Fast Moving Object Extraction in H.264/AVC Compressed Domain

This paper presents a novel approach for moving object extraction in the H.264/AVC compressed domain, which based on Ant Colony clustering Algorithm (ACA) and threshold method in macro block layer. Firstly, the Motion Vector (MV) field and the macro block types are extracted from the H.264/AVC compressed video, and then merge MVs with the same characteristic. Secondly, an improved ACA is used to classify the MV field into different motion homogenous regions. At the same time, use macro block types to determine the location of objects. Finally, using the complementarities of macro block template and MVs clustering template to obtain final objects. Experimental results for several video sequences demonstrate that in the case of ensuring accuracy, the proposed approach can extract moving object faster.

Salient object extraction for user-targeted video content association

The increasing amount of videos on the Internet and digital libraries highlights the necessity and importance of interactive video services such as automatically associating additional materials(e.g.,advertising logos and relevant selling information) with the video content so as to enrich the viewing experience.Toward this end,this paper presents a novel approach for user-targeted video content association(VCA) .In this approach,the salient objects are extracted automatically from the video stream using complementary saliency maps.According to these salient objects,the VCA system can push the related logo images to the users.Since the salient objects often correspond to important video content,the associated images can be considered as content-related.Our VCA system also allows users to associate images to the preferred video content through simple interactions by the mouse and an infrared pen.Moreover,by learning the preference of each user through collecting feedbacks on the pulled or pushed images,the VCA system can provide user-targeted services.Experimental results show that our approach can effectively and efficiently extract the salient objects.Moreover,subjective evaluations show that our system can provide content-related and user-targeted VCA services in a less intrusive way.

An effective graph and depth layer based RGB-D image foreground object extraction method

We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.

Automatic extraction of foreground objects from Mars images

A novel method is proposed to automatically extract foreground objects from Martian surface images.The characteristics of Mars images are distinct,e.g.uneven illumination,low contrast between foreground and background,much noise in the background,and foreground objects with irregular shapes.In the context of these characteristics,an image is divided into foreground objects and background information.Homomorphism filtering is first applied to rectify brightness.Then,wavelet transformation enhances contrast and denoises the image.Third,edge detection and active contour are combined to extract contours regardless of the shape of the image.Experimental results show that the method can extract foreground objects from Mars images automatically and accurately,and has many potential applications.

E-GrabCut： an economic method of iterative video object extraction

Efficient, interactive foreground/background seg- mentation in video is of great practical importance in video editing. This paper proposes an interactive and unsupervised video object segmentation algorithm named E-GrabCut con- centrating on achieving both of the segmentation quality and time efficiency as highly demanded in the related filed. There are three features in the proposed algorithms. Firstly, we have developed a powerful, non-iterative version of the optimiza- tion process for each frame. Secondly, more user interaction in the first frame is used to improve the Gaussian Mixture Model （GMM）. Thirdly, a robust algorithm for the follow- ing frame segmentation has been developed by reusing the previous GMM. Extensive experiments demonstrate that our method outperforms the state-of-the-art video segmentation algorithm in terms of integration of time efficiency and seg- mentation quality.

Restructuring C Programs into C++ Programs

There exist a lot of legacy systems written in C language, which are difficult to understand, modify, maintain and reuse. How to improve the quality of these non object oriented systems has become an important issue in software engineering area. A possible way is to transform these procedural systems into semantically equivalent object oriented systems implemented in C++ language, which provides object oriented features such as data abstraction, inheritance and polymorphism, makes software system more comprehensible, maintainable and reusable. A detailed discussion on polymorphism analysis, object discovery and possible inheritance relation extraction on C to C++ conversion problem is made, which is also suitable to the transformation on legacy systems implemented in other procedural languages to equivalent object oriented systems.

Bounding box extraction from spherical hologram of elementary object to synthesize hologram of arbitrary three-dimensional scene with occlusion consideration(Invited Paper)

A novel method to extract a bounding box that contains the three-dimensional object from its spherical hologram is proposed. The proposed method uses the windowed Fourier transform to obtain the angular distribution of the quasi-collimated beams at each position in the spherical hologram and estimates the bounding box by accumulating the quasi-collimated beams in the volume inside the spherical hologram. The estimated bounding box is then used to realize occlusion effect between the objects in the synthesis of the three-dimensional scene hologram.