With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing. Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size). Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy. First, denoising and smoothing are applied on scene data. Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects. Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects. An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. Finally, labels are estimated via Maximum Entropy model. During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07% over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively. The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.

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Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-to-Object Relations

Ultrasound Computed Tomography for Material Inspection: Principles, Design and Applications

Vision-based vehicle detection for a driver assistance system

Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability. Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance. Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems. An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems. Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features. A mask with twenty-four edge direction information points (345°) was established based on edge line texture features. By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence. The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value. A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value. A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image. The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method. The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image. Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods. The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.

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Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

With the popularity of automobiles, road traffic accidents and congestion have become increasingly serious. Therefore, technologies are needed to solve problems such as speeding and congestion. The detection and tracking of vehicles based on computer vision and Internet of Things monitoring are an important part of the intelligent traffic monitoring system. The angle between the camera and the vehicle will cause the gradually moving vehicles to have a connection during image segmentation. This paper aims to improve the detection accuracy of vehicles from camera images. A new separation method of the vehicle detection area was proposed in this paper. Moving areas are extracted by inter-frame differences, and vehicle areas are formed from the areas. If more than one vehicle area partially overlaps as one area, it is necessary to separate the area. The existing method extracts a place to be separated from an outline of the area. However, it is impossible for the method to separate vehicles using the extracted shape. Therefore, a new method is proposed that makes the place to be separated defined by the reshaping of the area with the use of the Fourier descriptor. The method tries to detect the place from the area. As a result, this method makes it possible to separate the area that the existing method cannot separate and it has obtained a high accuracy of separation in the experimental data of the Internet of Things monitoring.

Separation of Vehicle Detection Area Using Fourier Descriptor Under Internet of Things Monitoring

Ultrasonic image reconstruction based on maximum likelihood expectation maximization for concrete structural information

A novel method for moving vehicle tracking based on horizontal edge identification and local autocorrelation images

Many state-of-the-art shape features have been proposed for the shape recognition task. In this paper, to explore whether a shape feature influences object segmentation, we propose a specific shape feature, Fisher shape (a form of bag of contour fragments), and we combine this with the appearance feature with multiple kernel learning to create a pipeline of object segmentation system. The experimental results on benchmark datasets clearly demonstrate that the pipeline of object segmentation is effective and that the Fisher shape can improve object segmentation with only the appearance feature.

Bag of contour fragments for improvement of object segmentation

Vehicle shadow and superposition have a great influence on the accuracy of vehicles detection in traffic video. Many background models have been proposed and improved to deal with detection moving object. This paper presented a method which improves Gaussian mixture model to get adaptive background. The HSV color space was used to eliminate vehicle shadow, it was based on a computational colour space that makes use of our shadow model. Vehicle superposition elimination was discussed based on vehicle contour dilation and erosion method. Experiments were performed to verify that the proposed technique is effective for vehicle detection based traffic surveillance systems. Detection results showed that our approach was robust to widely different background and illuminations.

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Improving Vehicle Detection Accuracy Based on Vehicle Shadow andSuperposition Elimination

Edge feature extraction is an important basis part for subsequent processing of video frame. The region with a small color difference is transformed into a luminance value for edge detection, which increases the difficulty of edge detection and reduces the detection accuracy. In order to improve the precision of edge detection in the regions with small color difference, local autocorrelation is proposed to enhance the edge feature. Edge detection is completed by using chroma information, and the detection rate is used to measure the extent of the detected edge of the ideal edge to be extracted. The input video frame is converted to YCbCr color mode, and local autocorrelation is applied to the luminance information Y and chroma information Cb for edge detection. Complementary processing is applied to the resulting image of edge detection based on local autocorrelation. In order to further improve the detection rate, supplementary processing is carried out. The standard edge images are defined for quantitative analysis and evaluation of proposed methods. Two kinds of edge correction processing based on the local autocorrelation comparison method and average method are proposed. Through experimental verification, the comparison method has a higher detection rate than the common method on the basis of local autocorrelation.

Zhu Hongjin

Papers

Ultrasonic image reconstruction based on maximum likelihood expectation maximization for concrete structural information

A novel method for moving vehicle tracking based on horizontal edge identification and local autocorrelation images

Bag of contour fragments for improvement of object segmentation

Improving Vehicle Detection Accuracy Based on Vehicle Shadow andSuperposition Elimination

Edge Detection With Chroma Components of Video Frame Based on Local Autocorrelation