Objective Segmentation Quality Research Articles

基于机器视觉的羊只目标提取算法设计与实现

In order to improve the quality of sheep foreground object segmentation, images are segmented using the watershed algorithm in combination with a growing region algorithm, and the pixel-by-pixel comparison of segmentation is optimized to reduce the processing time. Compared with other algorithms, the optimized watershed algorithm can achieve more complete target extraction, and its processing time is improved by over 50% compared with the other six algorithms. Moreover, the optimized watershed algorithm has the optimal overall image quality indicators. This algorithm can provide a reference for the real-time extraction of the activity state of sheep.

Objective Object Segmentation Visual Quality Evaluation: Quality Measure and Pooling Method

Objective object segmentation visual quality evaluation is an emergent member of the visual quality assessment family. It aims to develop an objective measure instead of a subjective survey to evaluate the object segmentation quality in agreement with human visual perception. It is an important benchmark for assessing and comparing the performances of object segmentation methods in terms of visual quality. Despite its essential role, sufficient study compared with other visual quality evaluation studies is still lacking. In this article, we propose a novel full-reference objective measure that includes a two-level single object segmentation visual quality measure and a pooling method for multiple object segmentation overall visual quality. The single object segmentation visual quality measure combines a pixel-level sub-measure and a region-level sub-measure for evaluating the similarity of area, shape, and object completeness between the segmentation result and the ground truth in terms of human visual perception. For the proposed multiple object segmentation overall visual quality pooling method, the rank of each object’s segmentation quality as a novel factor is integrated into the weighted harmonic mean to evaluate the overall quality. To evaluate the performance of our proposed measure, we tested it on an object segmentation subjective visual quality assessment database. The experimental results demonstrate that our proposed two-level measure and pooling method with good robustness perform better in matching subjective assessments compared with other state-of-the-art objective measures.

Optimising and comparing source-extraction tools using objective segmentation quality criteria

Context. With the growth of the scale, depth, and resolution of astronomical imaging surveys, there is increased need for highly accurate automated detection and extraction of astronomical sources from images. This also means there is a need for objective quality criteria, and automated methods to optimise parameter settings for these software tools. Aims. We present a comparison of several tools developed to perform this task: namely SExtractor, ProFound, NoiseChisel, and MTObjects. In particular, we focus on evaluating performance in situations that present challenges for detection. For example, faint and diffuse galaxies; extended structures, such as streams; and objects close to bright sources. Furthermore, we develop an automated method to optimise the parameters for the above tools. Methods. We present four different objective segmentation quality measures, based on precision, recall, and a new measure for the correctly identified area of sources. Bayesian optimisation is used to find optimal parameter settings for each of the four tools when applied to simulated data, for which a ground truth is known. After training, the tools are tested on similar simulated data in order to provide a performance baseline. We then qualitatively assess tool performance on real astronomical images from two different surveys. Results. We determine that when area is disregarded, all four tools are capable of broadly similar levels of detection completeness, while only NoiseChisel and MTObjects are capable of locating the faint outskirts of objects. MTObjects achieves the highest scores on all tests for all four quality measures, whilst SExtractor obtains the highest speeds. No tool has sufficient speed and accuracy to be well suited to large-scale automated segmentation in its current form.

Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.

Interactive machine learning for fast and robust cell profiling.

Automated profiling of cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases and dependent on user experience. Here, we use interactive machine learning to identify the optimum cell profiling configuration that maximises quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is obtained. We use Bayesian optimisation, an established machine learning algorithm, to learn from this information and automatically recommend the next configuration to examine with the aim of maximising the quality of the processing or analysis. Compared to existing interactive machine learning tools that require domain expertise for per-class or per-pixel annotations, we rely on users’ explicit assessment of output quality of the cell profiling task at hand. We validated our interactive approach against the standard human trial-and-error scheme to optimise an object segmentation task using the standard software CellProfiler. Our toolkit enabled rapid optimisation of an object segmentation pipeline, increasing the quality of object segmentation over a pipeline optimised through trial-and-error. Users also attested to the ease of use and reduced cognitive load enabled by our machine learning strategy over the standard approach. We envision that our interactive machine learning approach can enhance the quality and efficiency of pipeline optimisation to democratise image-based cell profiling.

