3819 results about "Histogram" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention discloses an unmanned aerial vehicle electric power inspection image intelligent analysis method and system based on deep learning and multi-modal fusion and a medium thereof, and relates to the technical field of electric power equipment detection. The method comprises the following steps: planning an optimal inspection path by adopting an A * algorithm to realize multi-sensor synchronous data acquisition; adaptive histogram equalization and defogging processing are carried out on the visible light image, non-uniformity correction and temperature calibration are carried out on the infrared image, and filtering and registration are carried out on point cloud data; constructing a multi-scale feature fusion network based on improved VGGNet-16, and introducing deformable convolution and a cross-modal attention mechanism to realize multi-source data fusion; defect detection is carried out based on a three-level template library and a feature map cross-correlation algorithm, and the precision is improved in combination with non-maximum suppression and sub-pixel positioning; and finally generating a detection report containing defect types, positions and maintenance suggestions. According to the invention, the automation level and the detection precision of power inspection are obviously improved.

The invention provides a reinforcement learning-based desilting strategy optimization method and system, and relates to the technical field of intelligent water area desilting, and the method comprises the steps: carrying out the multi-scale decomposition, adaptive histogram equalization, contrast optimization and edge sharpening processing of a collected image, and obtaining an optimized desilting environment image; then constructing a three-dimensional voxel grid model based on the optimized image, and establishing an environment interference source mathematical model and a dynamic evolution rule of sludge distribution; and finally, inputting the image features into a dredging strategy optimization network, through interaction with a simulation environment, updating network parameters based on a dual time sequence difference algorithm, generating a motion track planning scheme and an operation parameter configuration scheme of the dredging robot, and optimizing a value function on line according to an actual dredging effect. The desilting efficiency can be effectively improved, and the desilting cost is reduced.

The invention provides a satellite-borne multi-band synthetic aperture radar signal processing optimization method and system, and relates to the technical field of signal processing, and the method comprises the steps: obtaining original echo data of a satellite-borne multi-band synthetic aperture radar, carrying out Doppler center frequency estimation, carrying out two-dimensional matrix product operation, and obtaining compensation echo data; distance direction position information and scattering intensity information are obtained based on a distance direction matching filtering algorithm of sub-band decomposition; determining spatial distribution density, performing adaptive block processing, and performing phase error modeling in each block to generate a continuous phase correction function; performing wavelet domain convolution operation to obtain corrected echo data, obtaining a multi-band two-dimensional image, extracting a multi-dimensional feature vector through a deep neural network, performing weight learning, performing nonlinear weighted fusion, and performing adaptive histogram equalization enhancement to determine a synthetic aperture radar imaging result.

The invention discloses a method for identifying impurities and bubbles in a wine bottle based on multi-feature comprehensive judgment, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: employing a high-resolution industrial camera to be combined with an LED light source, and continuously shooting multiple frames of images of the wine bottle under the consistent shooting angle and illumination condition; carrying out the preprocessing of the collected image through employing a self-adaptive histogram equalization and Gaussian blur method; extracting the edges of impurities and bubbles in the wine bottle on the image by adopting a Canny edge detection algorithm to obtain an edge image; segmenting the edge image into a plurality of target areas by using a contour detection technology, and extracting possible bubble and impurity areas from the edge image; extracting target area features including circularity, gray average, gray uniformity, texture features and color space; and according to the extracted features, bubbles and impurities are distinguished by applying a comprehensive judgment rule. According to the method, the bubbles and the impurities are comprehensively judged by adopting multi-feature combination, so that the accuracy and the robustness of detection are greatly improved.

The invention provides an image-based three-dimensional point cloud traffic marker classification method and system, and relates to the technical field of computer vision, and the method comprises the steps: collecting a multi-frame multi-angle image of a traffic marker, converting the multi-frame multi-angle image into point cloud data, constructing a histogram based on reflection intensity and geometric features, determining an optimal segmentation threshold value, and carrying out the segmentation of the point cloud data; the method comprises the following steps: carrying out region merging by combining spatial distribution characteristics of scattering coefficients and absorption coefficients to obtain point cloud sub-regions with uniform materials, determining a core region by utilizing a curvature entropy value and a normal vector entropy value, establishing a local coordinate system, carrying out region growth and dynamic merging through spatial position and geometric structure constraints to obtain candidate point cloud sub-regions, and carrying out point cloud distribution on the candidate point cloud sub-regions. And extracting the position offset and the attitude variation to construct a time sequence motion feature, thereby realizing accurate classification of the traffic markers.

