Tuomas Eerola

Segmentation of Overlapping Elliptical Objects in Silhouette Images

Segmentation of partially overlapping objects with a known shape is needed in an increasing amount of various machine vision applications. This paper presents a method for segmentation of clustered partially overlapping objects with a shape that can be approximated using an ellipse. The method utilizes silhouette images, which means that it requires only that the foreground (objects) and background can be distinguished from each other. The method starts with seedpoint extraction using bounded erosion and fast radial symmetry transform. Extracted seedpoints are then utilized to associate edge points to objects in order to create contour evidence. Finally, contours of the objects are estimated by fitting ellipses to the contour evidence. The experiments on one synthetic and two different real data sets showed that the proposed method outperforms two current state-of-art approaches in overlapping objects segmentation.

Bayesian network model of overall print quality: Construction and structural optimisation

Prediction of overall visual quality based on instrumental measurements is a challenging task. Despite the several proposed models and methods, there exists a gap between the instrumental measurements of print and human visual assessment of natural images. In this work, a computational model for representing and quantifying the overall visual quality of prints is proposed. The computed overall quality should correspond to the human visual quality perception when viewing the printed images. The proposed model is a Bayesian network which connects the objective instrumental measurements to the subjective opinion distribution of human observers. This relationship can be used to score printed images, and additionally, to computationally study the connections of the attributes. A novel graphical learning approach using an iterative evolve-estimate-simulate loop learning the quality model based on psychometric data and instrumental measurements is suggested. The network structure is optimised by applying evolutionary computation (evolve). The estimation of the Bayesian network parameters is within the evolutionary loop. In this loop, the maximum likelihood approach is used (estimate). The stochastic learning process is guided by priors devised from the psychometric subjective experiments (performance through simulation). The model reveals and represents the explanatory factors between its elements providing insight to the psychophysical phenomenon of how observers perceive visual quality and which measurable entities affect the quality perception. By using true data, the design choices are demonstrated. It is also shown that the best-performing network establishes a clear and intuitively correct structure between the objective measurements and psychometric data.

Is there hope for predicting human visual quality experience

One of the most important research goals in media science is a computational model for the human perception of visual quality, that is, how to predict the subjective visual quality experience. This research area has converged to developing new and investigating existing lower-level measurable quantities, physical, visual or computational, which could explain the high level experience. A principal research question, whether the prediction of the visual quality experience based on any lower-level objective measurements is possible at all, has received much less attention. This question is investigated in this study. First, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Second, we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation. Finally, the presented ranking method is used to provide quantitative evidence that visual quality experience can be predicted using lower-level measurable quantities. This result is novel and by simultaneously revealing the underlying lower-level factors it should re-direct the future research towards the true model.

Segmentation of Partially Overlapping Nanoparticles Using Concave Points

This paper presents a novel method for the segmentation of partially overlapping nanoparticles with a convex shape in silhouette images. The proposed method involves two main steps: contour evidence extraction and contour estimation. Contour evidence extraction starts with contour segmentation where contour segments are recovered from a binarized image by detecting concave points. After this, contour segments which belong to the same object are grouped by utilizing properties of fitted ellipses. Finally, the contour estimation is implemented through a non-linear ellipse fitting problem in which partially observed objects are modeled in the form of ellipse-shape objects. The experiments on a dataset consisting of nanoparticles demonstrate that the proposed method outperforms two current state-of-art approaches in overlapping nanoparticles segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have an approximately elliptical shape, such as cell segmentation.

Finding best measurable quantities for predicting human visual quality experience

The literature of visual quality is mainly concentrated on devising new physical, visual, or computational quality features which could indirectly reflect ldquotrue visual qualityrdquo. The problem is that the true visual quality is always a subjective and context sensitive judgement of a single individual or a group of individuals. Therefore, the developed methods are only loosely connected to this ultimate objective, and the existing de facto and official standards have been designed by forming a consensus among experts of a specific field (e.g., in the printing industry). In this study, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Then we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation trial. Finally by using the above framework, we devise the best combinations from a set of well-known measurable quantities. The devised combinations can be considered as optimal when agreement with the human visual quality experience is desired, and therefore, they also reveal completely novel information about measuring visual quality.

