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Semantic Segmentation on Multi-Spectral Images
2021 29th Signal Processing and Communications Applications Conference (SIU), 2021Semantic segmentation is considered to be one of the basic steps in understanding image content. For semantic segmentation, if multi-spectral images are used together with color images, more successful results are obtained due to complementary information obtained from multi-spectral images.
Elmacı, Mehmet +2 more
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Semantic Segmentation of Fisheye Images
2019Semantic segmentation of fisheye images (e.g., from action-cameras or smartphones) requires different training approaches and data than those of rectilinear images obtained using central projection. The shape of objects is distorted depending on the distance between the principal point and the object position in the image. Therefore, classical semantic
Gregor Blott +2 more
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Image Segmentation by semantic method
Pattern Recognition, 1987Abstract The problem of region detection is addressed. Linear and quadratic approximation schemes are used to approximate the regions in an image. A set of attributes, which represent the properties of a region, are defined. A distance function, which has structural as well as semantic part, is introduced.
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Semantic image segmentation for pedestrian detection
Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, 2017A typical traffic monitoring system for pedestrian detection uses a stationary camera. In Advanced Driving Assistance Systems (ADAS), the camera is mounted in front of the vehicle's window so that the camera and the object move in any arbitrary direction. Semantic image segmentation is widely used for road scene interpretation.
Adi Nurhadiyatna, Sven Loncaric
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Cross-Image Pixel Contrasting for Semantic Segmentation
IEEE Transactions on Pattern Analysis and Machine IntelligenceThis work studies the problem of image semantic segmentation. Current approaches focus mainly on mining "local" context, i.e., dependencies between pixels within individual images, by specifically-designed, context aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization objectives (e.g., IoU-like loss). However,
Tianfei Zhou, Wenguan Wang
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Image Semantics Segmentation using Watershed Algorithm
2006 IEEE International Conference on Service Operations and Logistics, and Informatics, 2006In this paper a novel image semantics segmentation algorithm is proposed, which combines edge and region-merged based techniques. First, an edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of an image gradient.
Miao chengliang, Xie shengli, Yu weiyu
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Semantic Segmentation-Based Image Inpainting Detection
2020Image manipulation detection has evolved as a very challenging problem in the field of multimedia forensics. Object removal from the image is one of the most used manipulation operations in the image. Object removal accomplished by exemplar-based image inpainting makes image visually pleasing and physically plausible without any noticeable trace.
Nitish Kumar, Toshanlal Meenpal
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Semantic AutoSAM: Self-Prompting Segment Anything Model for Semantic Segmentation of Medical Images
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)Segment Anything Model (SAM) is a foundation model that can be prompted with sparse prompts, like boxes or points, and dense prompts such as masks. SAM outputs binary masks based on the given prompts but lacks semantic understanding as it doesn't output the class of the predicted mask.
Assefa S, Wahd +3 more
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Semantic Guided Deep Unsupervised Image Segmentation
2019Image segmentation is an important step in many image processing tasks. Inspired by the success of deep learning techniques in image processing tasks, a number of deep supervised image segmentation algorithms have been proposed. However, availability of sufficient labeled training data is not plausible in many application domains.
Saha, Sudipan +3 more
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