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The invention relates to the technical field of machine learning, in particular to a grain classification and recognition method and system based on image analysis, and the method comprises the following steps: obtaining grain particle image data, calculating a gray histogram and a gradient change rate, calling a gray co-occurrence matrix to extract grain particle texture density, and scanning a sliding window to obtain local feature parameters of grain particles. According to the method, through combined analysis of a gray level histogram and a gradient change rate, dynamic extraction of particle textures through a sliding window, enhancement of complex surface feature capture, normalization of contrast and direction consistency parameters, construction of weighted feature vectors, dynamic correction of variance contribution degree and suppression of environmental interference, and fusion of near-infrared and short-wave infrared reflectivity changes, the near-infrared and short-wave infrared reflectance change is improved. And quantifying the mean value and peak-to-valley ratio of spectrum difference values, matching visible light characteristics to verify multi-dimensional constraints, constructing a spectrum-texture matrix by local reflectivity rate and gradient change rate, and synchronously evaluating global similarity and spatial distribution difference by adopting Euclidean distance and local offset double-layer matching.

The invention discloses a real-time three-dimensional fusion reconstruction method for a ship comprehensive test scene, and the method comprises the steps: carrying out the preprocessing of a target object image data flow, converting an image coordinate system into a camera coordinate system, obtaining a sparse point cloud model, and constructing a voxel three-dimensional grid; the method comprises the following steps: constructing a scale space by using a Gaussian difference pyramid to detect key points of an image under different scales, calculating a gradient direction histogram around the key points and taking the gradient direction histogram as a descriptor, matching the descriptor by using an Euclidean distance, and estimating relative pose transformation between the images to complete a local matching process. Projecting and mapping the local frame block to a reference frame to form a key frame, and entering a global matching process; and extracting a surface grid of the three-dimensional model from the dense body grid by adopting a Marching Cubes algorithm, traversing each cube unit in the surface grid, and obtaining distance information from a vertex to a nearest surface based on the cube units, thereby generating a final three-dimensional model of the test target object.

The invention relates to the field of resistor defect detection, in particular to an alloy resistor surface defect real-time detection method and system based on image processing. The method comprises the following steps: acquiring an alloy resistor surface image, calculating a local sudden disturbance factor of a pixel point, analyzing a gray offset condition and a gradient direction deflection condition in a neighborhood of the pixel point, and calculating a gray texture disturbance factor; calculating a local defect response factor; obtaining each candidate region, analyzing the shape of each candidate region, and constructing a salient region structure responsivity in combination with local defect influence factors of pixel points in the candidate regions; giving a suspected abnormal weight to each pixel point in the gray scale resistor surface image, constructing a weighted gray scale histogram based on the suspected abnormal weight and the gray scale value, obtaining a segmentation threshold in the weighted gray scale histogram by using an Otsu threshold segmentation algorithm, and detecting the surface defect of the alloy resistor; and the precision of alloy resistor surface defect detection is improved.

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention relates to the technical field of image enhancement, in particular to a protector bimetallic strip detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a bimetallic strip, and carrying out the preprocessing of the surface image, and obtaining a gray image; performing threshold segmentation on the grayscale image to obtain at least one segmentation region; obtaining an evaluation coefficient of each segmented region, obtaining an adaptive cutting parameter when contrast-limited adaptive histogram equalization is carried out on each segmented region according to the evaluation coefficient of each segmented region, and carrying out image enhancement on each segmented region in the grayscale image according to the adaptive cutting parameter to obtain an enhanced grayscale image; the corrosion detection result in the bimetallic strip is obtained by using the enhanced gray level image, so that the enhanced image can reflect more effective information, and the surface corrosion detection precision of the bimetallic strip according to the enhanced image is improved.

The invention relates to the technical field of gesture recognition, in particular to an anti-interference optimized gesture recognition method. Comprising the following steps: acquiring a gesture video stream through a camera, constructing a dynamic background model by using a frame difference method and a Gaussian mixture model, eliminating a static background and interference, and extracting a target area image; performing local brightness histogram analysis on the target region image, and optimizing the image quality by adopting a region adaptive compensation algorithm and a multi-scale edge enhancement technology; positioning a gesture area in real time by using a color histogram and a feature matching algorithm, and dynamically updating a gesture track in combination with Kalman filtering; and extracting gesture shapes, tracks and dynamic mode features through deep learning, comparing the features with a standard model library, and outputting gesture categories and corresponding function instructions. According to the method, a multi-level optimization strategy is adopted for a complex background, a dynamic target and a changeable illumination environment, so that the anti-interference capability and the recognition precision of gesture recognition are improved.

