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Image diffusion is the underlying machine vision research method, and is frequently used to remove image noise. In this paper, we propose a new kernel function for image diffusion by combining the intervening contour (IC) and the color (C) component, called ICC kernel function, which can smooth inner messy texture while maintaining image structure. The intervening contour is used to ensure the diffusion...
Image segmentation is a hard task and many methods have been developed to alleviate its difficulties. A common preprocessing step designed for this purpose is to compute an over-segmentation of the image, often referred to as superpixels. In this paper, we propose a new approach to superpixels computation. In a first step, a hypergraph-based representation of the image is built. Then, a coarsening...
Segmentation in computer vision refers to the process of partitioning a digital image into multiple segments (sets of pixels). It has several features which make it suitable for techniques inspired by nature. It can be parallelized, locally solved and the input data can be easily encoded by bio-inspired representations. In this paper, we present a new software for performing a segmentation of 2D digital...
A system for fast multi-view 3D model reconstruction of object sequences is composed of a number of hardware and software components: the multiple simultaneous image acquisition subsystems, the computation platform, the object/background segmentation algorithm, and in this case, a volumetric carving procedure based on the silhouettes of the objects from each view that generates a volumetric representation...
The problem of object detection and tracking has received relatively less attention in low frame rate and low resolution videos. Here we focus on motion segmentation in videos where objects appear small (less than 30-pixel tall people) and have low frame rate (less than 5 Hz). We study challenging cases where some of the, otherwise successful, approaches may break down. We investigate a number of...
We present a method for object tracking over time sequence imagery. The image plane is represented with a 4-connected planar graph where vertices are associated with pixels. On each image, the outer contour of the object is localized by finding the optimal cycle in the graph such that a cost function based on temporal, appearance and shape priors is minimized. Our contribution is the particle filtering-based...
The synergistic combination of single standing methods for the efficient and effective solution of complex problems represent the next level of research the last two decades in the area of computer vision and image understanding, due to their complicated challenges. Many of these combinations are based on the human researcher's experimental studies and most recently on a semi-automated or automated...
Automatic facial action unit (AU) detection from video is a long-standing problem in computer vision. Two main approaches have been pursued: (1) static modeling - typically posed as a discriminative classification problem in which each video frame is evaluated independently; (2) temporal modeling - frames are segmented into sequences and typically modeled with a variant of dynamic Bayesian networks...
Motion vector estimation is an important parameter for video segmentation. Effective video compression can be achieved by choosing a correct approach for the calculation of motion vector. Here in this paper we propose an optical flow motion vector estimation through iterative Lucas-Kanade pyramidal implementation for both large & small motion in image pyramid representation a group of pixel information...
In this paper we present a Bayesian framework for segmenting images into their constituent visual patterns. The segmentation algorithm optimizes the posterior probability and outputs a scene representation as a hierarchical graph representation, in a spirit similar to stochastic grammars in natural language. This computational framework integrates two popular inference approaches-generative (top-down)...
New imaging devices provide image data at very high spatial resolution acquisition and throughput rate. In satellite or medical two-dimensional images, high-content and large image issues plead for more high semantic level interactions between the computer vision systems and the end-users in order to leverage the cognitive symbiosis between both systems for practical tasks such as clinical disease...
Image ground segmentation is an important task in the area of computer vision for the robot navigation because the ground region is often taken as the traversable terrain. In this paper, we propose a learning-based method for image ground segmentation which applies the Adaboost learning method to combine multiple cues for detection of the ground region in an image. Firstly, an image is segmented into...
Detection of linear structure is a very important problem in image processing and computer vision. The task of finding lines in 2D images has long being studied, but the work in 3D space does not have any promising work yet. This paper investigates the issue of line detection for range images. It proposes an approach to find a wire-frame composed of lines that can represent precisely and comprehensively...
This paper, we will review the main approaches of partitioning an image into regions by using gray values in order to reach a correct interpretation of the image. We mainly compare the region-based segmentation with the boundary estimation using edge detection. Image segmentation is an important step for many image processing and computer vision algorithms while an edge can be described informally...
We consider one of the most basic questions in computer vision, that of finding a low-level image representation that could be used to seed diverse, subsequent computations of image understanding. Can we define a relatively general purpose image representation which would serve as the syntax for diverse needs of image understanding? What makes good image syntax? How do we evaluate it? We pose a series...
A new region-based depth ordering algorithm is proposed based on the segmented motion layers with affine motion models. Starting from an initial set of layers that are independently extracted for each frame of an input sequence, relative depth order of every layer is determined following a bottom-to-top approach from local pair-wise relations to a global ordering. Layer sets of consecutive time instants...
Image representation is an important issue in computer graphics, computer vision, robotics, image processing and pattern recognition. In this paper, we proposed an improved color image representation method by using the direct non-symmetry and anti-packing model with triangles and rectangles (DNAMTR). Also, we propose an algorithm of the DNAMTR for color images and analyze the total data amount of...
Shadow image edge detection by using an adaptive background model is a critical component for many vision-based applications. Most background models were maintained in pixel-based forms, while some approaches began to study block-based representations which are more robust to non-stationary backgrounds. In this paper, a novel method that combines edge growing and granular computing approaches into...
Markerless human motion capture has received much attention in computer vision and computer graphics communities. A hierarchical skeleton template is frequently used to model the human body in literature, because it simplifies markerless human motion capture as a problem of estimating the human body shape and joint angle parameters. The proposed work establishes a skeleton based markerless human motion...
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