Framework for modeling visual printed image quality from the paper perspective

Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for specifying the quality potential of printers and materials in more detail than before. Both production and end-use standpoints are relevant. This paper gives an overview of an on-going study which has the goal of determining a framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is founded on the concept of a layered network model. The model and its subjective, objective and instrumental measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image content has a marked effect on the dimensions. This underlines the challenges in designing the test images.

Segmentation of Saimaa Ringed Seals for Identification Purposes

Wildlife photo-identification is a commonly used technique to identify and track individuals of wild animal populations over time. It has various applications in behavior and population demography studies. Nowadays, mostly due to large and labor-intensive image data sets, automated photo-identification is an emerging research topic. In this paper, the first steps towards automatic individual identification of the critically endangered Saimaa ringed seal (Phoca hispida saimensis) are taken. Ringed seals have a distinctive permanent pelage pattern that is unique to each individual making the image-based identification possible. We propose a superpixel classification based method for the segmentation of ringed seal in images to eliminate the background and to simplify the identification. The proposed segmentation method is shown to achieve a high segmentation accuracy with challenging image data. Furthermore, we show that using the obtained segmented images promising identification results can be obtained even with a simple texture feature based approach. The proposed method uses general texture classification techniques and can be applied also to other animal species with a unique fur or skin pattern.

Framework for developing image-based dirt particle classifiers for dry pulp sheets

One important aspect of assessing the quality in pulp and papermaking is dirt particle counting and classification. Knowing the number and types of dirt particles present in pulp is useful for detecting problems in the production process as early as possible and for fixing them. Since manual quality control is a time-consuming and laborious task, the problem calls for an automated solution using machine vision techniques. However, the ground truth required to train an automated system is difficult to ascertain, since all of the dirt particles should be manually segmented and classified based on image information. This paper proposes a framework for developing and tuning dirt particle detection and classification systems. To avoid manual annotation, dry pulp sheets with a single dirt type in each were exploited to generate semisynthetic images with the ground truth information. To classify the dirt particles, a set of features were computed for each image segment. Sequential feature selection was employed to determine a close-to-optimal set of features to be used in classification. The framework was tested both with semisynthetically generated images based on real pulp sheets and with independent original real pulp sheets without any generation. The results of the experiments show that the semisynthetic procedure does not significantly change the properties of images and has little effect on the particle segmentation. The feature selection proved to be important when the number of dirt classes changes since it allows to improve the classification results. Using the standard classification methods, it is possible to obtain satisfactory results, although the methods modeling the data, such as the Bayesian classifier using the Gaussian Mixture Model, show better performance.

Detection of Curvilinear Structures by Tensor Voting Applied to Fiber Characterization

The paper presents a framework for the detection of curvilinear objects in images. Such objects are challenging to be described by a geometrical model, and although they appear in a number of applications, the problem of detecting curvilinear objects has drawn limited attention. The proposed approach starts with an edge detection algorithm after which the task of object detection becomes a problem of edge linking. A state-of-the-art local linking approach called tensor voting is used to estimate the edge point saliency describing the likelihood of a point belonging to a curve, and to extract the end points and junction points of these curves. After the tensor voting, the curves are grown from high-saliency seed points utilizing a linking method proposed in this paper. In the experimental part of the work, the method was systematically tested on pulp suspension images to characterize fibers based on their length and curl index. The fiber length was estimated with the accuracy of 71.5% and the fiber curvature with the accuracy of 70.7%.

Comparison of General Object Trackers for Hand Tracking in High-Speed Videos

The problem of tracking a hand in video has gained a lot of attention due to its numerous applications in human computer interfaces. So far, the work has been limited to the use of standard speed videos, but the recent developments in imaging technology and computing hardware have made it attractive to exploit high-speed imaging for tracking the hand more accurately both in space and time. To produce videos of good quality, the high-speed imaging requires more light when compared to imaging with conventional frame rates. Therefore, grey-scale high-speed imaging is in common use and this makes the use of hand tracking methods relying specifically on color information unsuitable. In this work, we provide the first solid comparison of state-of-the-art general object trackers on hand tracking with a primary focus on grey-scale high-speed videos. Novel annotated high-speed video data were collected and made publicly available for evaluation purposes. The algorithms were tested with both finger and hand targets, and with grey-scale and color videos. In addition to tracking accuracies, the stability, sensitivity, and the processing speeds of the algorithms were evaluated. The experiments show that the results vary significantly in all aspects, but certain methods such as Compressive Tracking and Hough Track methods performed better overall.