GrabCut Research Articles

Cucumber appearance quality detection under complex background based on image processing

Cucumber fruit appearance quality is an important basis of growth status. In order to improve the quality detection accuracy and processing efficiency of cucumber color image under complicated background, an improved GrabCut algorithm was proposed to extract the cucumber boundary. Firstly, including pixel size normalization, rectangular box set and scale image resolution, pretreatments of cucumber image were adopted to reduce the iteration times and operation time of GrabCut algorithm. Then, the Gaussian mixture model was chosen to find out the possible prospect of target region and background region in the preprocessed rectangular frame on the preliminary modeling. Meanwhile, by the optimization of K-means cluster to the initial GMM model, the effective target area was extracted. Finally, the whole image noise and serrated boundary was removed by morphological operations to segment the outline of the complete target prospects with appropriate structure size. And then the cucumber appearance quality detection instrument was designed to extract the texture and shape features exactly, so that it could obtain cucumber appearance quality and evaluate its growth effectively. With the segmentation experiments by almost 300 cucumber original images from greenhouse in Shandong Province, the results showed that the improved GrabCut algorithm could effectively extract the complete and smooth boundary of cucumber. With relatively high segmentation evaluation index, the precision was 93.88%, the recall rate was 99.35%, the F-Measure reached 96.53%, and the misclassification error was controlled at minimum 5.84%. The average running time was shortened to 1.4023 s. The comparison results showed that the improved GrabCut algorithm was the best, followed by the SLIC and traditional GrabCut method. Cucumber appearance quality detection instrument could also extract more accurate feature parameters. And it could meet the basic growth condition assessment by automatic image processing. Keywords: cucumber, complicated background, quality detection, image processing, GrabCut DOI: 10.25165/j.ijabe.20181104.3090 Citation: Ye H J, Liu C Q, Niu P Y. Cucumber appearance quality detection under complex background based on image processing. Int J Agric & Biol Eng, 2018; 11(4): 193-199.

A Hierarchical Grab Cut Image Segmentation Algorithm

This paper aims to speed up a segmentation algorithm “Grab Cut” by separating the process of segmentation into hierarchical steps. The Grab Cut algorithm segments images by means of the color clustering concept and the process requires a lot of iteration for it to get converged. Therefore, it is a time-consuming process which we are interested in improving this process. In this study, we adopt the idea of hierarchical processing. The first step is to compute at low resolution to make the iteration much faster, and the second step use the result of the first step to carry on iteration at original resolution so that the total execution time can be reduced. Specifically speaking, segmentation of a low resolution image will lead to high-speed and similar-segmentation result to the segmentation at original resolution. Hence, once the iterations at low resolution have converged, we can utilize the parameters of segmentation result to initialize the next segmentation on original resolution. This way, the number of iteration of segmentation at original resolution will be reduced through the initialization of those parameters. Since the execution time of low resolution images is relatively short, the total hierarchical execution time will be reduced consequently. Also, we made a comparison among the four methods of reduction on image resolution. Finally, we found that reducing the number of basins by “Median Filter” resulted in best segmentation speed.

Identification of Different Food Grains by Extracting Colour and Structural Features using Image Segmentation Techniques

Objectives: This paper proposes a method of identifying the different food crops which is automated. Identification of food crops can be automated using image segmentation techniques which reduces manual work. Method: Images of various food grains like wheat, paddy, cereals, barley and many more are captured using high resolution camera. Grab Cut algorithm helps in extraction only the part of image that the user is interested in. The colour features and outer boundaries of the food grains are extracted using Watershed algorithm and Canny detect detection. Findings: Extracting only the required part of an image i.e. the food grains, have made the processing easier as less features will be extracted. This reduction becomes very important when large data-set is considered. Many of the techniques used for segmenting grains consider only one feature, either colour or structural feature. The proposed system uses both colour and structural features which makes segmenting more accurate. Canny edge detection and Watershed algorithm takes less time for computation when compared with other techniques. Applications: An electronic hardware component can be built and the software part which identifies the grains can be embedded with it and used in the agricultural fields for separating food grains.

A new multi-modal approach to bib number/text detection and recognition in Marathon images

Bib number/text detection and recognition in Marathon natural images is challenging because of unconstrained poses created by background and bib number font variations. This paper presents a new multi-modal approach to integrate torso and text detection approaches in a novel way to achieve good results in contrast to the conventional methods that rely on the characteristics of texts but not numerals of arbitrary orientations. In the first stage, we explore HOG features along with an SVM classifier for upper body detection from the input image. Then the well-known Grab Cut method is adapted for foreground segmentation from the upper body image. Next, the pictorial structural model has been used for torso detection with the help of conditional random field (CRF). In the second stage, we propose to use a text detection method for locating texts from torso results, which are used for recognition further. We conduct experiments on a standard database as well as our database for validating torso detection in real life scenarios. To show the effectiveness of the proposed multi-modal approach, we also conduct extensive experiments on text detection and recognition before and after torso detection.

