121 results about "Gabor filter" patented technology

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention discloses a metal surface defect detection and classification method, system, equipment and medium, and the method comprises the steps: processing images of different defect types through gray normalization, adaptive histogram equalization, a Gabor filter, a maximum entropy algorithm, morphological processing and an improved local binary pattern; carrying out defect position and size calculation on the processed image, and carrying out GUI (Graphical User Interface) measurement page design; the system, the equipment and the medium are used for realizing the metal surface defect detection and classification method. According to the method, the image processing effect is remarkable, the image contrast is improved, the defect boundary identification is enhanced, the accurate segmentation of the image and the detailed optimization of the structure are realized, the image features are accurately extracted, and efficient classification training is supported; the method has the advantages of improving image quality, enhancing defect detection precision and optimizing feature extraction and analysis, and can be widely applied to the field of high-precision image analysis such as metal defect detection, industrial quality control and computer vision.

The invention discloses an intelligent identification system for an osteoporosis area, belongs to the field of image processing calculation, and aims to solve the problems of limited technical coverage and lack of a dynamic optimization mechanism. In a feature extraction stage, a system combines traditional image processing and deep learning technologies in parallel, extracts bone trabecula multidirectional texture features by using a Gabor filter and a local binary pattern algorithm, analyzes bone contour curvature by combining Sobel edge detection and morphological operation, constructs geometric morphological parameters, and performs feature extraction on the bone trabecula. The local branch focuses on the porosity and arrangement rule of the bone trabecula by adopting a 3D convolutional network, the global branch is embedded into a compression excitation module based on an improved MobileNetV3 network to strengthen the overall morphological expression of the bone, the response intensity of a local microstructure is enhanced by space attention, the weight of global bone topological characteristics is calibrated by channel attention, and a multi-scale splicing strategy is combined, so that the overall morphological expression of the bone trabecula is optimized. And finally, outputting a fusion feature matrix after noise suppression, and remarkably improving the expression ability of pathological features.

The invention relates to the field of quality detection, in particular to an alloy casting quality detection method and system based on machine vision, and the method comprises the steps: obtaining an alloy casting surface image, setting a window, and moving the window along the alloy casting surface image to obtain a plurality of window regions; and for each window area, calculating the optimal detection wavelength in the Gabor filter, extracting a texture image in the window area by using the Gabor filter, and inputting the texture image into a preset detection model to obtain the defect type of the surface of the alloy casting. The method can better adapt to the characteristics of different surface defects, can extract clearer and more accurate texture characteristics, and further improves the detection precision of the surface quality of the alloy casting.

The invention relates to an unmanned aerial vehicle autonomous inspection method based on an AI technology, and the method comprises the following steps: S1, capturing a high-resolution image through an airborne camera in an inspection process, carrying out the spatial calibration of the image through IMU and LiDAR data, and guaranteeing the precise correspondence between the captured image data and a physical environment; s2, extracting texture features in the image by using a Gabor filter, acquiring edge features in the image by using a Canny edge detection algorithm, and detecting a linear structure by using Hough transform to assist in identifying a lead; s3, an improved DeepLab model is adopted to segment a lead area; s4, performing real-time tracking on the identified lead position by using a Kalman filter, and introducing a dynamic Bayesian network DBN to perform short-term change prediction on the lead position; and S5, generating a map of the current environment based on SLAM, and optimizing the inspection path of the unmanned aerial vehicle based on an RRT improved algorithm. Accurate wire inspection is achieved, it is ensured that operation is conducted along a safe route of a wire, efficiency is improved, and the risk of inspection is reduced.

The invention belongs to the technical field of image processing, and particularly relates to a screw fastening quality evaluation method and system based on image features, and the method comprises the steps: collecting a screw fastening image, and obtaining a complex response diagram through a Log-Gabor filter; obtaining a phase congruency diagram according to each complex response diagram; acquiring gradient directions of pixel points in the phase consistency graph, and constructing an accumulator graph according to the gradient directions and offset points in a preset radius range; according to salient points in the accumulator graph and local signal-to-noise ratios of the salient points, screw positioning points are determined through two-dimensional quadratic polynomial function fitting; the torque value of the screw is obtained according to the screw positioning point driving torque measuring device, and whether the screw is fastened or not is judged. According to the method, the screw is positioned by using phase consistency in the frequency domain, so that the interference of specular reflection and extreme shadow on positioning is overcome, the accuracy of screw positioning is improved, and screw fastening quality evaluation is assisted.