The invention discloses a digital signal analysis method and device, a terminal and a storage medium. The method comprises the following steps: eliminating data related jitter corresponding to a target digital signal from time interval error data corresponding to the target digital signal; obtaining a random jitter histogram corresponding to the target digital signal and a periodic jitter histogram corresponding to the target digital signal; obtaining an error rate curve corresponding to the target digital signal based on data related jitter corresponding to the target digital signal, the random jitter histogram and the periodic jitter histogram; generating a bathtub curve corresponding to the target digital signal according to the error rate curve corresponding to the target digital signal; and obtaining a target analysis result based on the bathtub curve. The invention aims to accurately and efficiently complete the jitter analysis of the digital signal so as to judge the stability of a digital signal system.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention relates to a segmentation model based on a CNN and Transform parallel double-branch structure, and belongs to the technical field of medical data prediction. The method comprises the following steps: acquiring a CT image, and preprocessing the CT image by executing windowing processing and contrast limited adaptive histogram equalization; extracting features of lung lesions in the preprocessed CT image through a parallel double-branch structure; inputting the extracted features into a cross enhancement fusion module, and performing complementary fusion on the features through dynamic weight distribution to obtain fused features; the fusion features are input into a multi-scale context information extraction module, and lesion boundary sensitivity is enhanced through cavity convolution of different expansion rates; the encoder features and the decoder features are fused through jump connection, and a segmentation result is output after resolution is recovered based on up-sampling; and optimizing model training by adopting a weighted loss function. Accurate segmentation of the lung lesion in the pulmonary tuberculosis CT image is realized, and clearer and more accurate lesion area information can be provided.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention relates to the technical field of network video monitoring, in particular to a terminal equipment collaborative video monitoring method and system, which comprises the following steps of: monitoring video data based on terminal equipment, extracting a motion vector, a pixel intensity value and a color histogram of each frame, carrying out space-time alignment, calculating an average value and a variance of each parameter, and calculating the average value and the variance of each parameter; time sequence characteristics are analyzed, parameter changes of key frames are extracted, and an initial behavior characteristic set is established; according to the method, the motion vector, the pixel intensity and the color histogram of each frame of video are analyzed, and the data are subjected to space-time alignment and statistical analysis, so that the understanding of a behavior mode is improved, and clustering analysis helps a system to efficiently identify abnormities, monitor in real time and calculate parameter deviation to ensure that abnormal behaviors are found in time; the adaptability and efficiency of the system to the monitoring environment are further improved through data exchange and collaborative analysis between the terminal devices, and the coverage range and the response speed of the monitoring system are remarkably enhanced through the highly collaborative processing mode.

A method may include obtaining a petrographic image. The method may further include determining various region proposals based on the petrographic image and a selective searching function. A respective region proposal among the region proposals may correspond to various pixels in the petrographic image according to a predetermined dimension. The method may further include determining color histogram data for the petrographic image. The method may further include determining input image data based on the petrographic image, the region proposals, and the color histogram data. The method may further include determining a rock object using the input image data and a machine-learning model.

The invention relates to the technical field of video compression, in particular to a video monitoring data compression and transmission method and system, and the method comprises the following steps: analyzing the texture features of a video frame, calculating the gray gradient of each pixel, obtaining the texture change rate, detecting edge information, calculating the distribution density of edge pixels, and extracting a color histogram. And calculating the dispersion degree of color distribution. In the invention, a dynamic coding parameter adjustment mechanism based on content is introduced to analyze the video frame, a compression strategy is adjusted according to the content complexity, and the coding process can accurately meet the requirements of different video contents through comprehensive analysis on the texture change rate, the edge density and the color dispersion degree; according to the scheme, the coding parameters can be dynamically adjusted by monitoring the coded video quality in real time and comparing the coded video quality with the preset quality threshold value, and the storage and transmission of data are further optimized while the video quality is ensured.

The invention relates to the technical field of image enhancement, in particular to a liver CT image enhancement method and system. The method comprises the following steps: acquiring an initial window of each connected domain; obtaining the density change degree of each initial window according to the density distribution of pixel points in each initial window on the CT image; obtaining a multi-scale image corresponding to the CT image, and obtaining the high-frequency information change degree of each initial window according to the frequency domain distribution characteristics of the initial windows corresponding to the same positions among different-scale images; obtaining a window correction coefficient of each initial window, correcting each initial window, and obtaining a correction window of each initial window; and performing adaptive histogram equalization on the images of different scales according to the correction window, and performing weighted fusion in combination with the confusion degree of the images of the corresponding scales to obtain an enhanced CT image. According to the method, the effect of influence enhancement is improved by obtaining the accurate window size during image equalization processing.

