A New Edge-Based Method for Artificial Text Detection

2014 ◽  
Vol 998-999 ◽  
pp. 793-796
Author(s):  
Don Gao Zhou ◽  
Qi Cai Du ◽  
Qiang Lin ◽  
Jia Yu Lin
Keyword(s):  
Semantic Content ◽  
Recall Rate ◽  
Text Detection ◽  
Threshold Method ◽  
Textual Information ◽  
Video Frames ◽  
Local Threshold ◽  
Projection Analysis ◽  
Edge Based ◽  
High Level

In video indexing and retrieval systems, textual information is high-level semantic content to describe images or video frames, which is also one of the most interesting objects of study. A great progress has been made in text detection in recent decades, but it is still a great challenge due to the complex background, various fonts and different languages, etc. In this paper, we propose a edge-based approach to detect artificial text. First, the image edge map and difference range map are calculated. Then the map is binarized by local threshold method. Finally the textual information is located through the regional projection analysis. Experience results show our method’s precision rate is 91.1% and recall rate 93.2%, which outperforms the previous work.

Study for Embedded Colony Algorithm Based on Morphological Technology

2014 ◽  
Vol 989-994 ◽  
pp. 2173-2178
Author(s):  
Ke Yi Chen ◽  
Xiao Yue Zheng ◽  
Hui Yu
Keyword(s):  
Complete Separation ◽  
Morphological Processing ◽  
Detection Accuracy ◽  
Image Analysis System ◽  
Threshold Method ◽  
Target Segmentation ◽  
Erosion Processes ◽  
Local Threshold ◽  
Analysis System ◽  
Edge Based

The fast image processing based on iterative erosion principle can complete separation of colony image of adhesion target quickly. This algorithm is composed of local threshold method of image binarization; the morphological processing, analyzing; removing the edge based on Hough Transform and using improved iterative erosion algorithm to adhesion target segmentation and counting. The algorithm has scale invariance and self-adaption for recognition different size colony target. The conditional expansion algorithm designed in the paper can effectively modify the count mistakes which are caused by fragments formed by the segmentation of large size targets in the iterative and erosion processes. The algorithm has been applied in portable colony counting instrument on ARM11 embedded platform. Twenty heterotrophic bacterial samples have been tested in experiments. The results show that this algorithm can realize the image segmentation rapidly and effectively and the time of analysis is less than 2 seconds. The images edge detection rate can reach 100%. The detection accuracy can get the requirements of GB 4789.2-2010 and achieve deviation of 3% in colonies’ total number less than 300CFU.The applied of algorithm is widely in the similar particle image analysis system.

An Edge-Based Algorithm for Text Extraction in Images and Video Frame

2011 ◽  
Vol 403-408 ◽  
pp. 900-907
Author(s):  
Anubhav Kumar ◽  
Awanish Kr Kaushik ◽  
R.L. Yadava ◽  
Divya Saxena
Keyword(s):  
Edge Detection ◽  
Text Detection ◽  
Detection Methods ◽  
Video Frame ◽  
Large Dataset ◽  
Projection Profile ◽  
The Past ◽  
Video Frames ◽  
Edge Based ◽  
Detection And Localization

In this paper, a proposal of a new and unusual framework to detect and extract the text from the images and video frames have been presented. In the past various methods have been presented for detection and localization of text in images and video frames. In this paper, a comparison has been made between several text detection methods and proposed method for text detection in images and video frames. The proposed method is carried out by edge detection, and the projection profile method is used to localize the text region better. Various experiments have been carried out to evaluate and compare the performance of the proposed algorithm. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical. Various parameters like average time, precision and recall rates and analyzed for both existing and proposed method to determine the success and limitation of our method.