The invention relates to a bearing unbalanced sample fault diagnosis method based on dynamic modeling and a causal interpretable GAN, and belongs to the technical field of mechanical intelligent detection. Aiming at the problems of low model diagnosis precision caused by data imbalance in the prior art and deficient physical significance and poor interpretability of existing GAN network generation samples, the invention provides a technical scheme of fusing dynamic modeling and an interpretable generative adversarial network. The method comprises the following steps: generating a fault response signal with physical significance through a two-degree-of-freedom dynamic model; constructing a GabcauACGAN network, combining a causal loss function to restrain the relevance between the generated features and fault tags, and extracting time-frequency features by adopting a learnable Gabor filtering layer. The method has the technical effects that the time-frequency similarity SSIM of a generated fault sample and a real signal reaches 99%, and the diagnosis accuracy is improved to 98% or above.

The invention relates to a bridge structure modal parameter identification method, which is based on a bridge structure tiny vibration video acquired by a camera. VMD (variational mode decomposition), PBVM (phase-based video motion amplification), Gabor filtering-based phase extraction method GBP (Gabor filtering), an improved clustering algorithm ICA (independent clustering algorithm) and an integrated covariance-driven random subspace recognition (SSI-Cov) and FDD (frequency domain decomposition) algorithm are fused to construct a set of complete non-contact structural modal parameter recognition system. According to the method, effective decoupling of multiple vibration modes of a bridge structure, frequency domain feature extraction of small-amplitude vibration and intelligent and automatic modal parameter extraction are achieved under the conditions that the target frequency bandwidth does not need to be known in advance, environmental noise interference exists and no obvious feature target exists, and the method belongs to the technical field of bridge structure health monitoring.

A computer implemented method of classifying brain activity signals includes: receiving, as input to a neural network, input data comprising a plurality of brain activity signals; applying a first block to the input data to generate a plurality of first order wavelet scalograms, wherein the first convolutional block is configured to apply a plurality of Gabor filters to each of the plurality of brain activity signals, wherein each Gabor filter is associated with a learned bandwidth and learned frequency; applying one or more further blocks to the plurality of first order wavelet scalograms to generate a plurality of feature maps, wherein each further block comprises one or more convolutional layers; and applying a classification block to the plurality of feature maps, wherein the classification block is configured to generate one or more classifications of the plurality of brain activity signals from the plurality of feature maps.

The invention relates to the technical field of image processing, in particular to a knitted fabric defect detection method and system based on a projection method.The method comprises the steps that firstly, a surface image of a knitted fabric is collected, and the surface image is preprocessed into a first image; if the first image is a non-jacquard fabric image, directly taking the first image as a second image, and if the first image is a jacquard fabric image, extracting texture features by using Gabor filtering to separate high-frequency and low-frequency regions, respectively performing diffusion processing on the high-frequency and low-frequency regions by using a PM model, and combining the high-frequency and low-frequency regions into the second image; projecting the second image in multiple directions, generating gray integral curves of different angles, calculating a first-order derivative of the gray integral curves, identifying a potential defect area through mutation points, and locally amplifying the potential defect area into a third image; a YOLOV7 model is used for detecting defects, calculating surface defect values, collecting the content ratio, the physical strength and the chemical content of the fabric, calculating score values respectively, carrying out weighted summation on the score values and the surface defect values, generating a quality evaluation result, and improving the detection precision of the knitted fabric through multi-dimensional characteristic analysis.

