Fast ISAR imaging method based on scene segmentation

Although compressed sensing inverse synthetic aperture radar(ISAR) imaging methods are widely used in radar signal processing, its reconstructing time and memory storage space requirements are very high. The main reason is that large scene reconstruction needs a higher dimension of the sensing matrix. To reduce this limitation, a fast high resolution ISAR imaging method,which is based on scene segmentation for random chirp frequencystepped signals, is proposed. The idea of scene segmentation is used to solve the problems aforementioned. In the method,firstly, the observed scene is divided into multiple sub-scenes and then the sub-scenes are reconstructed respectively. Secondly, the whole image scene can be obtained through the stitching of the sub-scenes. Due to the reduction of the dimension of the sensing matrix, the requirement of the memory storage space is reduced substantially. In addition, due to the nonlinear superposition of the reconstructed time of the segmented sub-scenes, the reconstruction time is reduced, and the purpose of fast imaging is achieved.Meanwhile, the feasibility and the related factors which affect the performance of the proposed method are also analyzed, and the selection criterion of the scene segmentation is afforded. Finally,theoretical analysis and simulation results demonstrate the feasibility and effectiveness of the proposed method.

Tracking a Screen and Detecting Its Rate of Change in 3-D Video Scenes of Multipurpose Halls

An automatic approach is presented to track a wide screen in a multipurpose hall video scene. Once the screen is located, this system also generates the temporal rate of change by using the edge detection based method. Our approach adopts a scene segmentation algorithm that explores visual features （texture） and depth information to perform efficient screen localization. The cropped region which refers to the wide screen undergoes salient visual cues extraction to retrieve the emphasized changes required in rate-of- change computation. In addition to video document indexing and retrieval, this work can improve the machine vision capability in the behavior analysis and pattern recognition.

Domain adaptive semantic segmentation by optimal transport

Scene segmentation is widely used in autonomous driving for environmental perception.Semantic scene segmentation has gained considerable attention owing to its rich semantic information.It assigns labels to the pixels in an image,thereby enabling automatic image labeling.Current approaches are based mainly on convolutional neural networks(CNN),however,they rely on numerous labels.Therefore,the use of a small amount of labeled data to achieve semantic segmentation has become increasingly important.In this study,we developed a domain adaptation framework based on optimal transport(OT)and an attention mechanism to address this issue.Specifically,we first generated the output space via a CNN owing to its superior of feature representation.Second,we utilized OT to achieve a more robust alignment of the source and target domains in the output space,where the OT plan defined a well attention mechanism to improve the adaptation of the model.In particular,the OT reduced the number of network parameters and made the network more interpretable.Third,to better describe the multiscale properties of the features,we constructed a multiscale segmentation network to perform domain adaptation.Finally,to verify the performance of the proposed method,we conducted an experiment to compare the proposed method with three benchmark and four SOTA methods using three scene datasets.The mean intersection-over-union(mIOU)was significantly improved,and visualization results under multiple domain adaptation scenarios also show that the proposed method performed better than semantic segmentation methods.

Underwater Image Enhancement Based on the Dark Channel Prior and Attenuation Compensation

Aimed at the two problems of underwater imaging, fog effect and color cast, an Improved Segmentation Dark Channel Prior(ISDCP) defogging method is proposed to solve the fog effects caused by physical properties of water. Due to mass refraction of light in the process of underwater imaging, fog effects would lead to image blurring. And color cast is closely related to different degree of attenuation while light with different wavelengths is traveling in water. The proposed method here integrates the ISDCP and quantitative histogram stretching techniques into the image enhancement procedure. Firstly, the threshold value is set during the refinement process of the transmission maps to identify the original mismatching, and to conduct the differentiated defogging process further. Secondly, a method of judging the propagating distance of light is adopted to get the attenuation degree of energy during the propagation underwater. Finally, the image histogram is stretched quantitatively in Red-Green-Blue channel respectively according to the degree of attenuation in each color channel. The proposed method ISDCP can reduce the computational complexity and improve the efficiency in terms of defogging effect to meet the real-time requirements. Qualitative and quantitative comparison for several different underwater scenes reveals that the proposed method can significantly improve the visibility compared with previous methods.

A multiphase texture segmentation method based on local intensity distribution and Potts model

Because texture images cannot be directly processed by the gray level information of individual pixel,we propose a new texture descriptor which reflects the intensity distribution of the patch centered at each pixel.Then the general multiphase image segmentation model of Potts model is extended for texture segmentation by adding the region information of the texture descriptor.A fast numerical scheme based on the split Bregman method is designed to speed up the computational process.The algorithm is efficient,and both the texture descriptor and the characteristic functions can be implemented easily.Experiments using synthetic texture images,real natural scene images and synthetic aperture radar images are presented to give qualitative comparisons between our method and other state-of-the-art techniques.The results show that our method can accurately segment object regions and is competitive compared with other methods especially in segmenting natural images.