Abstractions in Intelligent Multimedia Databases

2013 ◽  
pp. 195-220
Author(s):  
Ranjan Parekh ◽  
Nalin Sharda
Keyword(s):  
Semantic Content ◽  
Multimedia Databases ◽  
Low Level ◽  
Layered Architecture ◽  
Automated Inference ◽  
Semantic Concepts ◽  
High Level ◽  
Intelligent Multimedia ◽  
Visual Keywords ◽  
Semantic Layer

Semantic characterization is necessary for developing intelligent multimedia databases, because humans tend to search for media content based on their inherent semantics. However, automated inference of semantic concepts derived from media components stored in a database is still a challenge. The aim of this chapter is to demonstrate how layered architectures and “visual keywords” can be used to develop intelligent search systems for multimedia databases. The layered architecture is used to extract meta-data from multimedia components at various layers of abstractions. While the lower layers handle physical file attributes and low-level features, the upper layers handle high-level features and attempts to remove ambiguities inherent in them. To access the various abstracted features, a query schema is presented, which provides a single point of access while establishing hierarchical pathways between feature-classes. Minimization of the semantic gap is addressed using the concept of “visual keyword” (VK). “Visual keywords” are segmented portions of images with associated low- and high-level features, implemented within a semantic layer on top of the standard low-level features layer, for characterizing semantic content in media components. Semantic information is however predominantly expressed in textual form, and hence is susceptible to the limitations of textual descriptors – viz. ambiguities related to synonyms, homonyms, hypernyms, and hyponyms. To handle such ambiguities, this chapter proposes a domain specific ontology-based layer on top of the semantic layer, to increase the effectiveness of the search process.

Neural Correlates of Fixated Low- and High-level Scene Properties during Active Scene Viewing

2020 ◽  
Vol 32 (10) ◽  
pp. 2013-2023
Author(s):  
John M. Henderson ◽  
Jessica E. Goold ◽  
Wonil Choi ◽  
Taylor R. Hayes
Keyword(s):  
Real World ◽  
Visual Analysis ◽  
Scene Perception ◽  
Active Vision ◽  
Semantic Content ◽  
Retrosplenial Cortex ◽  
Edge Density ◽  
Visual Stream ◽  
Lateral Occipital Complex ◽  
High Level

During real-world scene perception, viewers actively direct their attention through a scene in a controlled sequence of eye fixations. During each fixation, local scene properties are attended, analyzed, and interpreted. What is the relationship between fixated scene properties and neural activity in the visual cortex? Participants inspected photographs of real-world scenes in an MRI scanner while their eye movements were recorded. Fixation-related fMRI was used to measure activation as a function of lower- and higher-level scene properties at fixation, operationalized as edge density and meaning maps, respectively. We found that edge density at fixation was most associated with activation in early visual areas, whereas semantic content at fixation was most associated with activation along the ventral visual stream including core object and scene-selective areas (lateral occipital complex, parahippocampal place area, occipital place area, and retrosplenial cortex). The observed activation from semantic content was not accounted for by differences in edge density. The results are consistent with active vision models in which fixation gates detailed visual analysis for fixated scene regions, and this gating influences both lower and higher levels of scene analysis.

Lane Marking Detection Based on Segments with Upper and Lower Structure

2019 ◽  
Vol 34 (02) ◽  
pp. 2055005
Author(s):  
Junjie Huang ◽  
Zhiling Wang ◽  
Huawei Liang ◽  
Linglong Lin ◽  
Biao Yu ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Urban Traffic ◽  
Middle Level ◽  
Threshold Method ◽  
Line Segments ◽  
Lane Marking ◽  
Lane Departure ◽  
Lower Structure ◽  
Multiple Disturbance ◽  
High Level

An effective and accurate lane marking detection algorithm is a fundamental element of the intelligent vehicle system and the advanced driver assistant system, which can provide important information to ensure the vehicle runs in the lane or warn the driver in case of lane departure. However, in the complex urban environment, lane markings are always affected by illumination, shadow, rut, water, other vehicles, abandoned old lane markings and non-lane markings, etc. Meanwhile, the lane markings are weak caused by hard use over time. The dash and curve lane marking detection is also a challenge. In this paper, a new lane marking detection algorithm for urban traffic is proposed. In the low-level phase, an iterative adaptive threshold method is used for image segmentation, which is especially suitable for the blurred and weakened lane markings caused by low illumination or wear. In the middle-level phase, the algorithm clusters the candidate pixels into line segments, and the upper and lower structure is used to cluster the line segments into candidate lanes, which is more suitable for curve and dashed lane markings. In the high-level phase, we compute the highest scores to get the two optimal lane markings. The optimal strategy can exclude interference similar to lane markings. We test our algorithm on Future Challenge TSD-Lane dataset and KITTI UM dataset. The results show our algorithm can effectively detect lane markings under multiple disturbance, occlusions and sharp curves.

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