The invention discloses a space-time image velocity measurement method and system based on multi-direction Gabor filtering, and the method comprises the steps: carrying out the filtering processing of a space-time image through Gabor filters in multiple directions, so as to enhance the texture features in multiple directions, carrying out the image quality evaluation of a Gabor filtering image in each direction, and obtaining the velocity measurement of the space-time image. The filtering direction with the best textural feature enhancement effect is screened out; a clustering point set with the highest comprehensive evaluation score is screened out through a two-layer evaluation screening mode of clustering quality evaluation and line segment quality evaluation, straight line fitting is carried out based on the clustering point set, an optimal flow velocity line can be obtained, and the river surface flow velocity is obtained through calculation; according to the method, a three-layer evaluation system composed of filtering direction evaluation, clustering quality evaluation and line segment quality evaluation is combined, a high-quality, correct and effective flow velocity line can be accurately extracted from a space-time image, and the accuracy and robustness of a flow velocity measurement result are greatly improved.

The invention provides a few-sample steganalysis method based on feature enhancement and sample expansion, and the method combines a few-sample learning theory, enhances the diversity of feature space, and uses the generated pseudo steganalysis sample to finely adjust the pre-training model, thereby improving the training effect and detection precision of the steganalysis model. Specifically, firstly, high-frequency noise features of an image are extracted through a spatial domain rich model and a 2D Gabor filter, and a steganographic feature map with the most representative is screened through normalization feature saliency measurement; thirdly, generating a steganography feature prototype by adopting a variational auto-encoder VAE, and generating a large number of pseudo-steganography samples through methods of sampling, threshold segmentation and the like, so as to enhance the diversity of training samples; and finally, in combination with the real steganography sample and the pseudo steganography sample, carrying out hierarchical training on the pre-trained steganography analysis model, and gradually optimizing the model performance. The method is suitable for various steganography analysis networks, and steganography images generated by different steganography algorithms can be effectively detected.

The invention belongs to the technical field of image analysis and processing, and relates to an automobile circuit board solder paste printing quality detection method and system. The method comprises the steps of constructing a Laplacian pyramid for a target solder paste area image, extracting multi-level geometric and position features, performing weighting based on local phase consistency to obtain a first fusion feature, and comparing the first fusion feature with a first fusion feature of a historical qualified sample to obtain a second fusion feature; calculating a time sequence drift correction characteristic capable of correcting the drift in the production process; constructing a neighborhood feature map by combining the first fusion features of the target region and the spatial neighborhood of the target region, and extracting spatial morphological features by using an orthogonal Gabor filter; and performing vector splicing on the first fusion feature, the time sequence drift correction feature and the spatial form feature to generate a comprehensive combined feature, and inputting the comprehensive combined feature into a predetermined classification model to obtain a printing quality detection result. According to the invention, the detection rate of the solder paste area defect on the automobile circuit board can be improved.

The invention discloses a micro-scratch filtering method and device in wafer defect detection, equipment and a storage medium, and the method comprises the steps: extracting the frequency spectrum characteristics of a wafer image, carrying out the direction characteristic analysis based on the frequency spectrum characteristics, and carrying out the calculation to obtain the center frequency of a target region suspected to have micro-scratches; constructing a Gabor filter according to the center frequency, and reinforcing the target area by using the Gabor filter; respectively carrying out gray integration along the direction vector and the vertical direction vector of the linear defect of the strengthened target area, and calculating the strength attribute of the target area; and when the strength attribute is higher than a preset threshold value, marking the defect of the target area as a micro-scratch and filtering out the micro-scratch. According to the method, the micro scratches in the image can be accurately positioned and filtered out, and the influence of the micro scratches on the accuracy of wafer defect detection is prevented.

Systems and methods are provided for performing optical coherence tomography (OCT) angiography for the rapid generation of en-face images. According to one example embodiment, differential interferograms obtained using a spectral domain or swept source OCT system are convolved with a Gabor filter, where the Gabor filter is computed according to an estimated surface depth of the tissue surface. The Gabor-convolved differential interferogram is processed to produce an en-face image, without requiring the performing of a fast Fourier transform and k-space resampling. In another example embodiment, two interferograms are separately convolved with a Gabor filter, and the amplitudes of the Gabor-convolved interferograms are subtracted to generate a differential Gabor-convolved interferogram amplitude frame, which is further processed to generate an en-face image without performing a fast Fourier transform and k-space resampling. The example OCTA methods disclosed herein are shown to achieve faster data processing speeds compared to conventional OCTA algorithms.

