Incremental Superpixels for Real-Time Video Analysis (original) (raw)

Image Segmentation Methods Based on Superpixel Techniques: A Survey

2020

There is a growing demand for image processing in a wide range of applications such as photography, robotics, television, remote sensing, industrial inspection, and medical diagnosis. This study overviews some of the existing image segmentation methods that focus on producing superpixels. A superpixel or segment is a homogeneous, local coherent structure that specifies information oversampling or scales resolutions. There are many image segmentation or superpixelization methods which divide color image with different techniques according to their characteristics and parameters as image acquisition might be seriously affected by many factors such as light and shadow. Several image segmentation algorithms were investigated in image processing research for creating superpixels that may lack the ability to control the size, number, and compactness of segments. Superpixel generation algorithms can be categorized into graph-based methods and gradient-ascent

COMPARATIVE ANALYSIS OF SUPERPIXEL SEGMENTATION METHODS

Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. Superpixel's purpose is to reduce the redundancy in the image and increase efficiency from the point of view of the next processing task. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. Many methods for the computation of superpixels were already presented. A drawback of most of these methods is their high computational complexity and hence high computational time consumption. K mean based SLIC method shows better performance as compare to other while evaluating on the bases of under segmentation error and boundary recall, etc parameters.

A Survey on Different Methods for Superpixel Segmentation

2019

Image segmentation is an important part of image analysis process, since it differentiates between the salient objects and the other objects or from their background. It is the process of dividing digital image into multiple segments and the main aim of segmentation is to pinpoint objects and boundaries. There are different methods for segmenting image, here we are considering the concept of superpixels inorder to segment image. Superpixel can mainly accelerate the successive processing since the superpixels of an image carry more information than a normal pixel. This paper deals with detailed survey on different superpixel segmentation techniques. IndexTerms: Salient object, Superpixel, Discriminability. ________________________________________________________________________________________________________

Applications and Datasets for Superpixel Techniques A Survey

2020

The use of superpixels instead of pixels can significantly improve the speed of the current pixel-based algorithms, and can even produce better results in many applications such as robotics, remote sensing, industrial inspection, and medical diagnosis. Two main tasks related to vision could benefit from superpixels, named object class segmentation and medical image segmentation. In both cases, superpixels can increase performance significantly and also reduce the computational cost. In addition to superpixel applications, various datasets were employed for the evaluation of the superpixel algorithms. This work aims to survey the recent applications and the most common datasets that can be used based on superpixel techniques.

Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach

IEEE Access

Superpixel algorithms group visually coherent pixels and form an alternative representation of the regular structure of the pixel grid. This fundamental low-level computer vision preprocessing step greatly reduces the complexity of subsequent image processing tasks. However, most of the existing methods suffer from very high calculation costs which makes them quite unsuitable for time-sensitive applications. In this paper, we propose a new superpixel segmentation method, named IBIS for Iterative Boundaries implicit Identification for superpixels segmentation, that implicitly identifies the boundaries between superpixels and performs the segmentation using only a fraction of the pixels of the input image, thereby reducing the complexity and computation time. The results obtained during the experiments show that the segmentation quality of IBIS is comparable to that of state of the art methods with a computation time divided by a factor of 8 without parallelization of the processing for low resolution images (e.g., 320 × 240 pixels) as usually provided in public data sets. We also present and comprehensively evaluate the GPU variant of IBIS named IBIScuda that allows an optimal exploitation of the available resources considering the limited bandwidth between CPU and GPU memories.

Superpixel Hierarchy

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2018

Superpixel segmentation has been one of the most important tasks in computer vision. In practice, an object can be represented by a number of segments at finer levels with consistent details or included in a surrounding region at coarser levels. Thus, a superpixel segmentation hierarchy is of great importance for applications that require different levels of image details. However, there is no method that can generate all scales of superpixels accurately in real time. In this paper, we propose the superhierarchy algorithm which is able to generate multi-scale superpixels as accurately as the state-of-the-art methods but with one to two orders of magnitude speed-up. The proposed algorithm can be directly integrated with recent efficient edge detectors to significantly outperform the state-of-the-art methods in terms of segmentation accuracy. Quantitative and qualitative evaluations on a number of applications demonstrate that the proposed algorithm is accurate and efficient in genera...

Graph-based superpixel labeling for enhancement of online video segmentation

2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), 2013

In this paper, we propose a novel approach for video segmentation. The proposed work is based on exploiting a superpixel-based image segmentation approach to improve the performance of state-of-the-art foreground/background segmentation techniques. A fusion between a bilayer segmentation and a geodesic segmentation approaches with a graph-based superpixel segmentation method is performed. Four different combination alternatives are investigated in terms of performance and efficiency. Manually-labeled ground truth video sequences as well as our own recorded video sequences were used for evaluation purposes. The evaluation results confirm the potential of the proposed method in enhancing the accuracy of the video segmentation over the state-of-the-art.

Real-time Superpixel Segmentation by DBSCAN Clustering Algorithm

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2016

In this paper, we propose a real-time image superpixel segmentation method with 50fps by using the Density- Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with colorsimilarity and geometric restrictions is used to rapidly cluster the pixels, and then small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real-time superpixel algorithm (50fps) by the DBSCAN clustering outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency.

Background Subtraction Based on Random Superpixels Under Multiple Scales for Video Analytics

IEEE Access, 2018

Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region. Most previously available region-based background subtraction methods ignore the similarity between the pixels, meaning that the information gained from the pixels that do not contribute, or even contribute negatively to understanding an image, is taken into account. A new background subtraction model based on random superpixel segmentation under multiple scales is proposed. A custom region segmentation area is replaced with a superpixel segmentation area that uses similarity characteristics for pixels in the superpixel area. The compactness of the pixels in the same superpixel area means that the pixels positively contribute to understanding an image compared with when using custom region pixels. Superpixel segmentation is performed using the random simple linear iterative cluster method. Taking random samples during the superpixel segmentation process produces the Matthew effect, thus improving the robustness and efficiency of the model. Multi-scale superpixel segmentation is therefore guaranteed to give more accurate results. Standard benchmark experiments using the proposed approach produced encouraging results compared with the results given by previously available algorithms. INDEX TERMS Computer vision, motion detection, background subtraction, video segmentation, pattern recognition.

Adaptive strategy for superpixel-based region-growing image segmentation

Journal of Electronic Imaging

This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we firstly introduce a robust adaptive multi-scale superpixel similarity in which region comparisons are made both at content and common border level. Secondly, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adpative merging criterion to ensure that best region aggregations are given highest priorities. This allows to reach a final segmentation into consistent regions with strong boundary adherence. We perform experiments on the BSDS500 image dataset to highlight to which extent our method compares favorably against other well-known image segmentation algorithms. The obtained results demonstrate the promising potential of the proposed approach.