Effects of elemental images' quantity on three-dimensional segmentation using computational integral imaging.

3D object detection and isolation can be achieved algorithmically using computational integral-imaging data. The 3D scene is acquired by a multi-channel system, where each channel (elemental image) captures the scene from a shifted perspective angle. The number of these channels affects the weight, the cost, and the computational load of the segmentation process, while a lower number of channels may reduce the performance of the objects' separation in the 3D scene. This research examines the effect of the elemental images' quantity on the 3D object detection and segmentation, under both regular and noisy conditions. Moreover, based on our previous works, we perform an improvement of the 3D object segmentation quality using an adapted active-contour method.

Visual Quality Evaluation of Image Object Segmentation: Subjective Assessment and Objective Measure.

A visual quality evaluation of image object segmentation as one member of the visual quality evaluation family has been studied over the years. Researchers aim at developing the objective measures that can evaluate the visual quality of object segmentation results in agreement with human quality judgments. It is also significant to construct a platform for evaluating the performance of the objective measures in order to analyze their pros and cons. In this paper, first, we present a novel subjective object segmentation visual quality database, in which a total of 255 segmentation results were evaluated by more than thirty human subjects. Then, we propose a novel full-reference objective measure for an object segmentation visual quality evaluation, which involves four human visual properties. Finally, our measure is compared with some state-of-the-art objective measures on our database. The experiment demonstrates that the proposed measure performs better in matching subjective judgments. Moreover, the database is available publicly for other researchers in the field to evaluate their measures.

Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics

We present a new segmentation method called weighted Felzenszwalb and Huttenlocher (WFH), an improved version of the well-known graph-based segmentation method, Felzenszwalb and Huttenlocher (FH). Our algorithm uses a nonlinear discrimination function based on polynomial Mahalanobis Distance (PMD) as the color similarity metric. Two empirical validation experiments were performed using as a golden standard ground truths (GTs) from a publicly available source, the Berkeley dataset, and an objective segmentation quality measure, the Rand dissimilarity index. In the first experiment the results were compared against the original FH method. In the second, WFH was compared against several well-known segmentation methods. In both cases, WFH presented significant better similarity results when compared with the golden standard and segmentation results presented a reduction of over-segmented regions.

On Evaluating Video Object Segmentation Quality: A Perceptually Driven Objective Metric

The task of extracting objects in video sequences emerges in many applications such as object-based video coding (e.g., MPEG-4) and content-based video indexing and retrieval (e.g., MPEG-7). The MPEG-4 standard provides specifications for the coding of video objects, but does not address the problem of how to extract foreground objects in image sequences. Therefore, for specific applications, evaluating the quality of foreground/background segmentation results is necessary to allow for an appropriate selection of segmentation algorithms and for tuning their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, formal psychophysical experiments evaluating the quality of different video foreground object segmentation results have not yet been conducted. In this paper, a generic framework for both subjective and objective segmentation quality evaluation is presented. An objective quality assessment method for segmentation evaluation is derived on the basis of perceptual factors through subjective experiments. The performance of the proposed method is shown on different state-of-the-art foreground/background segmentation algorithms and our method is compared to other objective methods which do not include perceptual factors. Moreover, on the basis of subjective results, weighting strategies are introduced into the proposed metric to meet the specificity of different segmentation applications e.g., video compression, video surveillance and mixed reality. Experimental results confirm the efficiency of the proposed approach.

Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic

This paper proposes a new evolutionary region merging method in order to efficiently improve segmentation quality results. Our approach starts from an oversegmented image, which is obtained by applying a standard morphological watershed transformation on the original image. Next, each resulting region is represented by its centroid. The oversegmented image is described by a simplified undirected weighted graph, where each node represents one region and weighted edges measure the dissimilarity between pairs of regions (adjacent and non-adjacent) according to their intensities, spatial locations and original sizes. Finally, the resulting graph is iteratively partitioned in a hierarchical fashion into two subgraphs, corresponding to the two most significant components of the actual image, until a termination condition is met. This graph-partitioning task is solved by a variant of the min-cut problem (normalized cut) using a hierarchical social (HS) metaheuristic. We have efficiently applied the proposed approach to brightness segmentation on different standard test images, with good visual and objective segmentation quality results.

Toward a generic evaluation of image segmentation

Image segmentation plays a major role in a broad range of applications. Evaluating the adequacy of a segmentation algorithm for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to tune their parameters for optimal performance. However, objective segmentation quality evaluation is far from being a solved problem. In this paper, a generic framework for segmentation evaluation is introduced after a brief review of previous work. A metric based on the distance between segmentation partitions is proposed to overcome some of the limitations of existing approaches. Symmetric and asymmetric distance metric alternatives are presented to meet the specificities of a wide class of applications. Experimental results confirm the potential of the proposed measures.

Temporal stabilization of Video Object Segmentation for 3D-TV applications

Our aim is to insert depth information into an existing 2D video sequence to provide content for 3D-TV applications, which we try to achieve through segmentation of the objects in the given 2D video sequence. To this effect, we present a method for temporal stabilization of video object segmentation algorithms for 3D-TV applications. First, two quantitative measures to evaluate temporal stability without ground-truth are discussed. Then, a pseudo-3D curve evolution method, which spatio-temporally stabilizes the estimated segmentation of a video object is introduced. Temporal stability is achieved by re-distributing existing object segmentation errors such that they will be less disturbing when the scene is rendered and viewed in 3D. Our starting point is the hypothesis that if making segmentation errors is inevitable, these errors should be made in a temporally consistent way for 3D-TV applications. This hypothesis is supported by the experiments, which show that there is significant improvement in segmentation quality both in terms of the objective quantitative measures and in terms of the viewing comfort in subjective perceptual tests. Therefore, it is possible to increase the perceptual object segmentation quality without increasing the actual segmentation accuracy.

Objective evaluation of video segmentation quality

Video segmentation assumes a major role in the context of object-based coding and description applications. Evaluating the adequacy of a segmentation result for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to adjust their parameters for optimal performance. Subjective testing, the current practice for the evaluation of video segmentation quality, is an expensive and time-consuming process. Objective segmentation quality evaluation techniques can alternatively be used; however, it is recognized that, so far, much less research effort has been devoted to this subject than to the development of segmentation algorithms. This paper discusses the problem of video segmentation quality evaluation, proposing evaluation methodologies and objective segmentation quality metrics for individual objects as well as for complete segmentation partitions. Both stand alone and relative evaluation metrics are developed to cover the cases for which a reference segmentation is missing or available for comparison.

Stand-Alone Objective Segmentation Quality Evaluation

The identification of objects in video sequences, that is, video segmentation, plays a major role in emerging interactive multimedia services, such as those enabled by the ISO MPEG-4 and MPEG-7 standards. In this context, assessing the adequacy of the identified objects to the application targets, that is, evaluating the segmentation quality, assumes a crucial importance. Video segmentation technology has received considerable attention in the literature, with algorithms being proposed to address various types of applications. However, the segmentation quality performance evaluation of those algorithms is often ad hoc, and a well-established solution is not available. In fact, the field of objective segmentation quality evaluation is still maturing; recently, some more efforts have been made, mainly following the emergence of the MPEG object-based coding and description standards. This paper discusses the problem of objective segmentation quality evaluation in its most difficult scenario: stand-alone evaluation, that is when a reference segmentation is not available for comparative evaluation. In particular, objective metrics are proposed for the evaluation of stand-alone segmentation quality for both individual objects and overall segmentation partitions.