In this paper, we propose an original framework for an intuitive tuning of parameters in image and video segmentation algorithms. The proposed framework is very flexible and generic and does not depend on a specific segmentation algorithm, a particular evaluation metric, or a specific optimization approach, which are the three main components of its block diagram. This framework requires a manu...

Abstract Impact of Corporate Reputation on Brand Segmentation Strategy : An Empirical Study from Iranian Pharmaceutical Companies The impact of corporate reputation uses, including value creation, corporate communication and strategic resources on branding strategies such as segmentation and producing intangible assets for different industries is investigated in western countr...

Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metrics that only consider the quality of segmentation. However, safe operation a system in real world it crucial uncertainty prediction as well. In this work, we introduce novel task uncertainty-aware panoptic segmentation...

Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient addressing over-segmentation, top-down suffers from over-crop problem. However, a complete mask crucial for biological image analysis as delivers important morphological properties such shapes and volumes. In this paper, we propose region proposal rec...

the purpose of this study is to find out if there is a relationship between parameter ofsegmentation and customer satisfaction, in order to find out the current parameter of segmentationin automobile industries in india researcher has gone through several published journal, books,magazines and companies data. the result shows that the most important factor for segmentation ofautomobile industri...

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modeling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it suitable for scalable object-based wavelet coding. To optimize s...

The segmentation of structure from images is an inherently di cult problem in computer vision and a bottleneck to its widespread application, e.g., in medical imaging. This paper presents an approach for integrating local evidence such as regional homogeneity and edge response to form global structure for gure-ground segmentation. This approach is motivated by a shock-based morphogenetic langua...

Segmentation of a 3-dimensional (3D) polygonal mesh is a method of breaking the mesh down into \meaningful" connected subsets of vertices called regions. The method used here is based on the watershed segmentation scheme which appears prominently in the image segmentation literature and was later applied to the 3D segmentation problem. The watershed algorithm is based on a scalar value (curvatu...

Historically, figure-ground segmentation has been seen as an important and even necessary precursor for object recognition. In that context, segmentation is mostly defined as a data driven, that is bottom-up, process. As for humans object recognition and segmentation are heavily intertwined processes, it has been argued that top-down knowledge from object recognition can and should be used for ...

The Segmentation According to Natural Examples (SANE) algorithm learns to segment objects in static images from video training data. SANE uses background subtraction to find the segmentation of moving objects in videos. This provides object segmentation information for each video frame. The collection of frames and segmentations forms a training set that SANE uses to learn the image and shape p...