameters of a classification layer in a to-be-trained student model by utilizing the obtained parameters, inputting at least part of the image samples into the student model for classification, and adjusting parameters of a target layer in front of the classification layer in the student model according to a classification loss value of the student model, and enabling the image features of each type of target objects learned by the target layer in the student model to approach the image features of the type of target objects learned by the target layer in the teacher model, and ending the training until it isdetermined that the classification error of the student model is smaller than a set error, wherein the teacher model and the student model each comprise a convolution layer, a classification layer anda normalization layer which are connected in sequence, and the normalization layers of the teacher model and the student model use the same normalization function.

The embodiment of the invention provides an image processing method and device, equipment and a storage medium. The image processing method comprises the following steps: acquiring a first image and asecond image to be processed; splicing the first image and the second image to form a spliced third image; and extracting the content features of the third image and the style features of the secondimage through a pre-trained style conversion network model, performing normalization processing on the content features of the third image according to the style features of the second image to obtainprocessed target image features, and generating and outputting a corresponding target image according to the target image features. According to the embodiment of the invention, the first image and the second image can be spliced; and the content characteristics of the third image and the style characteristics of the second image can be deeply fused, so that the styles of the spliced third imageand the spliced second image are highly unified, and the method is more suitable for images of different styles, can be applied to the field of intelligent mapping, and realizes real-time and rapid intelligent mapping.

The invention discloses a lung parenchyma CT image segmentation method based on a weighted full convolutional neural network, and belongs to the field of medical image processing. The method comprisesthe following steps: selecting a public lung data set for preprocessing, and extracting a lung parenchyma boundary in a labeled image as a semantic category; designing an improved network structure based on a standard full convolutional neural network framework, and establishing an overall structure framework of the pulmonary parenchyma segmentation convolutional neural network by using a principle that a standard path structure for encoding and decoding simultaneously comprises jump connection, expansion convolution and batch normalization; adopting a weighted loss function layer; dividing the data set; carrying out offline model training out to acquire model weight parameters; inputting a test image and outputting a segmentation result by an output layer through layer-by-layer feedforward of a network. According to an existing lung parenchyma segmentation method, a segmentation missing phenomenon is prone to occurring in a focus area in lung parenchyma, and correct segmentation of the focus area in lung parenchyma segmentation can be effectively improved through enhancement processing on important pixels.

A computer implemented method is provided for processing a 3D fetal ultrasound image. A 3D fetal ultrasound image is obtained (either acquired or received from memory), and the spine is detected within the image. This enables a first reference axis to be defined. A second reference axis is defined perpendicular to the first reference axis, and the 3D fetal ultrasound image is updated (e.g. rotated in 3D space) using the first and second reference axes and an up / down (elevation) orientation detection. This provides a normalization of the orientation of the image, so that a machine learning approach is better able to identify landmarks within new images.

The invention relates to a near-infrared vein image processing method for filtering hair noise, which comprises the following steps: 1) collecting a near-infrared vein image and a visible light imageof a part to be imaged, and carrying out size standardization; 2) graying the visible light image; 3) carrying out blocking, and gray normalization and first filtering on each sub-image after blocking; 4) sequentially performing morphological expansion operation and morphological corrosion operation on each sub-image; performing gray value subtraction operation on each subimage and the corresponding subimage; 5) performing binarization and second filtering on each sub-image; 6) obtaining a coordinate set of hair pixel points; and 7) filtering hair noise of the near-infrared vein image. Compared with the prior art, the invention has the advantages that the specific positions of the hair noise pixel points can be given, the hair noise can be processed more effectively and accurately, and meanwhile damage to vein information in the near-infrared image is small.

The invention provides a target detection method and device for CT image data, and electronic equipment, which relate to the technical field of image processing. The method comprises the steps of: obtaining the CT image data, the CT image data comprising target features, performing pixel normalization on the CT image data to obtain a plurality of grayscale images, extracting a grayscale image group from the plurality of grayscale images according to a preset method, and combining the grayscale images into a corresponding pseudo-color image, the grayscale image group being composed of grayscaleimages according to a specified number and a specified sequence, and inputting the pseudo-color image into a pre-trained neural network, so that the pre-trained neural network detects the pseudo-color image to obtain the category and the position of the target feature. Therefore, the accuracy of CT image data target detection can be effectively improved.