An exemplary object detection method includes generating feature block components representing an image frame, and analyzing the image frame using the feature block components. For each feature block row of the image frame, feature block components associated with the feature block row are evaluated to determine a partial vector dot product for detector windows that overlap a portion of the image frame including the feature block row, such that each detector window has an associated group of partial vector dot products. The method can include determining a vector dot product associated with each detector window based on the associated group of partial vector dot products, and classifying an image frame portion corresponding with each detector window as an object or non-object based on the vector dot product. Each feature block component can be moved from external memory to internal memory once implementing the exemplary object detection method.

Support vector machine based object detection system and associated method

The invention discloses an improved support vector machine-LIBS (laser-induced breakdown spectroscopy) combined sorting method for steel materials. The method comprises the steps of detecting a series of rolled steel samples with known marks with an LIBS system to obtain data matrixes of the rolled steel with different marks, and utilizing a support vector machine to establish a sorting model on the known category data, wherein in the modeling process, an improved modeling method, namely combined modeling is used; after data of the sample to be measured is input to the model, performing fuzzy classification with a one-to-many method, screening candidate categories, and performing sophisticated classification by a one-to-one method, and finally determining the category of the data to be measured. According to the method, the one-to-many and one-to-one modeling methods are combined for use, and advantages of the methods are fully utilized, so the data to be measured can be subjected to two layers of analyzing systems of the fuzzy classification and the sophisticated classification, the influence of useless category information on a prediction process is reduced, the prediction accurate rate can be obviously improved and the calculating cost is reduced.

Improved support vector machine-LIBS (laser-induced breakdown spectroscopy) combined sorting method for steel materials

Embodiments of the present disclosure disclose a lane line identification modeling method and apparatus, and a lane line identification method and apparatus. The lane line identification modeling method includes: identifying an image region of a lane line in an image based on two-dimensional filtering (S11); constructing model training data by using the identified image region (S12); and training a convolutional neural network-based lane line identification model by using the model training data (S13). The lane line identification modeling method and apparatus, and the lane line identification method and apparatus provided in the embodiments of the present disclosure can improve the accuracy of lane line detection.

Method, apparatus, storage medium and device for modeling lane line identification, and method, apparatus, storage medium and device for identifying lane line

Methods and systems for sensor calibration are described. An example method involves receiving image data from a first sensor and sensor data associated with the image data from a second sensor. The image data includes data representative of a target object. The method further involves determining an object identification for the target object based on the captured image data. Additionally, the method includes retrieving object data based on the object identification, where the object data includes data related to a three-dimensional representation of the target object. Additionally, the method includes determining a predicted sensor value based on the based on the object data and the image data. Further, the method includes determining a sensor calibration value based on a different between the received sensor data and the predicted sensor value. Moreover, the method includes adjusting the second sensor based on the sensor calibration value.

Methods and systems for calibrating sensors using recognized objects

The present invention relates to a lane recognizing device and a method applying a top view image conversion, which can perform exact lane recognition by minimizing a distortion and an error in case of recognizing a lane including the boundary part of a curved line and a straight line of the lane during lane recognition, which is the basis of driving stability improvement, of an advanced driving assistance system (ADAS). The lane recognizing device applying a top view image, comprises: an imaging part, which photographs a road region including a lane and generates a road image; a top view converting part, which converts the road image into a top view image; a line extracting part, which extracts a line corresponding to a straight lane and a curved lane in the top view image, extracts a boundary line from a line formed by connecting dots marking the boundary of the straight lane and the curved lane, divides into a straight lane image including the straight lane indicated by a straight lane equation f(u) = b*u + a and a curved lane image including the curved lane based on the extracted boundary, and extracts the curved lane from the curved lane image; and a lane extracting part, which extracts a lane by connecting the extracted straight lane and curved lane. The effect of the present invention is to remarkably improve the accuracy of lane recognition by extracting a lane after converting a photographed road image into a top view image, thereby remarkably improving the driving safety of a vehicle with a vehicle recognizing device.

Apparatus and method for detecting lane based on top view image transform

PURPOSE: An apparatus for traffic lane detection and tracking in various road conditions is provided to continuously monitor a current position of a driving vehicle by interworking with a GIS and GPS, thereby solving problems such as a false alarm that can occur in a crossroad, T-type accessway, or the like CONSTITUTION: An apparatus for traffic lane detection and tracking in various road conditions comprises an image interface unit (300) to provide image frames received from a camera; a lane detection/matching performing unit (304) to select preparatory lane candidates using the images provide by the image interface unit and to determine a preparatory lane candidate the difference of which between a road model of a final lane determined in a previous frame is smaller than a threshold value; a lane tracking unit (308) to update the preparatory lane candidate with positional information of lanes including a width of lane, a curvature of lane, and a quantity of drivers included in a current road model information that is relative depending on positions; and an alarm generation unit (310) to determine whether a vehicle departures from lanes by comparing the updated lane information of the lane tacking unit to a previously stored lane information [Reference numerals] (300) Image interface unit; (302) Reverse projection performing unit; (304) Lane detection/matching performing unit; (306) Positional information providing unit; (308) Lane tracking unit; (310) Alarm generation unit

Lane departure warning system

PURPOSE: An integrated type SVM(Support Vector Machine) circuit device is provided to integrate linear and non-linear SVM circuits and to selectively drive the linear and non-linear SVM circuits, thereby efficiently recognizing and classifying an object. CONSTITUTION: An inner calculation integration circuit unit(20) receives feature values, support vectors, and kernel type values for recognition and classification objects. The inner calculation integration circuit unit calculates a first calculation value by performing selectively performing inner calculation based on the feature values and the support vectors corresponding to the kernel type values. When a value selecting a non-linear type with the kernel type values is inputted, a kernel function calculation circuit unit(50) receives the first calculation unit from the inner calculation integration circuit unit and calculates a second calculation value by performing calculation based on a kernel function. The kernel function calculation circuit unit recognizes and classifies the objects. [Reference numerals] (10) Preprocessing unit; (20) Inner calculation integration circuit unit; (30) Linear inner calculation unit; (40) Non-linear inner calculation unit; (50) Kernel function calculation circuit unit; (AA) Object image; (BB) Feature value; (CC) Support vector; (DD) Kernel type value; (EE) Recognition and classification result output;

Converged type circuit apparatus of support vector machine

PURPOSE: A sensor compensation system using video information and distance-angular information is provide to restructure the coordinates of the real world by using distance information obtained through output of a distance-angle sensor, and to automatically calculate a physical arrangement error in the distance-angle sensor and an image sensor. CONSTITUTION: An image sensor unit clearly recognizes an object from an input image. A distance-angle sensor unit extracts a rotation angle of the object by a laser. An information processing unit extracts the real location of the object. The information processing unit extracts the rotation angle of the object according to the extracted real location. The information processing unit outputs an error compensation signal. The error compensation signal directs the difference between the extracted rotation angles within a predetermined range.

System for sensor revision used image information and distant-angle information

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lee Sung Ho

Author Tools

Chat about Author

Papers

Lane departure warning system

Converged type circuit apparatus of support vector machine

System for sensor revision used image information and distant-angle information