The invention discloses an outdoor dot matrix display screen picture flaw detection method, device and system based on texture features and a server. According to the method, at least two pure color test pictures are obtained, and defect full-coverage detection is realized by utilizing sensitivity differences of different color channels; besides, multi-scale feature extraction is carried out based on a Gabor filter, so that scale features such as single-point dead pixels, regional dark spots and module-level distortion can be captured, and multiple types of flaws such as brightness deviation and large-area uneven brightness can be covered. According to the method, the detection rate of traditional leak detection scenes such as small dead pixels and color deviation is increased to 95% or above, the environment robustness is high, the false detection rate in the complex illumination and vibration environment is low, and a high-reliability automatic defect detection solution is provided for the field of outdoor advertisements and public display.

The present invention provides a B-ultrasound image deep learning texture analysis system, which relates to the field of medical image analysis technology. It combines the ResNet-50 model and multi-dimensional texture extraction, and realizes the effective fusion of low-level and high-level texture features by introducing Gabor filters and cross-layer information transmission; by capturing and analyzing the multi-dimensional texture of the B-ultrasound image, a comprehensive feature map is generated, and the system uses the Dropout layer to enhance the generalization ability of the ResNet-50 model, and further improves the accuracy of the analysis through feature fusion and weight learning, and the final output and decision support module can generate more accurate analysis results. The system is applicable to different types of B-ultrasound images, and effectively improves the accuracy and robustness of medical diagnosis.

The invention discloses a sandstone slice image component identification method based on a GL-SLIC algorithm and semi-supervised learning. The method comprises the following steps: firstly, acquiring a sandstone slice image amplified by 200 times under single polarization, converting an RGB image into a CIE-Lab color space, and extracting a GLBP texture feature vector; then a GL-SLIC superpixel segmentation algorithm is constructed, and adaptive segmentation based on texture and color features is realized in combination with a Gabor filter and a local binary pattern; then implementing a region merging algorithm to generate complete mineral particles and pore fragments; finally, a classifier based on VGG16 and a discriminator based on ResNet18 are constructed, a semi-supervised self-training framework is adopted, a model is initialized by using about 6% of manual labeling samples, a high-confidence-coefficient pseudo-label extension training data set is generated through iteration, and automatic recognition of sandstone components such as quartz, pores, kaolinite, rock debris and a matrix is achieved. According to the method, the recognition accuracy of 96.3% on a test set is achieved, compared with a traditional method, the segmentation precision and the recognition accuracy are remarkably improved, the data labeling cost is greatly reduced, and an efficient technical scheme is provided for automatic analysis of geological images.

The invention belongs to the technical field of image data processing, and particularly relates to a large shaft forging blank center forging method and system based on machine vision, and the method comprises the steps: carrying out the preprocessing of a high-temperature steel ingot image; carrying out convolution on the image by adopting a logarithm-Gabor filter bank to obtain complex response and local direction energy; for each pixel point, obtaining the structural significance of the pixel point based on the sum of the energy in the real part, the imaginary part and the local direction of the complex response; obtaining the direction certainty based on the local direction energy and the direction vector; obtaining a final voting weight by combining the structure significance and the direction certainty; accumulative voting is carried out based on the weight, the geometric axis of the steel ingot is calculated, and an anvil of the forging press is aligned with the geometric axis of the steel ingot. According to the method, weighted voting is carried out by combining the authenticity and directivity of the pixel points, strong interference such as water vapor and oxide skin is effectively restrained, and stable and accurate positioning of the steel ingot axis under the complex working condition is achieved.

