5 results about "Color normalization" patented technology

This invention relates to the field of image recognition and detection technology for foodborne pathogens, and discloses a method for identifying Listeria monocytogenes based on image recognition. The identification method includes: acquiring images of chromogenic culture medium plates and converting them to Lab and HSV color spaces; performing adaptive color normalization based on the background region of the culture medium; performing colony instance segmentation on the normalized image and extracting extended regions of interest; extracting saturation distribution sequences along radial rays and detecting halo transition patterns using first-order difference; statistically analyzing halo angle coverage and average transition amplitude, and combining halo quantization feature vectors; inputting the feature vectors into a gradient boosting decision tree classifier to output colony identification results. This invention solves the technical problems of unstable classifier discrimination boundaries caused by color shifts between different batches and the difficulty in detecting halos caused by weak lecithinase reactions.

This invention discloses a method and system for quantitatively assessing the degree of fibrosis in kidney pathological sections, belonging to the field of medical image processing technology. The method includes: performing color normalization and adaptive contrast enhancement preprocessing on Masson stained section images; classifying pixel-level tissue components using a multi-component semantic segmentation network containing a channel-space dual-path attention module; calculating the fibrosis area ratio and outputting a CI score based on the Banff grading standard; generating a fibrosis spatial distribution heatmap; and calculating the annual fibrosis progression rate through longitudinal follow-up comparison.

The present application relates to the technical field of image processing, and discloses a light staining sperm microscopic image light-weight style consistency preprocessing method and system, comprising: acquiring a light staining sperm microscopic image, performing color normalization on the light staining sperm microscopic image according to a deep staining sperm microscopic image, performing local contrast enhancement on the normalized light staining sperm microscopic image according to local contrast of a sperm head, a tail and a boundary region, performing brightness correction on the enhanced light staining sperm microscopic image according to the definition of the sperm head, the middle section and the tail structure and the background stability, and obtaining a light staining sperm microscopic image after light-weight style consistency preprocessing. The present application can solve the problems of staining difference, uneven brightness and boundary blur through image preprocessing without increasing the number of samples and additional training burden, improve the overall quality of the light staining sperm microscopic image, and improve the detection performance of the subsequent model.

The application discloses a drilling stratum AI logging method and system based on machine vision, collects multispectral drilling core image and multi-view ring scanning core surface image and depth identification information, generates a color consistent image group through light-color normalization registration; generates a lithology discrimination feature set through image partition splicing, lithology end member decomposition and grain size classification feature extraction; associates the lithology discrimination feature set with the depth identification information to establish a depth-lithology contrast index, identifies a stratum interface position through compaction compensation and cycle direction analysis, implements feature out-of-limit detection to generate a mutation trigger identification; extracts an interface transition zone feature slice according to the mutation trigger identification, constructs a depth-image analysis profile through multi-sampling point edge trend continuity evaluation and geometric distortion correction; finally, performs lithology partition boundary positioning and lithology classification comparison, and outputs a standardized image logging report, effectively improving the accuracy of lithology interface identification and the standardization degree of logging results.

This invention discloses a method for sorting fresh tea leaves based on a frequency-domain tree-structured topology network, along with a computer device and storage medium. The method encompasses a complete process: image acquisition, preprocessing, frequency-domain decomposition, depth modeling, map construction, and classification. First, image quality is improved through color normalization and edge enhancement. Then, frequency-domain tree-structured decomposition using wavelet transform and discrete cosine transform is performed to extract multi-scale features. Subsequently, long- and short-range dependency modeling and residual convolution modules are integrated to achieve multi-level feature representation. Finally, a tree-structured topology attention path and structure map simulating the bud-leaf-vein relationship are constructed to enhance semantic understanding. A tree-structure-aware classification function is used to achieve fine-grained classification of single buds, one bud and one leaf, one bud and two leaves, and one bud and multiple leaves. During training, cross-entropy loss, data augmentation, and regularization strategies are combined to improve model robustness. This method demonstrates high classification accuracy and stability on multiple tea image datasets, effectively improving the practicality of automatic sorting of fresh tea leaves.