Fully automatic person segmentation in unconstrained video using spatio-temporal conditional random fields

The segmentation of objects and people in particular is an important problem in computer vision. In this paper, we focus on automatically segmenting a person from challenging video sequences in which we place no constraint on camera viewpoint, camera motion or the movements of a person in the scene. Our approach uses the most confident predictions from a pose detector as a form of anchor or keyframe stick figure prediction which helps guide the segmentation of other more challenging frames in the video. Since even state of the art pose detectors are unreliable on many frames –especially given that we are interested in segmentations with no camera or motion constraints –only the poses or stick figure predictions for frames with the highest confidence in a localized temporal region anchor further processing. The stick figure predictions within confident keyframes are used to extract color, position and optical flow features. Multiple conditional random fields (CRFs) are used to process blocks of video in batches, using a two dimensional CRF for detailed keyframe segmentation as well as 3D CRFs for propagating segmentations to the entire sequence of frames belonging to batches. Location information derived from the pose is also used to refine the results. Importantly, no hand labeled training data is required by our method. We discuss the use of a continuity method that reuses learnt parameters between batches of frames and show how pose predictions can also be improved by our model. We provide an extensive evaluation of our approach, comparing it with a variety of alternative grab cut based methods and a prior state of the art method. We also release our evaluation data to the community to facilitate further experiments. We find that our approach yields state of the art qualitative and quantitative performance compared to prior work and more heuristic alternative approaches.

An Improved Camshift Algorithm Based on Grabcutwitha Lbp Model of Correction Tracking Centroid

According to the analysis of advantages and disadvantages of traditional Camshift algorithm, it can be known that background noise and interference of similar color objects will have a greater impact on Camshift algorithm, which may cause tracking errors easily. This paper presents an improved Camshift algorithm based on Grabcut and LBP centroid tracking correction model. Current frame image enhanced by enhanced coefficient, the Camshift algorithm will apply Grabcut object segmentation to achieve pure histogram for target object and then move the centroid which has been distracted due to similar color objects’ obstacles to uncovered parts of the target object by using LBP centroid tracking correction model. As a result, the problems caused by background noise and similar color obstacles distraction can be solved effectively. Tests presented in the paper can prove that the algorithm can be used to operate the real-time tracking much more steadily and accurately.

Iterative Human Pose Estimation based on A New Part Appearance Model

Human pose estimation has become a hot topic in the field of computer vision , it can be used in human activity analysis, video, and any other field ,it main purpose is that detect the position, scale and direction of parts of people .Because of the result in the iterative human pose estimation based on tree-structure model is susceptible to the background, In this paper(consider static images),part appearance model is improved, an appearance model based on colour and texture for iterative human pose estimation is proposed, experimental results show this method give a better performance and accuracy while reduce the search space by people detector and grab cut.

INTERACTIVE LIVER TUMOR SEGMENTATION USING GRAPHCUT AND GRABCUT

This literature review attempts to provide a brief overview of the most common segmentation techniques , a comparison between them. It discusses the “Grab-Cut” technique, and G raph techniques. GrabCut is a way to perform 2in an image that is very user friendly. The user only need to i nput a very rough segmentation between foreground a.The Graph Cut approaches to segmentation can be extended to 3-D dand can be used for segmenting 3-D volumes. Oth er segmentation techniques use either contour or edge segmentation to perform segmentation. The Graph Cut techniques ucontour edge detection. Typically this is down by drawing a rect angle around the object of interest. The way that tis accomplished technically is by using a combination of Graph Cuts statistica l models of the foreground background structure in the colour space. Grab Cut Technique use very minimum energy to separate F oreground Background Images.

An Interactive Image Segmentation Algorithm Based on Graph Cut

This paper introduces a new interactive image segmentation algorithm. This algorithm gets better result with fewer users interactive when segmenting single-object from images with complex foreground and background. Experiments show that this algorithm effectively solve the common over-segmentation and less-segmentation in graph cut, as well as resolve the problem of small regions error segmented in grab cut algorithm.

Breast Mass Segmentation Using Iterated Graph Cuts Based on Multi-Scale Smoothing

A novel scheme for mass segmentation in mammography is proposed,which is based on Graph Cuts algorithm and multi-scale analysis.Mammogram is segmented by statistical region merging firstly,and the obtained rough contour is used as the initial contour for Graph Cuts segmentation.In iterative optimization stage of the algorithm,multi-scale analysis method is introduced to estimate the Gaussian Mixture Model(GMM)parameters with pyramidal decomposing serial images instead of fix-scale original image.The algorithm estimates GMM parameters rapidly with fewer samples by utilizing the complementarities between segmentation accuracy of fine scale and segmentation easiness of coarse scale.In order to improve efficiency of the proposed approach,watershed algorithm is utilized to produce a region adjacency graph,replacing pixel adjacency graph with fewer samples.The proposed method,interactive Graph Cuts and GrabCut were simultaneously applied for mass segmentation on 110 mammographic ROIs,and the achieved average ratios of misclassification error are 1.57,3.46 and 5.01 respectively.The results demonstrate that the proposed method achieves a better performance in accuracy and robustness.