The invention discloses a bicycle chain wheel surface spraying quality detection method, which is characterized in that a target area of an image is rapidly and accurately detected through a YOLOv3 model, and then texture feature information of a bicycle chain wheel spraying surface is extracted by using a Gabor filter. The method can effectively distinguish surface features of different particles and capture fine texture changes. And finally, inputting the extracted texture features into a support vector machine (SVM) classification model for training and classification, thereby realizing accurate evaluation of the spraying quality. Compared with a traditional manual detection method, the method has the advantages that the detection precision and consistency are remarkably improved, personal errors are avoided, the method is particularly suitable for the complex situation that the particle size and the surface roughness are difficult to distinguish, and more objective and accurate spraying quality judgment is provided.

The present application relates to the technical field of image data processing, and more particularly to a forging surface defect detection method and system based on machine vision. The method comprises: acquiring a grayscale image of a forging surface to be detected; determining a boundary saliency weight; determining a path consistency weight; screening the boundary saliency weight and the path consistency weight to obtain a final weight; performing local adaptive threshold segmentation on the final feature map to obtain a binary image, and based on the connected regions in the binary image, performing defect recognition on the forging surface to be detected. By constructing the boundary saliency weight and the path consistency weight, the present application is respectively used for accurately positioning the defect edge and verifying the structural continuity, effectively suppresses the texture interference and reduces false alarms; by adaptive Gabor filtering, the response blind area problem of the traditional method is solved, and finally the two weights are fused to modulate the filtering response, thereby improving the accuracy of the forging surface defect detection.

The present invention discloses a convolution kernel generation method, device, and electronic device, belonging to the field of medical image processing technology. The convolution kernel generated by the present invention is applied to a tubular structure segmentation system, wherein the convolution kernel generation method includes: implementing a three-dimensional Gabor filter according to the Gabor filter principle and constructing a Gabor convolution kernel; based on the backpropagation principle, transferring gradients according to the loss value obtained in each iteration to update the weight of the Gabor convolution kernel; and combining a target model constructed with the Gabor convolution kernel based on a CNN model structure of the Gabor convolution kernel, wherein the target model includes three parts: feature mapping, feature extraction, and feature fusion. The weighted learnable Gabor convolution kernel for tubular structure segmentation constructed by the embodiment of the present invention can directly enhance the features of the tubular structure in terms of scale and direction, reduce redundant parameters of the CNN, and extract more accurate tubular tissue.

The present invention discloses a method and system for testing the durability of pantograph contact networks for highway trucks. The method comprises: step S1, using a high-definition area array camera to capture a pantograph image, and employing a Gabor filter and a support vector machine to identify a pantograph image to be tested in the pantograph image; step S2, using an improved edge detection algorithm to extract a pantograph edge image from the pantograph image to be tested; step S3, locating the pantograph contour based on the pantograph edge image, and obtaining pantograph contour similarity using an invariant moment and a similarity calculation formula; and step S4, obtaining the degree of wear of the pantograph contact network for highway trucks based on the contour similarity, and determining the durability of the pantograph contact network based on the wear degree. By employing a Gabor filter and a support vector machine (SVM) to identify the pantograph image, the present invention can effectively extract pantograph shape information and improve the recognition accuracy of the pantograph image to be tested.

The present application relates to the technical field of coast water edge line detection, and is an island water edge line detection method based on phase consistency random walk, comprising: converting a single polarization SAR image into a phase consistency image by using a two-dimensional logarithmic Gabor filter; obtaining a sea-land membership function by using an FCM method, and creating a global sea-land priori; constructing a cross-sea bridge marker field based on LSD, and creating a marker field priori of a non-water-permeable structure connected to an island; introducing a super-pixel layer to construct a water edge line detection model; inputting the single polarization SAR image into the water edge line detection model to obtain an output label of each pixel, and realizing island water edge line detection. The present application systematically solves the problems of coherent noise interference in the single polarization SAR image, difficulty in seed point initialization of the traditional random walk method, insufficient water edge line extraction precision caused by complex island contour, and invalidity of gray scale similarity in edge detection caused by large differences in ground object backscattering, and can effectively extract the island coast line of the single polarization SAR image.