In accordance with some embodiments, various examples of systems, methods, and media for single photon depth imaging with improved efficiency using learned compressive representations are provided. For example, systems and methods herein may detect a photon arrival based on a signal from a detector at a given position and time bin. A compressed histogram comprising K stored values, representing bins of the compressed histogram based on K values in a code word calculated based on the time bin and position and K coding tensors. Such systems and methods may be utilized for, among other uses, determining depth in a scene being imaged by the detector.

The invention relates to the technical field of data asset exploration, in particular to an intelligent data asset exploration method and system based on a knowledge graph, and the method comprises the steps: obtaining enterprise data assets, carrying out the dynamic annotation, and generating an annotation data set; performing semantic analysis based on the annotation data set to obtain a semantic association graph; constructing a knowledge graph model, calculating node weights, association path lengths and path contribution degrees, and generating a simulation association distribution graph; segmenting the semantic association graph through the node density and the relationship strength to obtain an actual association distribution graph; and adjusting the knowledge graph model based on the actual and simulated association distribution histogram. According to the invention, the intelligent level and accuracy of data asset exploration can be improved.

The invention discloses a carbon-coated foil coating uniformity detection method and system based on machine vision, and the method comprises the steps: S1, collecting blue light, green light, red light and near infrared spectrum images of the surface of a carbon-coated foil coating through a multi-angle camera, and generating a fused composite image through a multispectral image fusion algorithm; s2, performing adaptive histogram equalization and reflected light spot suppression processing on the fused composite image, and outputting an enhanced image; s3, coating edge features in the enhanced image are extracted, segmentation of a coating region is realized in combination with a region growing method, and a coating region mask is obtained; s4, extracting multi-scale texture features of the coating region by adopting a pre-trained ResNet-50 deep convolutional network, and constructing a coating surface feature vector; and S5, based on the extracted feature vectors and a standard template library, through a uniformity evaluation model, calculating a uniformity index of the coating, and detecting the uniformity of the coating. According to the invention, accurate detection of the coating uniformity can be realized.

The invention discloses a two-stage network underwater image enhancement method based on color correction and multicolor space stretching, and the method comprises the steps: compensating a red channel and a blue channel through a green channel, obtaining a red channel correction image and a blue channel correction image, constructing an improved U-Net network, and outputting the weight mapping of the red channel and the blue channel; performing dot product multiplication on the red and blue channel correction images and the corresponding red and blue channel weight mapping, and splicing the dynamic compensation results of the red and blue channels and the green channel into an RGB space color correction image; the RGB space color correction image is converted into an HSI space and a Lab color space respectively, histogram stretching is carried out on the HSI space, the Lab color space and the RGB space respectively, finally, the three kinds of images subjected to histogram stretching are converted into the RGB color space respectively, and the three images converted into the RGB color space are spliced to obtain a comprehensive input image; and inputting the comprehensive input image into the improved U-Net network, training again, and outputting an enhanced image, so that the imaging resolution and the color correction effect of the underwater camera can be remarkably improved.

The invention discloses a corn germination image segmentation method based on an elite adaptive rime algorithm. Relates to the technical field of agricultural seed detection and image processing, in particular to the technical field of corn germination image segmentation based on an elite adaptive rime algorithm. According to the method, a dual-adaptive weight mechanism and an elite reselection strategy are introduced into a rime optimization algorithm, the convergence capability of the algorithm is enhanced, and multi-threshold segmentation is carried out on the corn kernel germination image in combination with the Kapur entropy. And the image segmentation precision is effectively improved. The method comprises the following steps: acquiring a corn germination image data set; drawing a two-dimensional histogram, and inputting the two-dimensional histogram into a Kapur entropy function to obtain an objective function fobj; an elite solution module is initialized; a dual-adaptive weight mechanism is added; a soft rime strategy and a hard rime strategy are improved; updating the elite solution after the rime search strategy module; and the target function fobj is input into a rime improvement algorithm, and an optimal threshold value is obtained.

The invention relates to the technical field of image processing, and discloses an intelligent weight monitoring method, system and device for bred pigs and a storage medium. The method comprises the following steps: acquiring an original data set through multi-angle image acquisition; carrying out image processing by utilizing adaptive histogram equalization and Gaussian filtering; obtaining body shape parameters by adopting contour-skeleton feature extraction and curvature analysis; performing multi-source fusion processing in combination with the data of the Internet of Things; body weight modeling is carried out based on a hierarchy-increment prediction model and an attention mechanism; and generating a feeding scheme through reinforcement learning. According to the invention, the technical problems of insufficient data fusion and low prediction precision in the existing breeding pig weight monitoring method are solved.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.