The present invention relates to a monitoring method for identifying the distribution of dominant species in alpine grasslands based on hyperspectral imaging technology. The method comprises: selecting an area to be monitored within a monitoring area of ​​the alpine grassland, and determining typical dominant plant species in the area to be monitored. Obtaining a hyperspectral image of the area to be monitored, and preprocessing the hyperspectral image to meet model training requirements. Extracting the texture features of the hyperspectral image through a Gabor filter, and extracting the gradient features of the hyperspectral image based on edge information in the hyperspectral image to obtain a texture feature vector and a gradient feature vector of the hyperspectral image. Splicing the texture feature vector and the gradient feature vector, constructing a training data set and a test data set of a support vector machine model, training the support vector machine model, and obtaining a classification model. Finally, calling the classification model to monitor the current hyperspectral image, and outputting the category to which the monitored object in the current hyperspectral image belongs.

The invention provides a fingerprint image processing method which can be applied to the technical field of artificial intelligence. The fingerprint image processing method comprises the following steps: carrying out segmentation processing on an input fingerprint image by adopting a Gaussian mixture model based on an expectation maximization clustering algorithm to obtain a segmented fingerprint image; enhancing the segmented fingerprint image by using a direction and frequency adaptive Gabor filter to obtain an enhanced fingerprint image, the direction of the Gabor filter being determined by a fingerprint local ridge direction, and the frequency being determined by a local ridge frequency; performing binarization processing on the enhanced fingerprint image by adopting a local threshold method to obtain a binarized fingerprint image; and processing the binarized fingerprint image by adopting a sequential refinement algorithm, deleting boundary pixels until a skeleton with a single-pixel width is reserved, and obtaining a processed fingerprint image. The invention further provides a fingerprint image processing device and equipment, a storage medium and a program product.

The application provides a replay voice attack detection method for a voiceprint security authentication system. First, the voice signal is preprocessed, then a plurality of sub-band signals are obtained through linear equal-width Gabor filters, each sub-band signal is processed through FDEO to obtain an instantaneous amplitude and an instantaneous frequency, then the instantaneous amplitude and the instantaneous frequency are respectively taken as inputs of an SENet to obtain enhanced IACC and IFCC features, and the features are respectively processed through windowing and averaging and discrete cosine transformation to obtain respective low-dimensional feature vectors. Then, the extracted IACC and IFCC feature vectors are respectively used to train respective Gaussian mixture model classifiers to obtain respective classifier model parameters. In detection, the IACC and IFCC feature vectors of the voice to be detected are respectively input into the respective GMM classifiers and are scored for credibility, and finally score level fusion is performed to realize discrimination of true and false voices.

The invention provides a rock image classification method based on super-resolution and texture feature fusion. According to the method, the accuracy of mineral classification in the rock slice image is remarkably improved by combining a super-resolution model and a texture feature analysis technology, the enhanced image texture features are extracted by utilizing a Gabor filter, and classification is carried out by adopting a support vector machine. By comparing the performance of various SR models, the optimal model is selected to enhance the image resolution, so that the recognition effect of minerals and pores is improved. In addition, the system further optimizes the classification result through multi-color space feature fusion, finer mineral distribution data is provided for geological analysis, the mineral classification efficiency and precision are improved, and reliable technical support is provided for geological evaluation of oil and gas reservoirs.

The invention discloses a fault sand body identification method based on seismic attribute phase consistency, and the method comprises the steps: S1, extracting the root mean square attribute of a target horizon in a block, and obtaining basic input data; s2, performing normalization and transformation processing on the basic input data to obtain a radial component and an angle component of a phase consistency filter; s3, a multi-scale Log-Gabor filter is constructed; s4, circularly traversing different directions and different scales, calculating the filter response for the convolution of each direction and each scale, respectively accumulating the amplitude response of each scale as SumAn, accumulating the convolution result of an even number filter as SumE, accumulating the convolution result of an odd number filter as SumO, and then calculating a weighted mean response vector; s5, calculating phase consistency energy and phase consistency; s6, constructing covariance data of each point in the image, and calculating the maximum moment of the matrix according to the covariance data; and S7, on the basis of the weighted mean response vector obtained in the step S4, calculating a feature type, namely obtaining a lithologic feature.